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import os
import subprocess
import tempfile
from typing import List
import streamlit as st
import yaml
from trinity.algorithm import ALGORITHM_TYPE
from trinity.algorithm.advantage_fn import ADVANTAGE_FN
from trinity.algorithm.entropy_loss_fn import ENTROPY_LOSS_FN
from trinity.algorithm.kl_fn import KL_FN
from trinity.algorithm.policy_loss_fn import POLICY_LOSS_FN
from trinity.algorithm.sample_strategy import SAMPLE_STRATEGY
from trinity.common.constants import StorageType
from trinity.manager.config_registry import CONFIG_GENERATORS
from trinity.manager.config_registry.buffer_config_manager import (
get_train_batch_size,
parse_priority_fn_args,
)
from trinity.manager.config_registry.trainer_config_manager import use_critic
from trinity.utils.plugin_loader import load_plugins
register_map = {
"sample_strategy": SAMPLE_STRATEGY,
"policy_loss_fn": POLICY_LOSS_FN,
"advantage_fn": ADVANTAGE_FN,
"kl_loss_fn": KL_FN,
"kl_penalty_fn": KL_FN,
"entropy_loss_fn": ENTROPY_LOSS_FN,
}
class ConfigManager:
def __init__(self):
if "_init_config_manager" not in st.session_state:
self.reset_session_state()
load_plugins()
self.unfinished_fields = set()
CONFIG_GENERATORS.set_unfinished_fields(self.unfinished_fields)
st.set_page_config(page_title="Trinity-RFT Config Generator", page_icon=":robot:")
st.title("Trinity-RFT Config Generator")
self.maintain_session_state()
mode = st.pills(
"Select Mode",
options=["Beginner Mode", "Expert Mode"],
default="Beginner Mode",
label_visibility="collapsed",
)
if mode == "Beginner Mode":
self.beginner_mode()
else:
self.expert_mode()
if "config_generated" not in st.session_state:
st.session_state.config_generated = False
if "is_running" not in st.session_state:
st.session_state.is_running = False
self.generate_config()
def reset_session_state(self):
st.session_state["_init_config_manager"] = True
for key, value in CONFIG_GENERATORS.default_config.items():
st.session_state[key] = value
def maintain_session_state(self):
st.session_state["_init_config_manager"] = True
for key in CONFIG_GENERATORS.default_config:
st.session_state[key] = st.session_state[key]
def maintain_list_state(prefix, key_list):
last_idx, del_num = 0, 0
for idx in range(st.session_state[f"_{prefix}s_num"]):
if st.session_state.get(f"{prefix}_{idx}_del_flag", False):
del_num += 1
continue
for key in key_list:
full_key = f"{prefix}_{idx}_{key}"
last_full_key = f"{prefix}_{last_idx}_{key}"
st.session_state[last_full_key] = st.session_state[full_key]
last_idx += 1
st.session_state[f"_{prefix}s_num"] -= del_num
self.eval_dataset_keys = [
"name",
"path",
"split",
"subset_name",
"prompt_key",
"response_key",
"temperature",
"logprobs",
"n",
]
maintain_list_state("eval_taskset", self.eval_dataset_keys)
self.inference_model_keys = [
"model_path",
"engine_num",
"tensor_parallel_size",
"enforce_eager",
"enable_prefix_caching",
"enable_chunked_prefill",
"gpu_memory_utilization",
"dtype",
"seed",
"enable_thinking",
"enable_history",
"enable_openai_api",
"enable_auto_tool_choice",
"tool_call_parser",
"reasoning_parser",
]
maintain_list_state("auxiliary_model", self.inference_model_keys)
def get_configs(self, *config_names: str, columns_spec: List[int] = None):
CONFIG_GENERATORS.get_configs(*config_names, columns_spec=columns_spec)
def beginner_mode(self):
st.subheader("Global Config")
self.get_configs("project", "exp_name")
self.get_configs("checkpoint_root_dir")
