validate config to set defaults

Signed-off-by: Rahul Tuli <rtuli@redhat.com>

.github/workflows/main.yml

@@ -24,7 +24,7 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
-            axolotl_extras:
+            axolotl_extras: vllm
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"

.github/workflows/multi-gpu-e2e.yml

@@ -8,7 +8,6 @@ on:
       - 'setup.py'
       - 'pyproject.toml'
       - '.github/workflows/multi-gpu-e2e.yml'
-      - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
   workflow_dispatch:
   schedule:
     - cron: '0 0 * * 1,4'  # Runs at 00:00 UTC every monday & thursday
@@ -43,7 +42,7 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.5.1
-            axolotl_extras:
+            axolotl_extras: vllm
             num_gpus: 2
             nightly_build: "true"
           - cuda: 126

.github/workflows/tests.yml

@@ -258,12 +258,6 @@ jobs:
       fail-fast: false
       matrix:
         include:
-          - cuda: 124
-            cuda_version: 12.4.1
-            python_version: "3.11"
-            pytorch: 2.6.0
-            num_gpus: 1
-            axolotl_extras: llmcompressor
           - cuda: 124
             cuda_version: 12.4.1
             python_version: "3.11"
@@ -275,7 +269,7 @@ jobs:
             python_version: "3.11"
             pytorch: 2.5.1
             num_gpus: 1
-            axolotl_extras:
+            axolotl_extras: vllm
           - cuda: 126
             cuda_version: 12.6.3
             python_version: "3.11"

cicd/cicd.sh

@@ -52,4 +52,4 @@ pytest -v --durations=10 \
   --cov-append \
   --cov-report=xml:e2e-coverage.xml
 
-codecov upload-process -t $CODECOV_TOKEN -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION} || true
+codecov upload-process -t $CODECOV_TOKEN -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION}

cicd/multigpu.sh

@@ -20,4 +20,4 @@ pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/patched/ \
   --cov-report=xml:multigpu-coverage.xml
 
 # Upload coverage to Codecov
-codecov upload-process -t "${CODECOV_TOKEN}" -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION} || true
+codecov upload-process -t $CODECOV_TOKEN -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION}

codecov.yml

@@ -1,7 +1,5 @@
 codecov:
   require_ci_to_pass: yes
-  notify:
-    wait_for_ci: true
 
 coverage:
   precision: 2

docs/docker.qmd

@@ -28,8 +28,6 @@ main-base-py{python_version}-cu{cuda_version}-{pytorch_version}
 
 Tags examples:
 
-- `main-base-py3.11-cu128-2.7.0`
-- `main-base-py3.11-cu126-2.7.0`
 - `main-base-py3.11-cu124-2.6.0`
 - `main-base-py3.11-cu124-2.5.1`
 - `main-base-py3.11-cu124-2.4.1`
@@ -52,7 +50,7 @@ Link: [Docker Hub](https://hub.docker.com/r/axolotlai/axolotl)
 # on push to main
 main-py{python_version}-cu{cuda_version}-{pytorch_version}
 
-# latest main (currently torch 2.6.0, python 3.11, cuda 12.4)
+# latest main (currently torch 2.5.1, python 3.11, cuda 12.4)
 main-latest
 
 # nightly build
@@ -70,7 +68,6 @@ There may be some extra tags appended to the image, like `-vllm` which installs
 
 Tags examples:
 
