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255 lines
12 KiB
Python
255 lines
12 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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PanGu predict run
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"""
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import os
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import time
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import mindspore.common.dtype as mstype
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import mindspore.communication.management as D
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import moxing as mox
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import numpy as np
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from mindspore import context, Tensor
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from mindspore import export
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from mindspore.context import ParallelMode
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from mindspore.parallel import set_algo_parameters
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from mindspore.parallel._cost_model_context import _set_multi_subgraphs
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from mindspore.parallel.nn.transformer import TransformerOpParallelConfig
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from src.code_tokenizer import CodeTokenizer
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from src.pangu_alpha_config import set_parse, PanguAlphaConfig
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from src.pangu_alpha_fp16_predict import EvalNet, PanguAlphaModel
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from src.utils import get_args
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def load_model(args_opt):
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r"""
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The main function for load model
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"""
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# Set execution mode
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context.set_context(save_graphs=False,
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mode=context.GRAPH_MODE,
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device_target=args_opt.device_target)
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context.set_context(variable_memory_max_size="30GB")
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# Set parallel context
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if args_opt.distribute == "true":
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D.init()
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device_num = D.get_group_size()
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rank = D.get_rank()
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print("rank_id is {}, device_num is {}".format(rank, device_num))
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(
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parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL,
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gradients_mean=False,
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full_batch=True,
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loss_repeated_mean=True,
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enable_parallel_optimizer=False,
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pipeline_stages=args_opt.stage_num)
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set_algo_parameters(elementwise_op_strategy_follow=True)
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_set_multi_subgraphs()
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else:
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rank = 0
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device_num = 1
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context.reset_auto_parallel_context()
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context.set_auto_parallel_context(
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strategy_ckpt_load_file=args_opt.strategy_load_ckpt_path)
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context.set_context(
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save_graphs=False,
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save_graphs_path="/cache/graphs_of_device_id_" + str(rank),
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)
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use_past = (args_opt.use_past == "true")
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print('local_rank:{}, start to run...'.format(rank), flush=True)
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if args_opt.export:
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use_past = True
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# Set model property
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print("===args_opt: ", args_opt, flush=True)
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print("===device_num is: ", device_num, flush=True)
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args_opt.op_level_model_parallel_num = 1
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model_parallel_num = args_opt.op_level_model_parallel_num
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data_parallel_num = int(device_num / model_parallel_num)
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print("===data_parallel_num is: ", data_parallel_num, flush=True)
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parallel_config = TransformerOpParallelConfig(data_parallel=data_parallel_num,
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model_parallel=model_parallel_num,
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pipeline_stage=args_opt.stage_num,
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micro_batch_num=args_opt.micro_size,
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optimizer_shard=False,
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vocab_emb_dp=bool(args_opt.word_emb_dp),
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recompute=True)
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per_batch_size = args_opt.per_batch_size
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batch_size = per_batch_size * data_parallel_num
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# Now only support single batch_size for predict
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if args_opt.run_type == "predict":
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batch_size = 1
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config = PanguAlphaConfig(
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batch_size=batch_size,
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seq_length=args_opt.seq_length,
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vocab_size=args_opt.vocab_size,
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hidden_size=args_opt.embedding_size,
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num_layers=args_opt.num_layers,
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num_heads=args_opt.num_heads,
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post_layernorm_residual=False,
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dropout_rate=0.0,
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ffn_hidden_size=args_opt.embedding_size * 4,
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use_past=use_past,
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eod_token=args_opt.eod_id,
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eod_reset=False,
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parallel_config=parallel_config,
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load_ckpt_path=args_opt.load_ckpt_path,
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param_init_type=mstype.float32
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if args_opt.param_init_type == 'fp32'
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else mstype.float16,
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)
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print("===config is: ", config, flush=True)
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print("=====args_opt is: ", args_opt, flush=True)
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ckpt_name = args_opt.load_ckpt_name
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# Define network
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pangu_alpha = PanguAlphaModel(config)
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eval_net = EvalNet(pangu_alpha, pad_token=50256)
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eval_net.set_train(False)
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model_predict = Model(eval_net)
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# Compile network and obtain tensor layout for loading ckpt
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inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32)
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current_index = Tensor(np.array([0]), mstype.int32)
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if args_opt.distribute == "false":
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predict_layout = None
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elif config.use_past:
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batch_valid_length = Tensor(np.array([0]), mstype.int32)
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init_true = Tensor([True], mstype.bool_)
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print("Input shape:", inputs_np.shape, flush=True)
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inputs_np_1 = Tensor(np.ones(shape=(config.batch_size, 1)), mstype.int32)
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model_predict.predict_network.add_flags_recursive(is_first_iteration=True)
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print("is_first_iteration=True", flush=True)
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predict_layout = model_predict.infer_predict_layout(inputs_np, current_index, init_true, batch_valid_length)
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model_predict.predict_network.add_flags_recursive(is_first_iteration=False)
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print("is_first_iteration=False", flush=True)
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init_false = Tensor([False], mstype.bool_)
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_ = model_predict.infer_predict_layout(inputs_np_1, current_index, init_false, batch_valid_length)
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else:
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predict_layout = model_predict.infer_predict_layout(inputs_np, current_index)
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if context.get_context("save_graphs"):
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print("==============save_graph", flush=True)
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jobid = os.environ["BATCH_JOB_ID"]
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rank_id = rank
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mox.file.make_dirs("s3://wudao-1/yyf/graphs_" + jobid)
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mox.file.copy_parallel(src_url="/cache/graphs_of_device_id_" + str(rank_id),
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dst_url="s3://wudao-1/yyf/graphs_" + jobid + "/" + str(rank_id))
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print("======start load_distributed checkpoint", flush=True)
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if args_opt.load_ckpt_epoch > 0:
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time.sleep(rank * 0.5)
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os.mkdir(os.path.join(args_opt.save_checkpoint_path, f"rank_{rank}"))
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ckpt_name = f"code-13B0-{args_opt.load_ckpt_epoch}.ckpt"
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if not mox.file.exists(os.path.join(args_opt.load_ckpt_path, f"rank_{rank}", ckpt_name)):
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print(f"Checkpoint from rank {rank} doesn't exist!")