self.get_configs("monitor_type", "log_level", "save_interval")
st.subheader("Model Config")
self.get_configs("model_path", "max_model_len", columns_spec=[3, 1])
st.subheader("Algorithm Config")
self.get_configs("algorithm_type", "repeat_times", "actor_lr", "critic_lr")
st.subheader("Dataset Config")
if st.session_state["algorithm_type"] not in ("dpo", "sft"):
self.get_configs("taskset_path", "explore_batch_size", columns_spec=[3, 1])
else:
self.get_configs("experience_buffer_path", "train_batch_size", columns_spec=[3, 1])
if st.session_state["algorithm_type"] == "dpo":
self.get_configs("dpo_dataset_kwargs")
elif st.session_state["algorithm_type"] == "sft":
self.get_configs("sft_dataset_kwargs")
else:
self.get_configs("taskset_args")
self.get_configs("default_workflow_type", "default_reward_fn_type")
self.get_configs("total_epochs", "total_steps")
st.subheader("Resource Config")
self.get_configs("node_num", "gpu_per_node")
self.get_configs("engine_num", "tensor_parallel_size")
self.get_configs("trainer_gpu_num_display", "actor_ulysses_sequence_parallel_size")
if st.session_state["algorithm_type"] not in ("dpo", "sft"):
st.subheader("Synchronizer Config")
st.caption("Synchronization between trainer and explorer.")
self.get_configs("sync_method", "sync_style", "sync_interval")
def _expert_model_part(self):
self.get_configs("project", "exp_name")
self.get_configs("model_path")
self.get_configs("critic_model_path")
self.get_configs("checkpoint_root_dir")
self.get_configs("monitor_type", "node_num", "gpu_per_node")
self.get_configs("max_response_tokens", "max_model_len")
def _expert_buffer_part(self):
self.get_configs("total_epochs", "total_steps", "explore_batch_size", "train_batch_size")
self.get_configs(
"default_workflow_type", "default_eval_workflow_type", "default_reward_fn_type"
)
if st.session_state["algorithm_type"] == "dpo":
with st.expander("DPO Dataset Configs", expanded=True):
self.get_configs("experience_buffer_path")
self.get_configs("storage_type")
self.get_configs("dpo_dataset_kwargs")
elif st.session_state["algorithm_type"] == "sft":
with st.expander("SFT Dataset Configs", expanded=True):
self.get_configs("experience_buffer_path")
self.get_configs("storage_type")
self.get_configs("sft_dataset_kwargs")
else:
with st.expander("Taskset Configs", expanded=True):
self.get_configs("taskset_path")
self.get_configs("taskset_args")
with st.expander("Eval Tasksets Configs", expanded=True):
self.get_configs("eval_tasksets")
if st.session_state["algorithm_type"] not in ("dpo", "sft"):
with st.expander("Experience Buffer Configs", expanded=True):
self.get_configs("storage_type")
self.get_configs("experience_buffer_path")
self.get_configs("enable_replay_buffer")
self.get_configs("reuse_cooldown_time", "priority_fn")
self.get_configs("priority_fn_args")
# TODO: used for SQL storage
# self.buffer_advanced_tab = st.expander("Advanced Config")
# with self.buffer_advanced_tab:
# self.get_configs("buffer_max_retry_times", "max_retry_interval")
def _expert_explorer_part(self):
self.get_configs("sync_method", "sync_style", "sync_interval", "sync_timeout")
self.get_configs("runner_per_model", "eval_interval")
with st.expander("Rollout Model Config", expanded=True):
self.get_configs("engine_type", "engine_num", "tensor_parallel_size")
self.get_configs("gpu_memory_utilization", "dtype", "seed")
self.get_configs("enforce_eager", "enable_prefix_caching", "enable_chunked_prefill")
self.get_configs("enable_thinking", "enable_history")
