Generate: replace breaks by a loop condition (#29662)

* replace breaks by a loop condition

* Update src/transformers/generation/utils.py

Co-authored-by: amyeroberts <[email protected]>

---------

Co-authored-by: amyeroberts <[email protected]>

Author: gante

src/transformers/generation/utils.py

@@ -1778,6 +1778,24 @@ def typeerror():
 
         return result
 
+    def _has_unfinished_sequences(self, this_peer_finished: bool, synced_gpus: bool, device: torch.device) -> bool:
+        """
+        Returns whether there are still unfinished sequences in the device. The existence of unfinished sequences is
+        fed through `this_peer_finished`. ZeRO stage 3-friendly.
+        """
+        if synced_gpus:
+            # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+            # The following logic allows an early break if all peers finished generating their sequence
+            this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(device)
+            # send 0.0 if we finished, 1.0 otherwise
+            dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+            # did all peers finish? the reduced sum will be 0.0 then
+            if this_peer_finished_flag.item() == 0.0:
+                return False
+        elif this_peer_finished:
+            return False
+        return True
+
     def contrastive_search(self, *args, **kwargs):
         logger.warning_once(
             "Calling `contrastive_search` directly is deprecated and will be removed in v4.41. Use `generate` or a "
@@ -1939,19 +1957,9 @@ def _contrastive_search(
         unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
         model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
-        this_peer_finished = False  # used by synced_gpus only
-
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
+        this_peer_finished = False
 
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             # if the first step in the loop, encode all the prefix and obtain: (1) past_key_values;
             # (2) last_hidden_states; (3) logit_for_next_step; (4) update model kwargs for the next step
             if model_kwargs.get("past_key_values") is None:
@@ -2187,12 +2195,7 @@ def _contrastive_search(
 
             # stop when each sentence is finished
             unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
-
-            if unfinished_sequences.max() == 0:
-                this_peer_finished = True
-
-            if this_peer_finished and not synced_gpus:
-                break
+            this_peer_finished = unfinished_sequences.max() == 0
 
         if streamer is not None:
             streamer.end()
@@ -2395,6 +2398,7 @@ def _greedy_search(
             )
 
         # keep track of which sequences are already finished
+        this_peer_finished = False
         batch_size, cur_len = (
             model_kwargs["attention_mask"].shape
             if model_kwargs.get("attention_mask", None) is not None
@@ -2403,18 +2407,7 @@ def _greedy_search(
         unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
         model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
-        this_peer_finished = False  # used by synced_gpus only
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
-
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             # prepare model inputs
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
@@ -2480,13 +2473,7 @@ def _greedy_search(
                 )
 
             unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
-
-            # stop when each sentence is finished
-            if unfinished_sequences.max() == 0:
-                this_peer_finished = True
-
-            if this_peer_finished and not synced_gpus:
-                break
+            this_peer_finished = unfinished_sequences.max() == 0
 
         if streamer is not None:
             streamer.end()
@@ -2699,6 +2686,7 @@ def _sample(
             )
 
         # keep track of which sequences are already finished
+        this_peer_finished = False
         batch_size, cur_len = (
             model_kwargs["attention_mask"].shape
             if model_kwargs.get("attention_mask", None) is not None
@@ -2707,19 +2695,7 @@ def _sample(
         unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
         model_kwargs["cache_position"] = torch.arange(cur_len, device=input_ids.device)
 
-        this_peer_finished = False  # used by synced_gpus only
-        # auto-regressive generation
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
-
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             # prepare model inputs
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
@@ -2787,13 +2763,7 @@ def _sample(
                 )
 
             unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
-
-            # stop when each sentence is finished
-            if unfinished_sequences.max() == 0:
-                this_peer_finished = True
-
-            if this_peer_finished and not synced_gpus:
-                break
+            this_peer_finished = unfinished_sequences.max() == 0
 
         if streamer is not None:
             streamer.end()
@@ -3052,20 +3022,11 @@ def _beam_search(
         beam_scores[:, 1:] = -1e9
         beam_scores = beam_scores.view((batch_size * num_beams,))
 
