# Copyright 2024 Bytedance Ltd. and/or its affiliates # # 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. import os import torch from omegaconf import OmegaConf from torch.distributed.fsdp import CPUOffload, MixedPrecision from torch.distributed.fsdp import FullyShardedDataParallel as FSDP from torch.distributed.fsdp.api import ShardedStateDictConfig, ShardingStrategy, StateDictType from transformers import AutoModelForCausalLM, AutoTokenizer from verl import DataProto from verl.utils.distributed import initialize_global_process_group from verl.utils.fs import copy_to_local from verl.utils.model import compute_position_id_with_mask from verl.workers.rollout.hf_rollout import HFRollout BASE_HF_ROLLOUT_CONFIG = { "temperature": 1.0, "top_k": -1, "top_p": 1, "prompt_length": 64, "response_length": 64, "do_sample": True, "n": 1, "val_kwargs": { "top_k": -1, "top_p": 1.0, "temperature": 0, "n": 1, "do_sample": False, }, } def prepare_input_dataproto(tokenizer, config, validate): preencode_prompts = [ [{"role": "user", "content": "Who won the Champions League in 2019?"}], [{"role": "user", "content": "The founder of Apple is"}], [{"role": "user", "content": "What's your name"}], ] formatted_prompts = [ tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True) for conversation in preencode_prompts ] prompts = tokenizer(formatted_prompts, return_tensors="pt", padding="max_length", max_length=config.prompt_length) input_dataproto = DataProto.from_dict( { "input_ids": prompts["input_ids"], "attention_mask": prompts["attention_mask"], "position_ids": compute_position_id_with_mask(prompts["attention_mask"]), }, meta_info={ "bos_token_id": tokenizer.bos_token_id, "eos_token_id": tokenizer.eos_token_id, "pad_token_id": tokenizer.pad_token_id, "validate": validate, }, ) return input_dataproto def prepare_fsdp_model(model, world_size): from torch.distributed.device_mesh import init_device_mesh device_mesh = init_device_mesh("cuda", mesh_shape=(world_size,), mesh_dim_names=["fsdp"]) mixed_precision = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.float32, buffer_dtype=torch.float32) fsdp_model = FSDP( model, use_orig_params=True, auto_wrap_policy=None, device_id=torch.cuda.current_device(), sharding_strategy=ShardingStrategy.FULL_SHARD, mixed_precision=mixed_precision, cpu_offload=CPUOffload(offload_params=False), sync_module_states=False, device_mesh=device_mesh, ) FSDP.set_state_dict_type( fsdp_model, state_dict_type=StateDictType.SHARDED_STATE_DICT, state_dict_config=ShardedStateDictConfig() ) return fsdp_model def test_hf_rollout(n: int = 1, do_sample: bool = True, validate: bool = False): config = OmegaConf.create(BASE_HF_ROLLOUT_CONFIG) config.update({"n": n, "do_sample": do_sample}) assert torch.cuda.device_count() >= 2, "At least 2 GPUs is required to run tp+dp tests." local_rank, rank, world_size = initialize_global_process_group() # Initialize model and tokenizer local_cache_path = "~/.cache/verl/rlhf" local_cache_path = os.path.expanduser(local_cache_path) hdfs_path = "Qwen/Qwen2-7B-Instruct" local_model_path = copy_to_local(src=hdfs_path, cache_dir=local_cache_path) tokenizer = AutoTokenizer.from_pretrained(local_model_path, padding_side="left", trust_remote_code=True) tokenizer.pad_token = tokenizer.eos_token # Initialize FSDP model actor_model = AutoModelForCausalLM.from_pretrained(local_model_path, trust_remote_code=True) actor_model.to(torch.bfloat16) fsdp_model = prepare_fsdp_model(actor_model, world_size) # Initialize HFRollout and start generate hf_rollout = HFRollout(fsdp_model, OmegaConf.create(config)) input = prepare_input_dataproto(tokenizer, config, validate).to(torch.cuda.current_device()) outputs = hf_rollout.generate_sequences(input) # check generated batch size is expected generated_batch_size = outputs.batch.batch_size[0] assert generated_batch_size == input.batch.batch_size[0] * config.n for i in range(generated_batch_size): prompt_tokens = outputs.batch["prompts"][i] prompt_mask = prompt_tokens != tokenizer.pad_token_id prompt_tokens = prompt_tokens[prompt_mask] decoded_prompt = tokenizer.decode(prompt_tokens, skip_special_tokens=False) response_tokens = outputs.batch["responses"][i] response_mask = response_tokens != tokenizer.pad_token_id response_tokens = response_tokens[response_mask] decoded_response = tokenizer.decode(response_tokens, skip_special_tokens=False) attention_mask = outputs.batch["attention_mask"][i] position_ids = outputs.batch["position_ids"][i] prompt_length = outputs.batch["prompts"].size(1) response_length = outputs.batch["responses"].size(1) assert attention_mask.size(0) == prompt_length + response_length assert position_ids.size(0) == prompt_length + response_length # check response attention mask is expected response_attention = attention_mask[prompt_length:] eos_positions = (outputs.batch["responses"][i] == tokenizer.pad_token_id).nonzero(as_tuple=True)[0] if len(eos_positions) > 0: first_eos_pos = eos_positions[0].item() assert response_attention[: first_eos_pos + 1].all(), "Response attention mask should be 1 until EOS" if first_eos_pos + 1 < response_length: assert not response_attention[first_eos_pos + 1 :].any(), ( "Response attention mask should be 0 after EOS" ) else: assert response_attention.all(), "Response attention mask should be all 1 if no EOS token" # check response position ids is expected prompt_positions = position_ids[:prompt_length] response_positions = position_ids[prompt_length:] valid_response_length = min(len(response_tokens), response_length) if valid_response_length > 0: assert response_positions[0] == prompt_positions[-1] + 1 for j in range(1, valid_response_length): assert response_positions[j] == response_positions[j - 1] + 1 # print generated text for inspection if torch.distributed.get_rank() == 0: print(f"prompt: {decoded_prompt}") print(f"response: {decoded_response}") print("=" * 30) if __name__ == "__main__": test_hf_rollout(n=2, do_sample=True, validate=False) # test_hf_rollout(n=1, do_sample=False, validate=True) # test_hf_rollout(n=1, do_sample=True, validate=False)