-- `main-py3.11-cu126-2.7.0`
 - `main-py3.11-cu124-2.6.0`
 - `main-py3.11-cu124-2.5.1`
 - `main-py3.11-cu124-2.4.1`

examples/llama-4/scout-qlora-single-h100-flex.yaml

@@ -10,6 +10,7 @@ plugins:
 liger_glu_activation: true
 liger_rms_norm: true
 liger_layer_norm: true
+cut_cross_entropy: true
 
 llama4_linearized_experts: true  # needed with custom linearized experts model
 load_in_4bit: true

requirements.txt

@@ -11,13 +11,13 @@ liger-kernel==0.5.8
 
 packaging==23.2
 
-peft==0.15.2
+peft==0.15.1
 transformers==4.51.3
 tokenizers>=0.21.1
 accelerate==1.6.0
 datasets==3.5.0
 deepspeed>=0.15.4
-trl==0.17.0
+trl==0.16.1
 hf_xet==1.0.0
 hqq==0.2.5
 

setup.py

@@ -67,13 +67,13 @@ def parse_requirements(extras_require_map):
             if (major, minor) >= (2, 7):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 # _install_requires.append("xformers==0.0.29.post3")  # xformers seems to be hard pinned to 2.6.0
-                extras_require_map["vllm"] = ["vllm==0.8.4"]
+                extras_require_map["vllm"] = ["vllm==0.8.3"]
             elif (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append(
                     "xformers==0.0.29.post2"
                 )  # vllm needs post2 w torch 2.6
-                extras_require_map["vllm"] = ["vllm==0.8.4"]
+                extras_require_map["vllm"] = ["vllm==0.8.3"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:

src/axolotl/core/trainer_builder.py

@@ -932,6 +932,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = DataCollatorForSeq2Seq
 
         kwargs["return_tensors"] = "pt"
+        if issubclass(collator, DataCollatorForSeq2Seq):
+            kwargs["sequence_parallel_degree"] = training_args.sequence_parallel_degree
+            kwargs["ring_attn_func"] = training_args.ring_attn_func
 
         return collator(
             *collator_args,
@@ -1048,9 +1051,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
-        if self.cfg.use_wandb:
-            training_args_kwargs["run_name"] = self.cfg.wandb_name
-
         training_args_cls = None
         blocklist_args_kwargs = []
         if self.cfg.rl == "simpo":
@@ -1121,12 +1121,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             **training_args_kwargs,
         )
 
-        # unset run_name so wandb sets up experiment names
-        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
-            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
-                None
-            )
-
         return training_args
 
     def build(self, total_num_steps):

src/axolotl/core/trainers/base.py

@@ -371,15 +371,13 @@ class AxolotlTrainer(
                 num_items_in_batch=num_items_in_batch,
             )
 
-        loss = super().compute_loss(
+        return super().compute_loss(
             model,
             inputs,
             return_outputs=return_outputs,
             num_items_in_batch=num_items_in_batch,
         )
 
-        return loss
-
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
         concatenated_batch = {}

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

@@ -135,9 +135,7 @@ class GRPOStrategy:
         try:
             # use importlib to dynamically load the reward function from the module
             reward_func_module_name = reward_func_fqn.split(".")[-1]
-            reward_func_module = importlib.import_module(
+            reward_func_module = importlib.import_module(reward_func_fqn.split(".")[-2])
-                ".".join(reward_func_fqn.split(".")[:-1])
-            )
             reward_func = getattr(reward_func_module, reward_func_module_name)
             if not len(inspect.signature(reward_func).parameters) >= 2:
                 raise ValueError(

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

@@ -6,4 +6,4 @@
 from .optimizer import OptimizerMixin
 from .rng_state_loader import RngLoaderMixin
 from .scheduler import SchedulerMixin
-from .sequence_parallel import SequenceParallelContextManager, SequenceParallelMixin
+from .sequence_parallel import SequenceParallelMixin

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

@@ -1,86 +1,16 @@
-"""
+"""Module for Axolotl trainer sequence parallelism mixin"""
-Module for Axolotl trainer sequence parallelism mixin and training context manager
-"""
 
-import functools
 import logging
 
-import torch
 import torch.distributed as dist
 from datasets import Dataset
-from torch import nn
 from torch.utils.data import DistributedSampler, Sampler
-from torch.utils.hooks import RemovableHandle
 