self.get_configs("enable_openai_api", "enable_auto_tool_choice")
self.get_configs("tool_call_parser", "reasoning_parser")
with st.expander("Auxiliary Models", expanded=True):
self.get_configs("auxiliary_models")
def _expert_trainer_part(self):
self.get_configs("algorithm_type", "repeat_times", "save_interval")
self.get_configs("policy_loss_fn", "advantage_fn", "sample_strategy")
self.get_configs("kl_penalty_fn", "kl_loss_fn", "kl_coef_in_kl_loss_fn")
self.get_configs("entropy_loss_fn", "entropy_coef_in_entropy_loss_fn")
with st.expander("Advanced Algorithm Config"):
algorithm = ALGORITHM_TYPE.get(st.session_state["algorithm_type"])
default_config = algorithm.default_config()
config_key_list = []
for key in default_config.keys():
value = st.session_state[key]
if key == "repeat_times":
continue
default_args = register_map[key].get(value).default_args()
for sub_key in default_args.keys():
full_key = sub_key + "_in_" + key
if full_key in ("kl_coef_in_kl_loss_fn", "entropy_coef_in_entropy_loss_fn"):
continue
config_key_list.append(full_key)
idx = 0
while idx < len(config_key_list):
delta = 3 if len(config_key_list) - idx != 4 else 2
key_list = config_key_list[idx : idx + delta]
idx += delta
self.get_configs(*key_list)
self.get_configs("enable_preview")
if st.session_state["trainer_type"] == "verl":
self._expert_verl_trainer_part()
def _expert_verl_training_part(self):
st.subheader("RL Training Config")
self.get_configs("training_args")
self.get_configs("ppo_epochs", "training_strategy", "resume_mode", "impl_backend")
self.get_configs("resume_from_path")
if st.session_state["training_strategy"] == "fsdp":
self.get_configs("param_offload", "optimizer_offload", "forward_prefetch")
elif st.session_state["training_strategy"] == "fsdp2":
self.get_configs("offload_policy", "reshard_after_forward")
elif st.session_state["training_strategy"] == "megatron":
with st.expander("Megatron Config"):
self.get_configs("param_offload", "grad_offload", "optimizer_offload")
self.get_configs(
"tensor_model_parallel_size",
"pipeline_model_parallel_size",
"virtual_pipeline_model_parallel_size",
)
self.get_configs(
"expert_model_parallel_size",
"expert_tensor_parallel_size",
"context_parallel_size",
)
self.get_configs(
"sequence_parallel",
"use_distributed_optimizer",
"use_dist_checkpointing",
"use_mbridge",
)
self.get_configs("dist_checkpointing_path")
self.get_configs(
"recompute_granularity", "recompute_method", "recompute_num_layers"
)
self.get_configs("recompute_modules")
with st.expander("Advanced Config"):
self.get_configs("critic_warmup")
self.get_configs("default_hdfs_dir")
self.get_configs("del_local_ckpt_after_load")
self.get_configs("max_actor_ckpt_to_keep", "max_critic_ckpt_to_keep")
def _expert_verl_actor_part(self):
st.subheader("Actor Model Config")
self.get_configs("actor_lr", "actor_lr_scheduler_type", "actor_lr_warmup_steps_ratio")
self.get_configs("actor_grad_clip", "actor_ulysses_sequence_parallel_size")
self.get_configs(
"actor_ppo_micro_batch_size_per_gpu",
"ref_log_prob_micro_batch_size_per_gpu",
"actor_ppo_max_token_len_per_gpu",
)
self.get_configs("actor_entropy_from_logits_with_chunking", "actor_entropy_checkpointing")
self.get_configs("actor_load_checkpoint")
self.get_configs("actor_save_checkpoint")
def _expert_verl_critic_part(self):
st.subheader("Critic Model Config")
self.get_configs(
"critic_ppo_micro_batch_size_per_gpu", "critic_ulysses_sequence_parallel_size"
)
self.get_configs("critic_lr", "critic_lr_scheduler_type", "critic_lr_warmup_steps_ratio")
