Ra

applications/Chat/coati/dataset/prompt_dataset.py

@@ -35,14 +35,14 @@ def __init__(self,
             logger.info(f"Limiting dataset to {max_datasets_size} examples.")
             list_data_dict = list_data_dict[:max_datasets_size]
 
-        for data_dict in list_data_dict:
-            token = tokenizer(data_dict["instruction"],
-                              return_tensors='pt',
-                              max_length=max_length,
-                              padding='max_length',
-                              truncation=True)
-            for k, tensor in token.items():
-                self.keyed_prompt[k].extend(tensor.to(torch.cuda.current_device()).unbind())
+        instructions = [data_dict["instruction"] for data_dict in list_data_dict]
+        tokens = tokenizer(instructions,
+                           return_tensors='pt',
+                           max_length=max_length,
+                           padding='max_length',
+                           truncation=True)
+        for k, tensor in tokens.items():
+            self.keyed_prompt[k] = tensor.to(torch.cuda.current_device()).unbind()
 
     def __len__(self):
         return len(self.keyed_prompt["input_ids"])

applications/Chat/coati/dataset/sft_dataset.py

@@ -74,21 +74,18 @@ def __getitem__(self, idx):
         return dict(input_ids=self.input_ids[idx], labels=self.labels[idx])
 
 
-def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer, max_length: int) -> Dict:
+def _tokenize_fn(strings: Sequence[str],
+                 tokenizer: transformers.PreTrainedTokenizer,
+                 max_length: int
+                 ) -> Dict[str, torch.Tensor]:
     """Tokenize a list of strings."""
-    tokenized_list = [
-        tokenizer(
-            text,
-            return_tensors="pt",
-            padding="longest",
-            max_length=max_length,
-            truncation=True,
-        ) for text in strings
-    ]
-    input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list]
-    input_ids_lens = labels_lens = [
-        tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list
-    ]
+    tokenized_list = tokenizer(
+        strings, return_tensors="pt", padding="longest",
+        max_length=max_length, truncation=True
+    )
+    input_ids = labels = tokenized_list["input_ids"]
+    input_ids_lens = labels_lens = \
+        tokenized_list["input_ids"].ne(tokenizer.pad_token_id).sum(dim=-1)
     return dict(
         input_ids=input_ids,
         labels=labels,
@@ -105,7 +102,10 @@ def preprocess(
 ) -> Dict:
     """Preprocess the data by tokenizing."""
     examples = [s + t for s, t in zip(sources, targets)]
-    examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer, max_length) for strings in (examples, sources)]
+    examples_tokenized, sources_tokenized = [
+        _tokenize_fn(strings, tokenizer, max_length)
+        for strings in (examples, sources)
+    ]
     input_ids = examples_tokenized["input_ids"]
     labels = copy.deepcopy(input_ids)
     for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]):

applications/Chat/coati/experience_maker/naive.py

@@ -1,5 +1,6 @@
 import torch
-from coati.models.utils import compute_reward, normalize
+from coati.models.generation import generate_with_actor
+from coati.models.utils import calc_action_log_probs, compute_reward, normalize
 
 from .base import Experience, ExperienceMaker
 
@@ -16,13 +17,16 @@ def make_experience(self, input_ids: torch.Tensor, **generate_kwargs) -> Experie
         self.initial_model.eval()
         self.reward_model.eval()
 
-        sequences, attention_mask, action_mask = self.actor.generate(input_ids,
+        sequences, attention_mask, action_mask = generate_with_actor(self.actor,
+                                                                     input_ids,
                                                                      return_action_mask=True,
                                                                      **generate_kwargs)
         num_actions = action_mask.size(1)
 
-        action_log_probs = self.actor(sequences, num_actions, attention_mask)
-        base_action_log_probs = self.initial_model(sequences, num_actions, attention_mask)
+        actor_output = self.actor(sequences, attention_mask)
+        action_log_probs = calc_action_log_probs(actor_output, sequences, num_actions)
+        base_model_output = self.initial_model(sequences, attention_mask)
+        base_action_log_probs = calc_action_log_probs(base_model_output, sequences, num_actions)
         value = self.critic(sequences, action_mask, attention_mask)
         r = self.reward_model(sequences, attention_mask)
         reward = compute_reward(r, self.kl_coef, action_log_probs, base_action_log_probs, action_mask=action_mask)

applications/Chat/coati/models/base/__init__.py

@@ -1,24 +1,26 @@
+from typing import Union
+
 import torch.nn as nn
 
 from .actor import Actor
 from .critic import Critic
 from .reward_model import RewardModel
 