-from axolotl.monkeypatch.attention.ring_attn import (
+from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
-    RingAttnFunc,
-    get_ring_attn_group,
-    update_ring_attn_params,
-)
 
 LOG = logging.getLogger(__name__)
 
 
-def apply_sequence_parallelism(
-    batch: dict[str, torch.Tensor],
-    local_rank: int,
-    local_world_size: int,
-    ring_attn_func: RingAttnFunc,
-) -> dict[str, torch.Tensor]:
-    """
-    Apply sequence parallelism slicing to a batch.
-
-    Args:
-        batch: Batch dictionary (e.g., input_ids, attention_mask, etc.)
-        local_rank: Local rank in the sequence parallel group
-        local_world_size: World size of the sequence parallel group
-        ring_attn_func: The ring attention function to use
-
-    Returns:
-        Sliced batch dictionary.
-    """
-    # Update ring attention params if needed
-    if batch.get("position_ids") is not None:
-        update_ring_attn_params(position_ids=batch["position_ids"])
-
-    # Slice batch for sequence parallel processing
-    total_seq_len = batch["input_ids"].size(1)
-    for key in batch:
-        if (
-            key in batch
-            and isinstance(batch[key], torch.Tensor)
-            and batch[key].dim() > 1
-            and batch[key].size(1) == total_seq_len
-        ):
-
-            if ring_attn_func in [
-                RingAttnFunc.VARLEN_LLAMA3,
-                RingAttnFunc.BATCH_RING,
-            ]:
-                # Split in sequential fashion and grab this rank's chunk
-                batch[key] = (
-                    batch[key].chunk(local_world_size, dim=1)[local_rank].contiguous()
-                )
-            elif ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-                chunks = batch[key].chunk(2 * local_world_size, dim=1)
-
-                # Take rank's chunk and opposing chunk for zigzag pattern
-                selected_chunks = [
-                    chunks[local_rank],
-                    chunks[2 * local_world_size - local_rank - 1],
-                ]
-                batch[key] = torch.cat(selected_chunks, dim=1).contiguous()
-            elif ring_attn_func is RingAttnFunc.BATCH_STRIPE:
-                # Split into striped data and stack
-                tensor = torch.stack(
-                    batch[key].split(local_world_size, dim=1),
-                    dim=1,
-                ).transpose(1, 2)
-                batch[key] = tensor[:, local_rank].contiguous()
-
-    return batch
-
-
 class SequenceParallelMixin:
     """
     Mixin class for sequence parallelism support in trainers.
@@ -157,157 +87,3 @@ class SequenceParallelMixin:
         return self._create_sequence_parallel_sampler(
             eval_dataset, shuffle=False, is_eval=True
         )
-
-
-class SequenceParallelContextManager:
-    """
-    Context manager for sequence parallelism operations.
-
-    This class provides a context that will automatically apply sequence parallelism
-    during model forward passes using a pre-forward hook, and gather outputs from
-    across the sequence parallelism group using a post-forward hook.
-    """
-
-    def __init__(
-        self,
-        model: nn.Module,
-        sequence_parallel_degree: int,
-        ring_attn_func: RingAttnFunc,
-    ):
-        self.model = model
-        self.sequence_parallel_degree = sequence_parallel_degree
-        self.ring_attn_func = ring_attn_func
-        self.process_group = get_ring_attn_group()
-
-        # Initialize sequence parallel group details
-        self.local_rank = dist.get_rank(self.process_group)
-        self.local_world_size = dist.get_world_size(self.process_group)
-
-        # Will store hook handles for removal
-        self.hook_handles: list[RemovableHandle] = []
-
-        # Create a partially applied version of the apply_sequence_parallelism function
-        # with pre-configured params
-        self.apply_sequence_parallelism = functools.partial(
-            apply_sequence_parallelism,
-            local_rank=self.local_rank,
-            local_world_size=self.local_world_size,
-            ring_attn_func=self.ring_attn_func,
-        )
-
-    def __enter__(self):
-        # Forward pre-hook to apply sequence parallelism
-        def sequence_parallel_pre_hook(_, args, kwargs):
-            # Apply sequence parallelism to kwargs
-            kwargs = self.apply_sequence_parallelism(batch=kwargs)
-            return args, kwargs
-