self.get_configs("critic_grad_clip", "critic_cliprange_value")
self.get_configs("critic_load_checkpoint", "critic_save_checkpoint")
def _expert_verl_trainer_part(self):
name2func = {
"RL Training Config": self._expert_verl_training_part,
"Actor and Ref Config": self._expert_verl_actor_part,
}
if use_critic():
name2func["Critic Config"] = self._expert_verl_critic_part
tabs = st.tabs([name for name in name2func])
for tab, func in zip(tabs, name2func.values()):
with tab:
func()
def expert_mode(self):
tab2func = {
"Model": self._expert_model_part,
"Buffer": self._expert_buffer_part,
"Explorer and Synchronizer": self._expert_explorer_part,
"Trainer": self._expert_trainer_part,
}
if st.session_state["mode"] == "train":
del tab2func["Explorer and Synchronizer"]
tabs = st.tabs(list(tab2func.keys()))
for tab, func in zip(tabs, tab2func.values()):
with tab:
func()
def _generate_verl_config(self):
balance_batch = "balance_batch" in st.session_state["training_args"]
enable_gradient_checkpointing = (
"gradient_checkpointing" in st.session_state["training_args"]
)
use_remove_padding = "remove_padding" in st.session_state["training_args"]
use_dynamic_bsz = "dynamic_bsz" in st.session_state["training_args"]
use_fused_kernels = "use_fused_kernels" in st.session_state["training_args"]
if st.session_state["training_strategy"] == "fsdp":
distribution_config = {
"fsdp_config": {
"fsdp_size": -1,
"wrap_policy": {"min_num_params": 0},
"param_offload": st.session_state["param_offload"],
"optimizer_offload": st.session_state["optimizer_offload"],
"forward_prefetch": st.session_state["forward_prefetch"],
}
}
elif st.session_state["training_strategy"] == "fsdp2":
distribution_config = {
"fsdp_config": {
"fsdp_size": -1,
"offload_policy": st.session_state["offload_policy"],
"reshard_after_forward": st.session_state["reshard_after_forward"],
}
}
elif st.session_state["training_strategy"] == "megatron":
distribution_config = {
"megatron": {
"param_offload": st.session_state["param_offload"],
"grad_offload": st.session_state["grad_offload"],
"optimizer_offload": st.session_state["optimizer_offload"],
"tensor_model_parallel_size": st.session_state["tensor_model_parallel_size"],
"pipeline_model_parallel_size": st.session_state[
"pipeline_model_parallel_size"
],
"virtual_pipeline_model_parallel_size": st.session_state[
"virtual_pipeline_model_parallel_size"
],
"expert_model_parallel_size": st.session_state["expert_model_parallel_size"],
"expert_tensor_parallel_size": st.session_state["expert_tensor_parallel_size"],
"context_parallel_size": st.session_state["context_parallel_size"],
"sequence_parallel": st.session_state["sequence_parallel"],
"use_distributed_optimizer": st.session_state["use_distributed_optimizer"],
"use_dist_checkpointing": st.session_state["use_dist_checkpointing"],
"dist_checkpointing_path": st.session_state["dist_checkpointing_path"],
"seed": st.session_state["seed"],
# TODO: override_ddp_config
"override_transformer_config": {
"recompute_granularity": st.session_state["recompute_granularity"],
"recompute_modules": st.session_state["recompute_modules"],
"recompute_method": st.session_state["recompute_method"],
"recompute_num_layers": st.session_state["recompute_num_layers"],
},
"use_mbridge": st.session_state["use_mbridge"],
}
}
else:
distribution_config = {}
ppo_max_token_len_per_gpu = (
st.session_state["repeat_times"] * st.session_state["max_model_len"]
)
trainer_config = {