 
-def get_base_model(model: nn.Module) -> nn.Module:
+def get_base_model(model: Union[Actor, Critic, RewardModel]) -> nn.Module:
     """Get the base model of our wrapper classes.
-    For Actor, it's base model is ``actor.model`` and it's usually a ``transformers.PreTrainedModel``.
-    For Critic and RewardModel, it's base model is itself.
+    For Actor, Critic and RewardModel, return ``model.model``, 
+    it's usually a ``transformers.PreTrainedModel``.
 
     Args:
         model (nn.Module): model to get base model from
 
     Returns:
         nn.Module: the base model
     """
-    if isinstance(model, Actor):
-        return model.get_base_model()
-    return model
+    assert isinstance(model, (Actor, Critic, RewardModel)), \
+        f'Expect Actor, Critic or RewardModel, got {type(model)}, use unwrap_model first.'
+    return model.model
 
 
 __all__ = ['Actor', 'Critic', 'RewardModel', 'get_base_model']

applications/Chat/coati/models/base/actor.py

@@ -1,12 +1,9 @@
-from typing import Optional, Tuple, Union
+from typing import Optional
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
-from ..generation import generate
 from ..lora import LoRAModule
-from ..utils import log_probs_from_logits
 
 
 class Actor(LoRAModule):
@@ -24,42 +21,16 @@ def __init__(self, model: nn.Module, lora_rank: int = 0, lora_train_bias: str =
         self.model = model
         self.convert_to_lora()
 
-    @torch.no_grad()
-    def generate(
-        self,
-        input_ids: torch.Tensor,
-        return_action_mask: bool = True,
-        **kwargs
-    ) -> Union[Tuple[torch.LongTensor, torch.LongTensor], Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]:
-        sequences = generate(self.model, input_ids, **kwargs)
-        attention_mask = None
-        pad_token_id = kwargs.get('pad_token_id', None)
-        if pad_token_id is not None:
-            attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device)
-        if not return_action_mask:
-            return sequences, attention_mask, None
-        input_len = input_ids.size(1)
-        eos_token_id = kwargs.get('eos_token_id', None)
-        if eos_token_id is None:
-            action_mask = torch.ones_like(sequences, dtype=torch.bool)
-        else:
-            # left padding may be applied, only mask action
-            action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0
-            action_mask = F.pad(action_mask, (1 + input_len, -1), value=True)    # include eos token and input
-        action_mask[:, :input_len] = False
-        action_mask = action_mask[:, 1:]
-        return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):]
-
     def forward(self,
-                sequences: torch.LongTensor,
-                num_actions: int,
-                attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
-        """Returns action log probs
+                input_ids: torch.LongTensor,
+                attention_mask: Optional[torch.Tensor] = None,
+                **model_kwargs,  # HACK: `generate` method may pass more kwargs
+                ) -> torch.Tensor:
+        """Returns model output.
         """
-        output = self.model(sequences, attention_mask=attention_mask)
-        logits = output['logits']
-        log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:])
-        return log_probs[:, -num_actions:]
-
-    def get_base_model(self):
-        return self.model
+        output = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            **model_kwargs
+        )
+        return output

applications/Chat/coati/models/generation.py

@@ -1,8 +1,10 @@
-from typing import Any, Callable, Optional
+from typing import Any, Callable, Optional, Tuple, Union
 
 import torch
 import torch.distributed as dist
 import torch.nn as nn
+import torch.nn.functional as F
+
 
 try:
     from transformers.generation_logits_process import (
@@ -55,9 +57,8 @@ def sample(model: nn.Module,
     unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
 
     for _ in range(input_ids.size(1), max_length):
-        model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) if prepare_inputs_fn is not None else {
-            'input_ids': input_ids
-        }
+        model_inputs = prepare_inputs_fn(input_ids, **model_kwargs) \
+            if prepare_inputs_fn is not None else {'input_ids': input_ids}
         outputs = model(**model_inputs)
 