-        # Forward post-hook to gather outputs
-        def sequence_parallel_post_hook(_, __, output):
-            # Gather the sharded outputs
-            return self.gather_outputs(output)
-
-        # Register both hooks
-        self.hook_handles.append(
-            self.model.register_forward_pre_hook(
-                sequence_parallel_pre_hook, with_kwargs=True
-            )
-        )
-        self.hook_handles.append(
-            self.model.register_forward_hook(sequence_parallel_post_hook)
-        )
-
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        # Remove all hooks
-        for handle in self.hook_handles:
-            handle.remove()
-        self.hook_handles = []
-
-    def gather_outputs(self, output):
-        """Gather sharded outputs from all ranks and reconstruct the full tensor."""
-        # Handle different output formats (dict, tensor, etc.)
-        if isinstance(output, dict):
-            gathered_output = {}
-            for key, value in output.items():
-                if isinstance(value, torch.Tensor) and value.dim() > 1:
-                    # Gather logits or other sequence-sharded tensors
-                    gathered_value = self.gather_tensor(value)
-                    gathered_output[key] = gathered_value
-                else:
-                    gathered_value = value.clone()
-                    dist.all_reduce(
-                        gathered_value, op=dist.ReduceOp.SUM, group=self.process_group
-                    )
-                    gathered_output[key] = gathered_value
-            return gathered_output
-        if isinstance(output, torch.Tensor):
-            return self.gather_tensor(output)
-
-        return output
-
-    def gather_tensor(self, tensor):
-        """Gather a sharded tensor from all ranks."""
-        # Prepare tensors for all_gather
-        world_size = self.local_world_size
-
-        # Create list to store tensors from all ranks
-        gathered_tensors = [torch.zeros_like(tensor) for _ in range(world_size)]
-
-        # All-gather operation
-        dist.all_gather(gathered_tensors, tensor, group=self.process_group)
-
-        # Concatenate along sequence dimension (typically dim=1)
-        if self.ring_attn_func in [RingAttnFunc.VARLEN_LLAMA3, RingAttnFunc.BATCH_RING]:
-            # Simple concatenation for standard sharding
-            return torch.cat(gathered_tensors, dim=1)
-
-        if self.ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-            # Each rank has a pattern of (rank, world_size*2-rank-1)
-            reconstituted_tensors = [None] * (world_size * 2)
-
-            # First, split each gathered tensor into its two chunks
-            for rank, gathered_tensor in enumerate(gathered_tensors):
-                # Each tensor contains two chunks in the sequence dimension
-                chunk_size = gathered_tensor.size(1) // 2
-                chunk1, chunk2 = gathered_tensor.split(chunk_size, dim=1)
-
-                # Place chunks in their original positions
-                reconstituted_tensors[rank] = chunk1
-                reconstituted_tensors[world_size * 2 - rank - 1] = chunk2
-
-            # Concatenate the reconstituted tensors in the correct order
-            return torch.cat(reconstituted_tensors, dim=1)
-
-        # Otherwise, RingAttnFunc.BATCH_STRIPE
-        # In striping, each rank has every world_size-th slice
-        batch_size = tensor.size(0)
-        hidden_dim = tensor.size(-1)
-
-        # First, determine the full sequence length
-        total_seq_len = 0
-        for t in gathered_tensors:
-            total_seq_len += t.size(1)
-
-        # Create a tensor to hold the unstriped result
-        result = torch.zeros(
-            batch_size,
-            total_seq_len,
-            hidden_dim,
-            dtype=tensor.dtype,
-            device=tensor.device,
-        )
-
-        # For each rank's tensor, distribute its slices to the correct positions
-        for rank, gathered_tensor in enumerate(gathered_tensors):
-            # The rank's tensor contains every world_size-th slice
-            # starting from its rank position
-            seq_len = gathered_tensor.size(1)
-            for i in range(seq_len):
-                # Calculate the position in the full tensor
-                pos = i * world_size + rank
-                if pos < total_seq_len:
-                    result[:, pos] = gathered_tensor[:, i]
-
-        return result

