fixing eval for SP (#2468)

setup.py

@@ -106,7 +106,11 @@ def get_package_version():
 
 extras_require = {
     "flash-attn": ["flash-attn==2.7.4.post1"],
-    "ring-flash-attn": ["ring-flash-attn>=0.1.4", "yunchang==0.6.0"],
+    "ring-flash-attn": [
+        "flash-attn==2.7.4.post1",
+        "ring-flash-attn>=0.1.4",
+        "yunchang==0.6.0",
+    ],
     "deepspeed": [
         "deepspeed==0.16.4",
         "deepspeed-kernels",

src/axolotl/core/trainers/base.py

@@ -8,12 +8,11 @@ import logging
 import os
 from collections import defaultdict
 from functools import wraps
-from typing import Any, Literal
+from typing import Literal
 
 import datasets
 import torch
 from datasets import Dataset
-from torch import nn
 from torch.utils.data import (
     BatchSampler,
     DataLoader,
@@ -593,27 +592,3 @@ class AxolotlTrainer(
         output_dir = os.path.join(run_dir, checkpoint_folder)
         os.makedirs(output_dir, exist_ok=True)
         return super()._save_checkpoint(model, trial, **kwargs)
-
-    def training_step(
-        self,
-        model: nn.Module,
-        inputs: dict[str, torch.Tensor | Any],
-        num_items_in_batch: int | None = None,
-    ) -> torch.Tensor:
-        """
-        Perform a training step on a batch of inputs. Overrides the
-        `transformers.trainer.Trainer` method to handle sequence parallelism if
-        enabled.
-
-        Args:
-            model: Model to perform training step for.
-            inputs: Dictionary mapping.
-        """
-        # Set up sequence parallelism for this step if enabled
-        if self.args.sequence_parallel_degree > 1:
-            self._update_ring_flash_attn_params(inputs)
-
-        # Proceed with normal training step
-        loss = super().training_step(model, inputs, num_items_in_batch)
-
-        return loss

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

@@ -7,6 +7,7 @@ import torch
 import torch.distributed as dist
 import torch.nn.functional as F
 from datasets import Dataset
+from torch import nn
 from torch.utils.data import DistributedSampler, Sampler
 
 from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
@@ -129,3 +130,53 @@ class SequenceParallelMixin:
         )
 
         update_ring_flash_attn_params(cu_seqlens, self.ring_attn_group)
+
+    def training_step(
+        self,
+        model: nn.Module,
+        inputs: dict[str, torch.Tensor | Any],
+        num_items_in_batch: int | None = None,
+    ) -> torch.Tensor:
+        """
+        Perform a training step on a batch of inputs. Overrides the
+        `transformers.trainer.Trainer` method to handle sequence parallelism if
+        enabled.
+
+        Args:
+            model: Model to perform training step for.
+            inputs: Dictionary mapping.
+        """
+        # Set up sequence parallelism for this step if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._update_ring_flash_attn_params(inputs)
+
+        # Proceed with normal training step
+        return super().training_step(model, inputs, num_items_in_batch)  # type: ignore
+
+    def prediction_step(
+        self,
+        model: nn.Module,
+        inputs: dict[str, torch.Tensor | Any],
+        prediction_loss_only: bool,
+        ignore_keys: list[str] | None = None,
+    ) -> tuple[torch.Tensor | None, torch.Tensor | None, torch.Tensor | None]:
+        """
+        Perform a prediction step on a batch of inputs. Overrides the
+        `transformers.trainer.Trainer` method to handle sequence parallelism if
+        enabled.
+
+        Args:
+            model: Model to perform prediction step for.
+            inputs: Dictionary mapping of inputs.
+            prediction_loss_only: Whether to return only the loss.
+            ignore_keys: Keys to ignore in the inputs.
+
+        Returns:
+            Tuple of (loss, logits, labels).
+        """
+        # Set up sequence parallelism for this prediction step if enabled
+        if self.args.sequence_parallel_degree > 1:
+            self._update_ring_flash_attn_params(inputs)
+
+        # Proceed with normal prediction step
+        return super().prediction_step(model, inputs, prediction_loss_only, ignore_keys)  # type: ignore