"actor_rollout_ref": {
"model": {
"external_lib": None,
"override_config": {},
"enable_gradient_checkpointing": enable_gradient_checkpointing,
"use_remove_padding": use_remove_padding,
"use_fused_kernels": use_fused_kernels,
},
"actor": {
"strategy": st.session_state["training_strategy"],
"ppo_micro_batch_size_per_gpu": st.session_state[
"actor_ppo_micro_batch_size_per_gpu"
],
"use_dynamic_bsz": use_dynamic_bsz,
"ppo_max_token_len_per_gpu": st.session_state["actor_ppo_max_token_len_per_gpu"]
or ppo_max_token_len_per_gpu,
"ppo_epochs": st.session_state["ppo_epochs"],
"ulysses_sequence_parallel_size": st.session_state[
"actor_ulysses_sequence_parallel_size"
],
"entropy_from_logits_with_chunking": st.session_state[
"actor_entropy_from_logits_with_chunking"
],
"entropy_checkpointing": st.session_state["actor_entropy_checkpointing"],
"checkpoint": {
"load_contents": st.session_state["actor_load_checkpoint"],
"save_contents": st.session_state["actor_save_checkpoint"],
},
},
"ref": {
"log_prob_use_dynamic_bsz": use_dynamic_bsz,
"log_prob_max_token_len_per_gpu": ppo_max_token_len_per_gpu,
"ulysses_sequence_parallel_size": st.session_state[
"actor_ulysses_sequence_parallel_size"
],
"entropy_from_logits_with_chunking": st.session_state[
"actor_entropy_from_logits_with_chunking"
],
"entropy_checkpointing": st.session_state["actor_entropy_checkpointing"],
},
},
"critic": {},
"trainer": {
"balance_batch": balance_batch,
"resume_mode": st.session_state["resume_mode"],
"resume_from_path": st.session_state["resume_from_path"],
"default_hdfs_dir": st.session_state["default_hdfs_dir"],
"del_local_ckpt_after_load": st.session_state["del_local_ckpt_after_load"],
"max_actor_ckpt_to_keep": st.session_state["max_actor_ckpt_to_keep"],
"max_critic_ckpt_to_keep": st.session_state["max_critic_ckpt_to_keep"],
},
}
trainer_config["actor_rollout_ref"]["actor"].update(copy.deepcopy(distribution_config))
trainer_config["actor_rollout_ref"]["ref"].update(copy.deepcopy(distribution_config))
if use_fused_kernels:
trainer_config["actor_rollout_ref"]["model"]["fused_kernel_options"] = {
"impl_backend": st.session_state["impl_backend"],
}
if use_critic():
trainer_config["trainer"]["critic_warmup"] = st.session_state["critic_warmup"]
trainer_config["critic"] = {
"strategy": st.session_state["training_strategy"],
"optim": {
"lr": st.session_state["critic_lr"],
"lr_warmup_steps_ratio": st.session_state["critic_lr_warmup_steps_ratio"],
"lr_scheduler_type": st.session_state["critic_lr_scheduler_type"],
},
"model": {
"override_config": {},
"external_lib": None,
"enable_gradient_checkpointing": enable_gradient_checkpointing,
"use_remove_padding": use_remove_padding,
},
"ppo_mini_batch_size": get_train_batch_size(),
"ppo_micro_batch_size_per_gpu": st.session_state[
"critic_ppo_micro_batch_size_per_gpu"
],
"forward_micro_batch_size_per_gpu": st.session_state[
"critic_ppo_micro_batch_size_per_gpu"
],
"use_dynamic_bsz": use_dynamic_bsz,
"ppo_max_token_len_per_gpu": ppo_max_token_len_per_gpu * 2,
"forward_max_token_len_per_gpu": ppo_max_token_len_per_gpu * 2,
"ulysses_sequence_parallel_size": st.session_state[
"critic_ulysses_sequence_parallel_size"
],
"ppo_epochs": st.session_state["ppo_epochs"],
"grad_clip": st.session_state["critic_grad_clip"],
"cliprange_value": st.session_state["critic_cliprange_value"],
"checkpoint": {
"load_contents": st.session_state["critic_load_checkpoint"],
"save_contents": st.session_state["critic_save_checkpoint"],
},
}