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -27,6 +27,8 @@ pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transform
 ```yaml
 plugins:
   - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
+
+cut_cross_entropy: true
 ```
 
 ## Supported Models

src/axolotl/integrations/cut_cross_entropy/args.py

@@ -28,7 +28,7 @@ class CutCrossEntropyArgs(BaseModel):
     Input args for Cut Cross Entropy.
     """
 
-    cut_cross_entropy: Optional[bool] = True
+    cut_cross_entropy: Optional[bool] = None
 
     @model_validator(mode="before")
     @classmethod

src/axolotl/train.py

@@ -6,7 +6,6 @@ import os
 import signal
 import sys
 import weakref
-from contextlib import nullcontext
 from pathlib import Path
 from typing import Any, Dict
 
@@ -26,9 +25,6 @@ from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
-from axolotl.core.trainers.mixins.sequence_parallel import (
-    SequenceParallelContextManager,
-)
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
@@ -189,28 +185,16 @@ def execute_training(
         trainer: The configured trainer object.
         resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
     """
-    # Define the context managers to use
+    LOG.info("Starting trainer...")
-    flash_context = (
+    if cfg.flash_optimum:
-        torch.backends.cuda.sdp_kernel(
+        with torch.backends.cuda.sdp_kernel(
+            # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
             enable_flash=True,
             enable_math=True,
             enable_mem_efficient=True,
-        )
+        ):
-        if cfg.flash_optimum
+            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-        else nullcontext()
+    else:
-    )
-    sequence_parallel_context = (
-        SequenceParallelContextManager(
-            model=trainer.model,
-            sequence_parallel_degree=cfg.sequence_parallel_degree,
-            ring_attn_func=cfg.ring_attn_func,
-        )
-        if cfg.sequence_parallel_degree > 1
-        else nullcontext()
-    )
-
-    LOG.info("Starting trainer...")
-    with flash_context, sequence_parallel_context:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
 
@@ -287,19 +271,7 @@ def save_trained_model(
                 os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
             except FileNotFoundError:
                 pass
-    elif cfg.local_rank == 0:
+    elif hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
-        if cfg.flash_optimum and BetterTransformer:
-            model = BetterTransformer.reverse(model)
-
-        if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
-            trainer.model.save_pretrained(
-                cfg.output_dir, safe_serialization=safe_serialization
-            )
-
-        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
-
-    if hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
-        # TODO: add integration support so this can be implemented completely within the plugin
         from axolotl.integrations.llm_compressor.utils import (
             save_compressed_model,
         )
@@ -312,6 +284,17 @@ def save_trained_model(
             save_compressed=cfg.llmcompressor.save_compressed,
         )
 
+    elif cfg.local_rank == 0:
+        if cfg.flash_optimum and BetterTransformer:
+            model = BetterTransformer.reverse(model)
+
+        if cfg.rl and cfg.adapter and not cfg.rl_adapter_ref_model:
+            trainer.model.save_pretrained(
+                cfg.output_dir, safe_serialization=safe_serialization
+            )
+
+        model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
+
 
 def create_model_card(cfg: DictDefault, trainer: Trainer):
     """

src/axolotl/utils/collators/batching.py

@@ -1,12 +1,20 @@
-"""Data collators for axolotl to pad labels and position_ids for packed sequences"""
+"""
+Data collators for axolotl to pad labels and position_ids for packed sequences. Also
+includes logic for handling sequence parallelism collation.
+"""
 
