decouple loss and generation

trl/trainer/grpo_trainer.py

@@ -366,32 +366,41 @@ def _set_signature_columns_if_needed(self):
         if self._signature_columns is None:
             self._signature_columns = ["prompt"]
 
-    # Trainer "prepares" the inputs before calling `compute_loss`. It converts to tensor and move to device.
-    # Since we preprocess the data in `compute_loss`, we need to override this method to skip this step.
-    def _prepare_inputs(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]:
-        return inputs
-
-    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
-        if return_outputs:
-            raise ValueError("The GRPOTrainer does not support returning outputs")
+    # Get the per-token log probabilities for the completions for the model and the reference model
+    def _get_per_token_logps(self, model, input_ids, attention_mask, logits_to_keep):
+        # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
+        logits = model(
+            input_ids=input_ids, attention_mask=attention_mask, logits_to_keep=logits_to_keep + 1
+        ).logits  # (B, L, V)
+        logits = logits[:, :-1, :]  # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+
+        # Compute the log probabilities for the input tokens. Use a loop to reduce memory peak.
+        per_token_logps = []
+        for logits_row, input_ids_row in zip(logits, input_ids[:, -logits_to_keep:]):
+            log_probs = logits_row.log_softmax(dim=-1)
+            token_log_prob = torch.gather(log_probs, dim=1, index=input_ids_row.unsqueeze(1)).squeeze(1)
+            per_token_logps.append(token_log_prob)
+        return torch.stack(per_token_logps)
 
+    def _prepare_inputs(self, inputs: dict[str, Union[torch.Tensor, Any]]) -> dict[str, Union[torch.Tensor, Any]]:
         device = self.accelerator.device
         prompts = [x["prompt"] for x in inputs]
         prompts_text = [maybe_apply_chat_template(example, self.processing_class)["prompt"] for example in inputs]
         prompt_inputs = self.processing_class(
             prompts_text, return_tensors="pt", padding=True, padding_side="left", add_special_tokens=False
         )
         prompt_inputs = super()._prepare_inputs(prompt_inputs)
+        prompt_ids, prompt_mask = prompt_inputs["input_ids"], prompt_inputs["attention_mask"]
 
         if self.max_prompt_length is not None:
-            prompt_inputs["input_ids"] = prompt_inputs["input_ids"][:, -self.max_prompt_length :]
-            prompt_inputs["attention_mask"] = prompt_inputs["attention_mask"][:, -self.max_prompt_length :]
+            prompt_ids = prompt_ids[:, -self.max_prompt_length :]
+            prompt_mask = prompt_mask[:, -self.max_prompt_length :]
 
         # Generate completions using either vLLM or regular generation
         if self.args.use_vllm:
             # First, have main process load weights if needed
             if self.state.global_step != self._last_loaded_step:
-                with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
+                with unwrap_model_for_generation(self.model, self.accelerator) as unwrapped_model:
                     state_dict = unwrapped_model.state_dict()
                 if self.accelerator.is_main_process:
                     llm_model = self.llm.llm_engine.model_executor.driver_worker.model_runner.model
@@ -418,18 +427,21 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N
             # Pad the completions, and concatenate them with the prompts
             completion_ids = [torch.tensor(ids, device=device) for ids in completion_ids]
             completion_ids = pad(completion_ids, padding_value=self.processing_class.pad_token_id)
-            prompt_inputs_repeated = torch.repeat_interleave(prompt_inputs["input_ids"], self.num_generations, dim=0)
-            prompt_completion_ids = torch.cat([prompt_inputs_repeated, completion_ids], dim=1)
+            prompt_ids = torch.repeat_interleave(prompt_ids, self.num_generations, dim=0)
+            prompt_mask = torch.repeat_interleave(prompt_mask, self.num_generations, dim=0)
+            prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
         else:
             # Regular generation path
-            with unwrap_model_for_generation(model, self.accelerator) as unwrapped_model:
+            with unwrap_model_for_generation(self.model, self.accelerator) as unwrapped_model:
                 prompt_completion_ids = unwrapped_model.generate(
-                    **prompt_inputs, generation_config=self.generation_config
+                    prompt_ids, attention_mask=prompt_mask, generation_config=self.generation_config
                 )
 
-        # Compute prompt length and extract completion ids
-        prompt_length = prompt_inputs["input_ids"].size(1)
-        completion_ids = prompt_completion_ids[:, prompt_length:]
+            # Compute prompt length and extract completion ids
+            prompt_length = prompt_ids.size(1)
+            prompt_ids = prompt_completion_ids[:, :prompt_length]
+            completion_ids = prompt_completion_ids[:, prompt_length:]
+            prompt_mask = prompt_mask.repeat_interleave(self.num_generations, dim=0)
 
         # Mask everything after the first EOS token
         is_eos = completion_ids == self.processing_class.eos_token_id
@@ -439,49 +451,28 @@ def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=N
         completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int()
 