if st.session_state["training_strategy"] in {"fsdp", "fsdp2"}:
trainer_config["critic"]["model"].update(copy.deepcopy(distribution_config))
elif st.session_state["training_strategy"] == "megatron":
trainer_config["critic"].update(copy.deepcopy(distribution_config))
else:
del trainer_config["critic"]
return trainer_config
def _gen_algorithm_config(self):
algorithm_config = {
"algorithm_type": st.session_state["algorithm_type"],
}
algorithm = ALGORITHM_TYPE.get(st.session_state["algorithm_type"])
default_config = algorithm.default_config()
current_config = {}
for key in default_config.keys():
current_config[key] = value = st.session_state[key]
if key == "repeat_times":
continue
default_args = register_map[key].get(value).default_args()
args = {}
for sub_key in default_args.keys():
full_key = sub_key + "_in_" + key
args[sub_key] = st.session_state.get(full_key, default_args[sub_key])
if default_args != args:
current_config[key + "_args"] = args
if default_config != current_config:
algorithm_config.update(current_config)
optimizer_config = {
"lr": st.session_state["actor_lr"],
"lr_warmup_steps_ratio": st.session_state["actor_lr_warmup_steps_ratio"],
"lr_scheduler_type": st.session_state["actor_lr_scheduler_type"],
}
algorithm_config["optimizer"] = optimizer_config
return algorithm_config
def _gen_buffer_config(self):
experience_buffer_path = st.session_state["experience_buffer_path"].strip()
if st.session_state["algorithm_type"] not in ("dpo", "sft"):
if (
not experience_buffer_path
and st.session_state["storage_type"] == StorageType.SQL.value
):
experience_buffer_path = f"sqlite:///{os.path.join(st.session_state['checkpoint_root_dir'], '.cache', st.session_state['project'], st.session_state['exp_name'])}/data.db"
else:
st.session_state["storage_type"] = StorageType.FILE.value
buffer_config = {
"batch_size": st.session_state["explore_batch_size"],
"train_batch_size": st.session_state["train_batch_size"],
"total_epochs": st.session_state["total_epochs"],
"total_steps": st.session_state["total_steps"],
"explorer_input": {},
"trainer_input": {
"experience_buffer": {
"name": "experience_buffer",
"storage_type": st.session_state["storage_type"],
"path": experience_buffer_path,
},
},
}
if not experience_buffer_path:
del buffer_config["trainer_input"]["experience_buffer"]["path"]
if st.session_state["train_batch_size"] is None:
if st.session_state["algorithm_type"] in ("dpo", "sft"):
buffer_config["train_batch_size"] = (
st.session_state["explore_batch_size"] * st.session_state["repeat_times"]
)
del buffer_config["batch_size"]
else:
del buffer_config["train_batch_size"]
if st.session_state["algorithm_type"] not in ("dpo", "sft"):
experience_buffer = buffer_config["trainer_input"]["experience_buffer"]
experience_buffer["replay_buffer"] = {
"enable": st.session_state["enable_replay_buffer"],
"priority_fn": st.session_state["priority_fn"],
"reuse_cooldown_time": st.session_state["reuse_cooldown_time"],
"priority_fn_args": parse_priority_fn_args(st.session_state["priority_fn_args"]),
}
if st.session_state["mode"] != "train":
buffer_config["explorer_input"] = {
"taskset": {
"name": "taskset",
"storage_type": StorageType.FILE.value,
"path": st.session_state["taskset_path"],
"split": st.session_state["taskset_split"],
"subset_name": st.session_state["taskset_subset_name"],
"format": {
"prompt_key": st.session_state["taskset_prompt_key"],
"response_key": st.session_state["taskset_response_key"],
},
"rollout_args": {