 from dataclasses import dataclass
 from typing import Any
 
 import numpy as np
+import torch
+import torch.distributed as dist
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
+from axolotl.monkeypatch.attention.ring_attn import update_ring_attn_params
+from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
+
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -41,6 +49,8 @@ class DataCollatorForSeq2Seq:
             The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
         return_tensors (`str`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
+        sequence_parallel_degree (`int`):
+            The degree of sequence parallelism. Default to 1 for no sequence parallelism.
     """
 
     tokenizer: PreTrainedTokenizerBase
@@ -51,6 +61,17 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
+    sequence_parallel_degree: int = 1
+    ring_attn_func: RingAttnFunc | None = None
+
+    def __post_init__(self):
+        if self.sequence_parallel_degree > 1:
+            from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
+
+            # Get information about our position in the SP group
+            sp_group = get_ring_attn_group()
+            self.local_rank = dist.get_rank(group=sp_group)
+            self.local_world_size = dist.get_world_size(group=sp_group)
 
     def __call__(self, features, return_tensors=None):
         has_attn_mask = "attention_mask" in features[0].keys()
@@ -120,8 +141,62 @@ class DataCollatorForSeq2Seq:
             )
             features["decoder_input_ids"] = decoder_input_ids
 
+        if self.sequence_parallel_degree > 1:
+            features = self.apply_sequence_parallelism(features)
+
         return features
 
+    def apply_sequence_parallelism(
+        self, batch: dict[str, torch.Tensor]
+    ) -> torch.Tensor:
+        """
+        Apply sequence parallelism slicing to a batch.
+
+        Args:
+            batch: Batch dictionary from parent collator.
+
+        Returns:
+            Sliced batch dictionary.
+        """
+        # Get local (start, end) for sequence parallelism slicing
+        total_seq_len = batch["input_ids"].size(1)
+
+        # Update params for varlen ring attention calculation
+        if batch.get("position_ids") is not None:
+            update_ring_attn_params(position_ids=batch["position_ids"])
+
+        # Slice batch for sequence parallel processing
+        for key in batch:
+            if batch[key].size(1) == total_seq_len:
+                if self.ring_attn_func in [
+                    RingAttnFunc.VARLEN_LLAMA3,
+                    RingAttnFunc.BATCH_RING,
+                ]:
+                    batch[key] = (
+                        batch[key]
+                        .chunk(self.local_world_size, dim=1)[self.local_rank]
+                        .contiguous()
+                    )
+                elif self.ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
+                    chunks = batch[key].chunk(2 * self.local_world_size, dim=1)
+
+                    # Take rank's chunk and opposing chunk for zigzag pattern
+                    selected_chunks = [
+                        chunks[self.local_rank],
+                        chunks[2 * self.local_world_size - self.local_rank - 1],
+                    ]
+                    batch[key] = torch.cat(selected_chunks, dim=1).contiguous()
+                elif self.ring_attn_func is RingAttnFunc.BATCH_STRIPE:
+                    # TODO(djsaunde): This doesn't seem to work as expected
+                    # Split into striped data and stack
+                    tensor = torch.stack(
+                        batch[key].split(self.local_world_size, dim=1),
+                        dim=1,
+                    ).transpose(1, 2)
+                    batch[key] = tensor[:, self.local_rank].contiguous()
+
+        return batch
+
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):

src/axolotl/utils/config/__init__.py

@@ -126,6 +126,9 @@ def normalize_config(cfg):
             with open(ds_config_path, encoding="utf-8") as f:
                 cfg.deepspeed = json.load(f)
 