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mox.file.copy(os.path.join(args_opt.load_ckpt_path, f"rank_{rank}", ckpt_name),
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os.path.join(args_opt.save_checkpoint_path, f"rank_{rank}", ckpt_name))
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param_dict = load_checkpoint(os.path.join(args_opt.save_checkpoint_path, f"rank_{rank}", ckpt_name))
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# TODO: add them back if not for the 1st run!
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if param_dict.get("epoch_num") and param_dict.get("step_num"):
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args_opt.has_trained_epoches = int(param_dict["epoch_num"].data.asnumpy())
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args_opt.has_trained_steps = int(param_dict["step_num"].data.asnumpy())
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os.mkdir(f'/home/work/sfs/cache/{os.environ["BATCH_JOB_ID"]}/1/rank_{rank}')
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while True:
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num = len(os.listdir(f'/home/work/sfs/cache/{os.environ["BATCH_JOB_ID"]}/1'))
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if num == device_num:
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break
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if rank % 8 == 0:
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print("Loaded ckpt in step 1: ", num)
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time.sleep(1)
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net_not_load = load_param_into_net(pangu_alpha, param_dict)
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print("====== load_distributed checkpoint done, net_not_load: ", net_not_load, flush=True)
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return model_predict, config, rank
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def export_mindir(model_predict, config):
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"""Export mindir model"""
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inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32)
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current_index = Tensor(np.array([0]), mstype.int32)
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batch_valid_length = Tensor(np.array([0]), mstype.int32)
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init_true = Tensor([True], mstype.bool_)
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inputs_np_1 = Tensor(np.ones(shape=(config.batch_size, 1)), mstype.int32)
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model_predict.predict_network.add_flags_recursive(is_first_iteration=True)
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export(model_predict.predict_network, inputs_np, current_index,
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init_true, batch_valid_length, file_name='pangu_alpha_1024', file_format='MINDIR')
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model_predict.predict_network.add_flags_recursive(is_first_iteration=False)
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export(model_predict.predict_network, inputs_np_1, current_index,
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init_true, batch_valid_length, file_name='pangu_alpha_1', file_format='MINDIR')
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print("Export finished and now exit.")
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def run_predict(model_predict, config, args_opt, rank):
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"""run predict"""
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from src.generate import generate, generate_increment
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# Define tokenizer
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tokenizer = CodeTokenizer(mode='6b')
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# Tokenize input sentence to ids
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samples = [
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"# language: Python\ndef add(a, b):\n '''\n Find the sum of a and b.\n '''\n",
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"def add(a, b):\n '''\n Find the sum of a and b.\n '''\n",
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"# language: Python\ndef optimization():\n '''\n Find the maximum of P=E**2*R/(R + r)**2 if E and r are fixed but R varies. Import sympy. Use sympy. Find where the derivative is equal to zero. Substitute the value of R into P.\n '''\n",
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"from typing import List\n\n\ndef has_close_elements(numbers: List[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n >>> has_close_elements([1.0, 2.0, 3.0], 0.5)\n False\n >>> has_close_elements([1.0, 2.8, 3.0, 4.0, 5.0, 2.0], 0.3)\n True\n \"\"\"\n",
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"// language: C++\nint add(int a, int b) {\n /* Find the sum of a and b. */\n",
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"int add(int a, int b) {\n /* Find the sum of a and b. */\n",
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"bool prime(int n) {\n // Find whether n is a prime number\n",
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"// language: JavaScript\nfunction add(a, b) {\n // Find the sum of a and b.\n",
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"# language: R\nadd<-function(a, b) {\n # Find the sum of a and b.\n",
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]
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verbose = False
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for i, sample in enumerate(samples):
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for _ in range(1):
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tokenized_token = tokenizer.encode_code(sample)
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input_ids = np.array(tokenized_token).reshape(1, -1)
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# Call inference
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generate_func = generate_increment if config.use_past else generate
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t0 = time.perf_counter()
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output_ids = generate_func(model_predict, input_ids, args_opt, verbose)
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# Decode output ids to sentence
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t1 = time.perf_counter()
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output_samples = tokenizer.decode_code(output_ids.tolist())
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output_samples_str = "".join(output_samples)
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if rank % 8 == 0:
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print(f"=================== prompt {i} ====================")
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print(sample, flush=True)
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print(f"=================== generation {i} ====================")
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print(output_samples_str, flush=True)
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print(
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f"=== Total time (s): {t1 - t0}, {output_ids.shape[-1] - input_ids.shape[-1]} tokens, {(output_ids.shape[-1] - input_ids.shape[-1]) / (t1 - t0)} token/s")
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break
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def main():
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"""Main process for predict or export model"""
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opt = get_args(True)
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set_parse(opt)
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model_predict, config, rank = load_model(opt)
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if opt.export:
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export_mindir(model_predict, config)
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else:
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run_predict(model_predict, config, opt, rank)
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if __name__ == "__main__":
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main()
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