"temperature": st.session_state["temperature"],
"logprobs": st.session_state["logprobs"],
},
},
"eval_tasksets": [],
"default_workflow_type": st.session_state["default_workflow_type"],
"default_eval_workflow_type": st.session_state["default_eval_workflow_type"],
"default_reward_fn_type": st.session_state["default_reward_fn_type"],
}
for idx in range(st.session_state["_eval_tasksets_num"]):
if st.session_state[f"eval_taskset_{idx}_path"].strip():
buffer_config["explorer_input"]["eval_tasksets"].append(
{
"name": st.session_state[f"eval_taskset_{idx}_name"],
"path": st.session_state[f"eval_taskset_{idx}_path"],
"split": st.session_state[f"eval_taskset_{idx}_split"],
"subset_name": st.session_state[f"eval_taskset_{idx}_subset_name"],
"format": {
"prompt_key": st.session_state[f"eval_taskset_{idx}_prompt_key"],
"response_key": st.session_state[
f"eval_taskset_{idx}_response_key"
],
},
"rollout_args": {
"temperature": st.session_state[f"eval_taskset_{idx}_temperature"],
"logprobs": st.session_state[f"eval_taskset_{idx}_logprobs"],
"n": st.session_state[f"eval_taskset_{idx}_n"],
},
}
)
else:
del buffer_config["explorer_input"]
if st.session_state["algorithm_type"] == "dpo":
experience_buffer = buffer_config["trainer_input"]["experience_buffer"]
experience_buffer["split"] = st.session_state["dpo_dataset_train_split"]
experience_buffer["format"] = {
"prompt_type": st.session_state["dpo_dataset_prompt_type"],
"prompt_key": st.session_state["dpo_dataset_prompt_key"],
"chosen_key": st.session_state["dpo_dataset_chosen_key"],
"rejected_key": st.session_state["dpo_dataset_rejected_key"],
}
elif st.session_state["algorithm_type"] == "sft":
experience_buffer = buffer_config["trainer_input"]["experience_buffer"]
experience_buffer["split"] = st.session_state["sft_dataset_train_split"]
experience_buffer["format"] = {
"prompt_type": st.session_state["sft_dataset_prompt_type"],
"prompt_key": st.session_state["sft_dataset_prompt_key"],
"messages_key": st.session_state["sft_dataset_messages_key"],
}
return buffer_config
def _gen_explorer_config(self):
explorer_config = {
"runner_per_model": st.session_state["runner_per_model"],
"rollout_model": {
key: st.session_state[key]
for key in self.inference_model_keys
if key != "model_path"
# "chat_template": None, # TODO: add chat template
},
"auxiliary_models": [],
"eval_interval": st.session_state["eval_interval"],
}
for i in range(st.session_state["_auxiliary_models_num"]):
auxiliary_model_config = {
key: st.session_state[f"auxiliary_model_{i}_{key}"]
for key in self.inference_model_keys
}
explorer_config["auxiliary_models"].append(auxiliary_model_config)
return explorer_config
def generate_config(self):
if st.session_state["trainer_type"] == "verl":
trainer_config = self._generate_verl_config()
else:
raise ValueError(f"Invalid trainer type: {st.session_state['trainer_type']}")
if len(self.unfinished_fields) > 0:
disable_generate = True
help_messages = (
f"Please check following fields: `{'`, `'.join(self.unfinished_fields)}`"
)
else:
disable_generate = False
help_messages = None
if st.button(
"Generate Config",
disabled=disable_generate,
help=help_messages,
use_container_width=True,
icon=":material/create_new_folder:",
):
st.session_state.config_generated = True
st.session_state.is_running = False
if st.session_state.config_generated:
config = {
"mode": st.session_state["mode"],
"project": st.session_state["project"],
"name": st.session_state["exp_name"],
"checkpoint_root_dir": st.session_state["checkpoint_root_dir"],