         next_token_logits = outputs['logits'][:, -1, :]
@@ -144,3 +145,35 @@ def generate(model: nn.Module,
         raise NotImplementedError
     else:
         raise ValueError("Unsupported generation mode")
+
+
+@torch.no_grad()
+def generate_with_actor(actor_model: nn.Module,
+                        input_ids: torch.Tensor,
+                        return_action_mask: bool = True,
+                        **kwargs
+                        ) -> Union[Tuple[torch.LongTensor, torch.LongTensor],
+                                   Tuple[torch.LongTensor, torch.LongTensor, torch.BoolTensor]]:
+    """Generate token sequence with actor model. Refer to `generate` for more details.
+    """
+    # generate sequences
+    sequences = generate(actor_model, input_ids, **kwargs)
+
+    # calculate auxiliary tensors
+    attention_mask = None
+    pad_token_id = kwargs.get('pad_token_id', None)
+    if pad_token_id is not None:
+        attention_mask = sequences.not_equal(pad_token_id).to(dtype=torch.long, device=sequences.device)
+    if not return_action_mask:
+        return sequences, attention_mask, None
+    input_len = input_ids.size(1)
+    eos_token_id = kwargs.get('eos_token_id', None)
+    if eos_token_id is None:
+        action_mask = torch.ones_like(sequences, dtype=torch.bool)
+    else:
+        # left padding may be applied, only mask action
+        action_mask = (sequences[:, input_len:] == eos_token_id).cumsum(dim=-1) == 0
+        action_mask = F.pad(action_mask, (1 + input_len, -1), value=True)    # include eos token and input
+    action_mask[:, :input_len] = False
+    action_mask = action_mask[:, 1:]
+    return sequences, attention_mask, action_mask[:, -(sequences.size(1) - input_len):]

applications/Chat/coati/models/utils.py

@@ -46,6 +46,25 @@ def log_probs_from_logits(logits: torch.Tensor, labels: torch.Tensor) -> torch.T
     return log_probs_labels.squeeze(-1)
 
 
+def calc_action_log_probs(output: torch.Tensor,
+                          sequences: torch.LongTensor,
+                          num_actions: int
+                          ) -> torch.Tensor:
+    """Calculate action log probs.
+
+    Args:
+        output (torch.Tensor): Output tensor of Actor.forward.
+        sequences (torch.LongTensor): Input sequences.
+        num_actions (int): Number of actions.
+
+    Returns:
+        torch.Tensor: Action log probs.
+    """
+    logits = output['logits']
+    log_probs = log_probs_from_logits(logits[:, :-1, :], sequences[:, 1:])
+    return log_probs[:, -num_actions:]
+
+
 def masked_mean(tensor: torch.Tensor, mask: torch.Tensor, dim: int = 1) -> torch.Tensor:
     tensor = tensor * mask
     tensor = tensor.sum(dim=dim)

applications/Chat/coati/trainer/ppo.py

@@ -3,8 +3,9 @@
 import torch
 import torch.nn as nn
 from coati.experience_maker import Experience, NaiveExperienceMaker
-from coati.models.base import Actor, Critic
+from coati.models.base import Actor, Critic, get_base_model
 from coati.models.loss import GPTLMLoss, PolicyLoss, ValueLoss
+from coati.models.utils import calc_action_log_probs
 from coati.replay_buffer import NaiveReplayBuffer
 from torch import Tensor
 from torch.optim import Optimizer
@@ -165,7 +166,8 @@ def training_step(self, experience: Experience) -> Dict[str, float]:
         self.critic.train()
         # policy loss
         num_actions = experience.action_mask.size(1)
-        action_log_probs = self.actor(experience.sequences, num_actions, attention_mask=experience.attention_mask)
+        actor_output = self.actor(experience.sequences, attention_mask=experience.attention_mask)
+        action_log_probs = calc_action_log_probs(actor_output, experience.sequences, num_actions)
         actor_loss = self.actor_loss_fn(action_log_probs,
                                         experience.action_log_probs,
                                         experience.advantages,
@@ -175,8 +177,8 @@ def training_step(self, experience: Experience) -> Dict[str, float]:
         if self.ptx_coef != 0:
             batch = next(iter(self.pretrain_dataloader))
             batch = to_device(batch, self.device)
-            ptx_log_probs = self.actor.get_base_model()(batch['input_ids'],
-                                                        attention_mask=batch['attention_mask'])['logits']
+            ptx_log_probs = self.actor(batch['input_ids'],
+                                       attention_mask=batch['attention_mask'])['logits']
             ptx_loss = self.ptx_loss_fn(ptx_log_probs, batch['labels'])
             actor_loss = ptx_loss * self.ptx_coef + actor_loss * (1 - self.ptx_coef)
 