-        this_peer_finished = False  # used by synced_gpus only
+        this_peer_finished = False
 
         decoder_prompt_len = input_ids.shape[-1]  # record the prompt length of decoder
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
 
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
             # if sequential is True, split the input to batches of batch_size and run sequentially
@@ -3192,10 +3153,7 @@ def _beam_search(
             cur_len = cur_len + 1
 
             if beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
-                if not synced_gpus:
-                    break
-                else:
-                    this_peer_finished = True
+                this_peer_finished = True
 
         sequence_outputs = beam_scorer.finalize(
             input_ids,
@@ -3441,20 +3399,10 @@ def _beam_sample(
         beam_scores = torch.zeros((batch_size, num_beams), dtype=torch.float, device=input_ids.device)
         beam_scores = beam_scores.view((batch_size * num_beams,))
 
-        this_peer_finished = False  # used by synced_gpus only
+        this_peer_finished = False
 
         decoder_prompt_len = input_ids.shape[-1]  # record the prompt length of decoder
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
-
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
             outputs = self(
@@ -3549,10 +3497,7 @@ def _beam_sample(
             cur_len = cur_len + 1
 
             if beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
-                if not synced_gpus:
-                    break
-                else:
-                    this_peer_finished = True
+                this_peer_finished = True
 
         sequence_outputs = beam_scorer.finalize(
             input_ids,
@@ -3804,20 +3749,10 @@ def _group_beam_search(
         beam_scores[:, ::num_sub_beams] = 0
         beam_scores = beam_scores.view((batch_size * num_beams,))
 
-        this_peer_finished = False  # used by synced_gpus only
+        this_peer_finished = False
 
         decoder_prompt_len = input_ids.shape[-1]  # record the prompt length of decoder
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
-
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             # predicted tokens in cur_len step
             current_tokens = torch.zeros(batch_size * num_beams, dtype=input_ids.dtype, device=device)
 
@@ -3955,10 +3890,7 @@ def _group_beam_search(
             cur_len = cur_len + 1
 
             if beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
-                if not synced_gpus:
-                    break
-                else:
-                    this_peer_finished = True
+                this_peer_finished = True
 
         final_beam_indices = sum(beam_indices, ()) if beam_indices is not None else None
         sequence_outputs = beam_scorer.finalize(
@@ -4213,20 +4145,10 @@ def _constrained_beam_search(
         beam_scores[:, 1:] = -1e9
         beam_scores = beam_scores.view((batch_size * num_beams,))
 
-        this_peer_finished = False  # used by synced_gpus only
+        this_peer_finished = False
 
         decoder_prompt_len = input_ids.shape[-1]  # record the prompt length of decoder
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
-
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
 
             outputs = self(
@@ -4320,10 +4242,7 @@ def _constrained_beam_search(
             cur_len = cur_len + 1
 
             if constrained_beam_scorer.is_done or all(stopping_criteria(input_ids, scores)):
-                if not synced_gpus:
-                    break
-                else:
-                    this_peer_finished = True
+                this_peer_finished = True
 
         sequence_outputs = constrained_beam_scorer.finalize(
             input_ids,
@@ -4553,18 +4472,8 @@ def _assisted_decoding(
         # other auxiliary variables
         max_len = stopping_criteria[0].max_length
 
-        this_peer_finished = False  # used by synced_gpus only
-        while True:
-            if synced_gpus:
-                # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
-                # The following logic allows an early break if all peers finished generating their sequence
-                this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
-                # send 0.0 if we finished, 1.0 otherwise
-                dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
-                # did all peers finish? the reduced sum will be 0.0 then
-                if this_peer_finished_flag.item() == 0.0:
-                    break
-
+        this_peer_finished = False
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
             cur_len = input_ids.shape[-1]
 
             #  1. Fetch candidate sequences from a `CandidateGenerator`
@@ -4733,13 +4642,7 @@ def _assisted_decoding(
                 )
 
             unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
-
-            # stop when each sentence is finished
-            if unfinished_sequences.max() == 0:
-                this_peer_finished = True
-
-            if this_peer_finished and not synced_gpus:
-                break
+            this_peer_finished = unfinished_sequences.max() == 0
 
         if streamer is not None:
             streamer.end()