"algorithm": self._gen_algorithm_config(),
"data_processor": {}, # TODO: Add data processor config
"model": {
"model_path": st.session_state["model_path"],
"max_prompt_tokens": st.session_state["max_prompt_tokens"],
"min_response_tokens": st.session_state["min_response_tokens"],
"max_response_tokens": st.session_state["max_response_tokens"],
"max_model_len": st.session_state["max_model_len"],
},
"cluster": {
"node_num": st.session_state["node_num"],
"gpu_per_node": st.session_state["gpu_per_node"],
},
"buffer": self._gen_buffer_config(),
"explorer": self._gen_explorer_config(),
"trainer": {
"trainer_type": st.session_state["trainer_type"],
"save_interval": st.session_state["save_interval"],
"enable_preview": st.session_state["enable_preview"],
"grad_clip": st.session_state["actor_grad_clip"],
"trainer_config": trainer_config,
},
"monitor": {
"monitor_type": st.session_state["monitor_type"],
},
"synchronizer": {
"sync_method": st.session_state["sync_method"],
"sync_style": st.session_state["sync_style"],
"sync_interval": st.session_state["sync_interval"],
"sync_timeout": st.session_state["sync_timeout"],
},
"log": {
"level": st.session_state["log_level"],
},
}
if use_critic():
config["model"]["critic_model_path"] = (
st.session_state["critic_model_path"].strip()
if st.session_state["critic_model_path"].strip()
else st.session_state["model_path"]
)
st.session_state.config_generated = True
st.subheader("Generated Config File")
# buttons = st.container()
# save_btn, run_btn = buttons.columns(2, vertical_alignment="bottom")
yaml_config = yaml.dump(config, allow_unicode=True, sort_keys=False)
# save_btn.download_button(
# "Save",
# data=yaml_config,
# file_name=f"{config['project']}-{config['name']}.yaml",
# mime="text/plain",
# icon=":material/download:",
# use_container_width=True,
# )
# run_btn.button(
# "Run",
# on_click=self.run_config,
# args=(
# buttons,
# yaml_config,
# ),
# icon=":material/terminal:",
# use_container_width=True,
# disabled=st.session_state.is_running,
# )
st.download_button(
"Save",
data=yaml_config,
file_name=f"{config['project']}-{config['name']}.yaml",
mime="text/plain",
icon=":material/download:",
use_container_width=True,
)
st.code(yaml_config, language="yaml")
def run_config(self, parent, yaml_config: str) -> None:
st.session_state.is_running = True
import ray
# first check if ray is running
ray_status = subprocess.run(
["ray", "status"],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
text=True,
)
if ray_status.returncode != 0:
parent.warning(
"Ray cluster is not running. Please start Ray first using `ray start --head`."
)
return
context = ray.init(ignore_reinit_error=True)
dashboard_url = context.dashboard_url
# save config to temp file
with tempfile.NamedTemporaryFile(mode="w", suffix=".yaml", delete=False) as tmpfile:
tmpfile.write(yaml_config)
tmpfile_path = tmpfile.name
# submit ray job
try:
subprocess.run(
[
"ray",
"job",
"submit",
"--no-wait",
"--",
"python",
"-m",
"trinity.cli.launcher",
"run",
"--config",
tmpfile_path,
],
text=True,
capture_output=True,
check=True,
)
parent.success(
f"Job submitted successfully!\n\n"
f"View progress in the Ray Dashboard: http://{dashboard_url}",
icon="✅",
)
except subprocess.CalledProcessError as e:
parent.error(f"Failed to submit job:\n\n{e.stderr}", icon="❌")
st.session_state.is_running = False
if __name__ == "__main__":
config_manager = ConfigManager()
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