@@ -200,14 +202,15 @@ def training_step(self, experience: Experience) -> Dict[str, float]:
         return {'reward': experience.reward.mean().item()}
 
 
-def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> None:
-    origin_model = strategy.unwrap_model(actor)
+def _set_default_generate_kwargs(strategy: Strategy, generate_kwargs: dict, actor: Actor) -> Dict:
+    unwrapper_model = strategy.unwrap_model(actor)
+    hf_model = get_base_model(unwrapper_model)
     new_kwargs = {**generate_kwargs}
     # use huggingface models method directly
-    if 'prepare_inputs_fn' not in generate_kwargs and hasattr(origin_model, 'prepare_inputs_for_generation'):
-        new_kwargs['prepare_inputs_fn'] = origin_model.prepare_inputs_for_generation
+    if 'prepare_inputs_fn' not in generate_kwargs and hasattr(hf_model, 'prepare_inputs_for_generation'):
+        new_kwargs['prepare_inputs_fn'] = hf_model.prepare_inputs_for_generation
 
-    if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(origin_model, '_update_model_kwargs_for_generation'):
-        new_kwargs['update_model_kwargs_fn'] = origin_model._update_model_kwargs_for_generation
+    if 'update_model_kwargs_fn' not in generate_kwargs and hasattr(hf_model, '_update_model_kwargs_for_generation'):
+        new_kwargs['update_model_kwargs_fn'] = hf_model._update_model_kwargs_for_generation
 
     return new_kwargs

applications/Chat/coati/trainer/strategies/base.py

@@ -4,7 +4,6 @@
 
 import torch
 import torch.nn as nn
-from coati.models.base import Actor, get_base_model
 from coati.replay_buffer import ReplayBuffer
 from torch.optim import Optimizer
 from torch.utils.data import DataLoader
@@ -69,21 +68,16 @@ def prepare(
             Union[List[ModelOrModelOptimPair], ModelOrModelOptimPair]: Models or model-optimizer-pairs in the original order.
         """
 
-        def prepare_model(model: nn.Module):
-            if isinstance(model, Actor):
-                return Actor(self.setup_model(model.get_base_model()))
-            return self.setup_model(model)
-
         rets = []
         for arg in models_or_model_optim_pairs:
             if isinstance(arg, tuple):
                 assert len(arg) == 2, f'Expect (model, optimizer) pair, got a tuple with size "{len(arg)}"'
                 model, optimizer = arg
-                model = prepare_model(model)
-                optimizer = self.setup_optimizer(optimizer, get_base_model(model))
+                model = self.setup_model(model)
+                optimizer = self.setup_optimizer(optimizer, model)
                 rets.append((model, optimizer))
             elif isinstance(arg, nn.Module):
-                rets.append(prepare_model(arg))
+                rets.append(self.setup_model(model))
             else:
                 raise RuntimeError(f'Expect model or (model, optimizer) pair, got {type(arg)}')
 
@@ -93,16 +87,15 @@ def prepare_model(model: nn.Module):
 
     @staticmethod
     def unwrap_model(model: nn.Module) -> nn.Module:
-        """Get the unwrapped model from a wrapped model. Useful for getting original huggingface model.
-        For Actor, it will unwrap `actor.model`.
+        """Get the unwrapped model from a wrapped model made by Strategy.prepare.
 
         Args:
             model (nn.Module): the model to unwrap
 
         Returns:
-            nn.Module: the original model (usually a huggingface model)
+            nn.Module: the original model
         """
-        return get_base_model(model)
+        return model
 
     @abstractmethod
     def save_model(self, model: nn.Module, path: str, only_rank0: bool = True) -> None:
@@ -133,4 +126,4 @@ def save_pretrained(self,
 
     @abstractmethod
     def get_model_state_dict_shard(self, model: nn.Module, **config):
-        pass
+        pass