+    if cfg.sequence_parallel_degree is None:
+        cfg.sequence_parallel_degree = 1
+
     if cfg.saves_per_epoch:
         save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
         if save_steps < 1.0:  # prevent saves on every step

src/axolotl/utils/data/sft.py

@@ -134,9 +134,10 @@ def prepare_dataset(cfg, tokenizer, processor=None, preprocess_iterable=None):
                     "csv", data_files=f.name, split="train", streaming=True
                 )
         else:
-            iter_ds = load_dataset(
+            if is_local_main_process():
-                path, streaming=True, split=split, name=name, data_files=data_files
+                iter_ds = load_dataset(
-            )
+                    path, streaming=True, split=split, name=name, data_files=data_files
+                )
 
         if skip:
             LOG.info(f"Skipping {skip} samples from the dataset")

src/axolotl/utils/gradient_checkpointing/__init__.py

@@ -1,7 +1,5 @@
 """custom checkpointing utils"""
 
-from functools import partial
-
 from axolotl.utils.gradient_checkpointing.unsloth import (
     Unsloth_Offloaded_Gradient_Checkpointer,
 )
@@ -11,10 +9,6 @@ def hf_grad_checkpoint_offload_wrapper(
     decoder_layer, *args, use_reentrant=None
 ):  # pylint: disable=unused-argument
     return Unsloth_Offloaded_Gradient_Checkpointer.apply(
-        (
+        decoder_layer.__self__,
-            decoder_layer.func.__self__
-            if isinstance(decoder_layer, partial)
-            else decoder_layer.__self__
-        ),
         *args,
     )

src/axolotl/utils/schemas/config.py

@@ -1149,17 +1149,22 @@ class AxolotlInputConfig(
 
         return data
 
-    @model_validator(mode="after")
+    @field_validator("sequence_parallel_degree", mode="after")
-    def check_sequence_parallel_degree(self):
+    @classmethod
-        if not self.sequence_parallel_degree:
+    def check_sequence_parallel_degree(cls, value, info):
-            self.sequence_parallel_degree = 1
+        if not value:
-        elif self.sequence_parallel_degree > 1:
+            value = 1
-            if not self.flash_attention:
+
+        if value > 1:
+            if not info.data.get("flash_attention"):
                 raise ValueError(
                     "flash_attention: true must be set with sequence_parallel_degree > 1"
                 )
 
-            if self.sample_packing and self.micro_batch_size > 1:
+            if (
+                info.data.get("sample_packing")
+                and not info.data["micro_batch_size"] == 1
+            ):
                 raise ValueError(
                     "micro_batch_size must be set to 1 when sample_packing is enabled"
                     "due to a `ring-flash-attn` requirement"
@@ -1179,40 +1184,42 @@ class AxolotlInputConfig(
             # according to the proportion of non-padding tokens per rank.
             LOG.warning(
                 "Sequence parallelism (SP) is enabled with "
-                f"sequence_parallel_degree={self.sequence_parallel_degree}. "
+                f"sequence_parallel_degree={value}. Please note that logged losses may "
-                "Please note that logged losses may differ slightly to the non-SP "
+                "differ slightly to the non-SP losses due to transformers Trainer "
-                "losses due to transformers Trainer implementation details. "
+                "implementation details. Please see "
-                "Please see https://github.com/axolotl-ai-cloud/axolotl/pull/2495#issuecomment-2784022042 "
+                "https://github.com/axolotl-ai-cloud/axolotl/pull/2495#issuecomment-2784022042 "
                 "for more details."
             )
 
-        return self
+        return value
 
-    @model_validator(mode="after")
+    @field_validator("ring_attn_func", mode="after")
-    def validate_ring_attn_func(self):
+    @classmethod
-        if getattr(self, "sequence_parallel_degree", 1) == 1:
+    def check_ring_attn_func(cls, value, info):
-            return self
+        if not info.data.get("sequence_parallel_degree", 1) > 1:
+            return value
 
