progress

src/axolotl/core/trainers/grpo/trainer.py

@@ -7,11 +7,11 @@ import torch.distributed as dist
 from accelerate.utils import is_deepspeed_available, is_peft_model
 from trl import GRPOTrainer
 from trl.extras.profiling import profiling_decorator
+from trl.trainer.utils import selective_log_softmax
 
 from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
 from axolotl.monkeypatch.attention.ring_attn import (
     get_ring_attn_group,
-    update_ring_attn_params,
 )
 
 if is_deepspeed_available():
@@ -72,6 +72,14 @@ class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
 
     # 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):
+        if dist.get_rank() == 0:
+            import ipdb; ipdb.set_trace()
+        dist.barrier()
+
+        if dist.get_rank() == 1:
+            import ipdb; ipdb.set_trace()
+        dist.barrier()
+
         if self.args.sequence_parallel_degree > 1:
             sp_group = get_ring_attn_group()
             self.local_rank = dist.get_rank(group=sp_group)
@@ -81,17 +89,10 @@ class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
             total_seq_len = input_ids.shape[1]
             remainder = total_seq_len % self.local_world_size
             if remainder != 0:
-                padding = self.local_world_size - remainder
-
-                if dist.get_rank() == 0:
-                    import ipdb
-
-                    ipdb.set_trace()
-                dist.barrier()
-
+                to_pad = self.local_world_size - remainder
                 pad_token_id = self.processing_class.pad_token_id or 0
                 padding = torch.full(
-                    (input_ids.shape[0], padding),
+                    (input_ids.shape[0], to_pad),
                     pad_token_id,
                     dtype=input_ids.dtype,
                     device=input_ids.device,
@@ -101,14 +102,14 @@ class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
                 # Also pad attention mask if it exists
                 if attention_mask is not None:
                     attn_padding = torch.zeros(
-                        (attention_mask.shape[0], padding),
+                        (attention_mask.shape[0], to_pad),
                         dtype=attention_mask.dtype,
                         device=attention_mask.device,
                     )
                     attention_mask = torch.cat([attention_mask, attn_padding], dim=1)
 
                 # Update total_seq_len after padding
-                total_seq_len += padding
+                total_seq_len += to_pad
 
             # Get local (start, end) for sequence parallelism slicing
             slice_size = total_seq_len // self.local_world_size
@@ -119,4 +120,71 @@ class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
             input_ids = input_ids[:, start:end]
             attention_mask = attention_mask[:, start:end]
 
-        super()._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)
+            # Calculate if this rank contains any tokens we need to keep
+            tokens_before_our_slice = self.local_rank * slice_size
+            print(f"{self.local_rank}: slice_size: {slice_size}")
+            print(f"{self.local_rank}: tokens_before_our_slice: {tokens_before_our_slice}")
+            if tokens_before_our_slice < logits_to_keep:
+                # How many tokens from our slice are needed
+                tokens_needed_from_slice = logits_to_keep - tokens_before_our_slice
+                logits_to_keep = min(slice_size, tokens_needed_from_slice)
+            else:
+                # This rank doesn't contain any tokens we need to keep
+                logits_to_keep = 0
+
+        print(f"{self.local_rank}: logits_to_keep: {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
+        logits = logits[:, :-1, :]  # (B, L-1, V), exclude the last logit: it corresponds to the next token pred
+
+        print(f"{self.local_rank}: logits.shape: {logits.shape}")
+
+        # First, let all ranks know the shape of each rank's tensor
+        local_shape = torch.tensor([logits.shape[0], logits.shape[1], logits.shape[2]], device=logits.device)
+        all_shapes = [torch.zeros_like(local_shape) for _ in range(self.local_world_size)]
+        dist.all_gather(all_shapes, local_shape, group=sp_group)
+
+        # Use a list-based approach to collect logits of different sizes
+        if self.local_rank == 0:
+            # Root process allocates space for receiving
+            gathered_logits = []
+            for shape in all_shapes:
+                b, s, v = shape.tolist()
+                gathered_logits.append(torch.zeros((b, s, v), dtype=logits.dtype, device=logits.device))
+        else:
+            gathered_logits = None
+            
+        # Gather to rank 0
+        dist.gather(logits, gathered_logits, dst=0, group=sp_group)
+        
+        # On rank 0, concatenate and distribute the result
+        if self.local_rank == 0:
+            concatenated_logits = torch.cat(gathered_logits, dim=1)
+            # Trim to keep only what we need
+            if concatenated_logits.shape[1] > logits_to_keep:
+                concatenated_logits = concatenated_logits[:, -logits_to_keep:, :]
+        else:
+            concatenated_logits = torch.zeros(
+                (logits.shape[0], logits_to_keep, logits.shape[2]),
+                dtype=logits.dtype, 
+                device=logits.device
+            )
+        
+        # Broadcast the result back to all ranks
+        dist.broadcast(concatenated_logits, src=0, group=sp_group)
+        logits = concatenated_logits
+
+        input_ids = input_ids[:, -logits_to_keep:]
+        # For transformers<=4.48, logits_to_keep argument isn't supported, so here we drop logits ourselves.
+        # See https://github.com/huggingface/trl/issues/2770
+        logits = logits[:, -logits_to_keep:]
+        # Divide logits by sampling temperature.
+        # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details
+        logits = logits / self.temperature
+
+        dist.barrier()
+
+        return selective_log_softmax(logits, input_ids)  # compute logprobs for the input tokens
+
+        # super()._get_per_token_logps(model, input_ids, attention_mask, logits_to_keep)

src/axolotl/core/trainers/mixins/sequence_parallel.py

@@ -4,6 +4,7 @@ Module for Axolotl trainer sequence parallelism mixin and training context manag
 
 import functools
 import logging
+from contextlib import contextmanager
 
 import torch
 import torch.distributed as dist
@@ -159,6 +160,7 @@ class SequenceParallelMixin:
         )
 
 
+<<<<<<< HEAD
 class SequenceParallelContextManager:
     """
     Context manager for sequence parallelism operations.
@@ -311,3 +313,40 @@ class SequenceParallelContextManager:
                     result[:, pos] = gathered_tensor[:, i]
 
         return result
+=======
+class SequenceParallelismManager:
+    def __init__(self, local_rank, local_world_size):
+        self.local_rank = local_rank
+        self.local_world_size = local_world_size
+        
+    @contextmanager
+    def apply(self, batch):
+        """
+        Context manager that applies sequence parallelism slicing to a batch,
+        and restores the original batch afterward if needed.
+        
+        Args:
+            batch: Batch dictionary from parent collator.
+            
+        Yields:
+            Sliced batch dictionary for use in the model.
+        """
+        # Get local (start, end) for sequence parallelism slicing
+        total_seq_len = batch["input_ids"].size(1)
+        slice_size = total_seq_len // self.local_world_size
+        start = self.local_rank * slice_size
+        end = start + slice_size
+        
+        # Update params for varlen ring attention calculation
+        if batch.get("position_ids") is not None:
+            update_ring_attn_params(
+                input_ids=batch["input_ids"], position_ids=batch["position_ids"]
+            )
+
+        # Slice batch for sequence parallel processing
+        for key in batch:
+            if isinstance(batch[key], torch.Tensor) and batch[key].size(1) == total_seq_len:
+                batch[key] = batch[key][:, start:end]
+
+        yield batch
+>>>>>>> c0054f07 (progress)