         # Concatenate prompt_mask with completion_mask for logit computation
-        prompt_mask_repeated = prompt_inputs["attention_mask"].repeat_interleave(self.num_generations, dim=0)
-        attention_mask = torch.cat([prompt_mask_repeated, completion_mask], dim=1)  # (B*G, P+C)
-
-        # Get the per-token log probabilities for the completions for the model and the reference model
-        def get_per_token_logps(model, input_ids, attention_mask, logits_to_keep):
-            # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
-            logits = model(
-                input_ids=input_ids, attention_mask=attention_mask, logits_to_keep=logits_to_keep + 1
-            ).logits  # (B, L, V)
-            logits = logits[:, :-1, :]  # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
-
-            # Compute the log probabilities for the input tokens. Use a loop to reduce memory peak.
-            per_token_logps = []
-            for logits_row, input_ids_row in zip(logits, input_ids[:, -logits_to_keep:]):
-                log_probs = logits_row.log_softmax(dim=-1)
-                token_log_prob = torch.gather(log_probs, dim=1, index=input_ids_row.unsqueeze(1)).squeeze(1)
-                per_token_logps.append(token_log_prob)
-            return torch.stack(per_token_logps)
+        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)  # (B*G, P+C)
 
         logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
-        per_token_logps = get_per_token_logps(model, prompt_completion_ids, attention_mask, logits_to_keep)
 
         with torch.inference_mode():
             if self.ref_model is not None:
-                ref_per_token_logps = get_per_token_logps(
+                ref_per_token_logps = self._get_per_token_logps(
                     self.ref_model, prompt_completion_ids, attention_mask, logits_to_keep
                 )
             else:
-                with self.accelerator.unwrap_model(model).disable_adapter():
-                    ref_per_token_logps = get_per_token_logps(
-                        model, prompt_completion_ids, attention_mask, logits_to_keep
+                with self.accelerator.unwrap_model(self.model).disable_adapter():
+                    ref_per_token_logps = self._get_per_token_logps(
+                        self.model, prompt_completion_ids, attention_mask, logits_to_keep
                     )
 
-        # Compute the KL divergence between the model and the reference model
-        per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1
-
         # Decode the generated completions
         completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
         if is_conversational(inputs[0]):
             completions = [[{"role": "assistant", "content": completion}] for completion in completions]
 
         # Compute the rewards
-        prompts = [prompt for prompt in prompts for _ in range(self.num_generations)]
+        prompts = [prompt for prompt in prompts for _ in range(self.num_generations)]  # repeat prompts
 
         rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device)
         for i, (reward_func, reward_processing_class) in enumerate(
@@ -521,15 +512,7 @@ def get_per_token_logps(model, input_ids, attention_mask, logits_to_keep):
         std_grouped_rewards = std_grouped_rewards.repeat_interleave(self.num_generations, dim=0)
         advantages = (rewards - mean_grouped_rewards) / (std_grouped_rewards + 1e-4)
 
-        # x - x.detach() allows for preserving gradients from x
-        per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1)
-        per_token_loss = -(per_token_loss - self.beta * per_token_kl)
-        loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
-
         # Log the metrics
-        completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item()
-        self._metrics["completion_length"].append(completion_length)
-
         reward_per_func = self.accelerator.gather_for_metrics(rewards_per_func).mean(0)
         for i, reward_func in enumerate(self.reward_funcs):
             if isinstance(reward_func, PreTrainedModel):
@@ -539,15 +522,51 @@ def get_per_token_logps(model, input_ids, attention_mask, logits_to_keep):
             self._metrics[f"rewards/{reward_func_name}"].append(reward_per_func[i].item())
 
         self._metrics["reward"].append(self.accelerator.gather_for_metrics(rewards).mean().item())
-
         self._metrics["reward_std"].append(self.accelerator.gather_for_metrics(std_grouped_rewards).mean().item())
 
+        return {
+            "prompt_ids": prompt_ids,
+            "prompt_mask": prompt_mask,
+            "completion_ids": completion_ids,
+            "completion_mask": completion_mask,
+            "ref_per_token_logps": ref_per_token_logps,
+            "advantages": advantages,
+        }
+
+    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
+        if return_outputs:
+            raise ValueError("The GRPOTrainer does not support returning outputs")
+        # Compute the per-token log probabilities for the model
+
+        prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
+        completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"]
+        input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
+        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
+        logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
+
+        per_token_logps = self._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
+
+        # Compute the KL divergence between the model and the reference model
+        ref_per_token_logps = inputs["ref_per_token_logps"]
+        per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1
+
+        # x - x.detach() allows for preserving gradients from x
+        advantages = inputs["advantages"]
+        per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1)
+        per_token_loss = -(per_token_loss - self.beta * per_token_kl)
+        loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
+
+        # Log the metrics
+        completion_length = self.accelerator.gather_for_metrics(completion_mask.sum(1)).float().mean().item()
+        self._metrics["completion_length"].append(completion_length)
+
         mean_kl = ((per_token_kl * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
         self._metrics["kl"].append(self.accelerator.gather_for_metrics(mean_kl).mean().item())
 
         return loss
 
     def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: Optional[list[str]] = None):
+        inputs = self._prepare_inputs(inputs)
         with torch.no_grad():
             with self.compute_loss_context_manager():
                 loss = self.compute_loss(model, inputs)