         from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
 
-        if self.ring_attn_func is not None:
+        if value is not None:
+            # Set the ring attention function if passed in config
             valid_funcs = list(RingAttnFunc)
-            if self.ring_attn_func in valid_funcs:
+            if value in valid_funcs:
-                self.ring_attn_func = RingAttnFunc(self.ring_attn_func)
+                value = RingAttnFunc(value)
             else:
                 raise ValueError(
-                    f"ring_attn_func: {self.ring_attn_func} must be in {valid_funcs}"
+                    f"ring_attn_func: {value} must be one of {valid_funcs}"
                 )
         else:
             # Default ring attention function selection
-            sample_packing = getattr(self, "sample_packing", False)
+            sample_packing = info.data.get("sample_packing")
-            self.ring_attn_func = (
+            value = (
                 RingAttnFunc.VARLEN_LLAMA3
                 if sample_packing
                 else RingAttnFunc.BATCH_RING
             )
 
-        return self
+        return value
 
     @model_validator(mode="before")
     @classmethod

src/axolotl/utils/trainer.py

@@ -348,7 +348,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                     load_from_cache_file=not cfg.is_preprocess,
                     desc="Add position_id column (PoSE)",
                 )
-    elif cfg.sample_packing:
+    elif cfg.sample_packing or cfg.sequence_parallel_degree > 1:
         drop_long_kwargs = {}
         if filter_map_kwargs:
             drop_long_kwargs["desc"] = "Add position_id column (Sample Packing)"
@@ -358,7 +358,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
             **filter_map_kwargs,
             **drop_long_kwargs,
         )
-        if cfg.eval_sample_packing:
+        if cfg.eval_sample_packing or cfg.sequence_parallel_degree > 1:
             if eval_dataset:
                 eval_dataset = eval_dataset.map(
                     add_position_ids,
@@ -528,13 +528,6 @@ def setup_torch_compile_env(cfg):
 def setup_deepspeed_env(cfg, stage=None):
     from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
 
-    from axolotl.utils.distributed import distributed_state
-
-    if distributed_state and distributed_state.initialized:
-        raise RuntimeError(
-            "Distributed State already initialized before Deepspeed setup"
-        )
-
     os.environ["ACCELERATE_USE_DEEPSPEED"] = "true"
     os.environ["ACCELERATE_DEEPSPEED_CONFIG_FILE"] = cfg.deepspeed
     if stage:

tests/e2e/integrations/test_llm_compressor.py

@@ -12,11 +12,7 @@ from axolotl.train import train
 from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
 from axolotl.utils.dict import DictDefault
 
-from tests.e2e.utils import (
+from tests.e2e.utils import check_model_output_exists, require_torch_2_4_1
-    check_model_output_exists,
-    require_llmcompressor,
-    require_torch_2_4_1,
-)
 
 MODELS = [
     "nm-testing/llama2.c-stories42M-pruned2.4-compressed",
@@ -30,7 +26,6 @@ MODELS = [
 @pytest.mark.parametrize(
     "save_compressed", [True, False], ids=["save_compressed", "save_uncompressed"]
 )
-@require_llmcompressor
 class TestLLMCompressorIntegration:
     """
     e2e tests for axolotl.integrations.llm_compressor.LLMCompressorPlugin
@@ -95,9 +90,12 @@ class TestLLMCompressorIntegration:
 
 
 def _check_llmcompressor_model_outputs(temp_dir, save_compressed):
-    if save_compressed:
-        assert (Path(temp_dir) / "recipe.yaml").exists()
 
+    # recipe.yaml should exist
+    assert (Path(temp_dir) / "recipe.yaml").exists()
+
+    # sparsity config exists if save_compressed
+    if save_compressed:
         from compressed_tensors import ModelCompressor
         from compressed_tensors.config import Sparse24BitMaskConfig
 

tests/e2e/multigpu/solo/test_grpo.py

@@ -4,14 +4,11 @@ GRPO test suite
 
 import os
 import random
-import shutil
 import subprocess  # nosec B404
 import sys
-import tempfile
 import time
 from pathlib import Path
 
-import psutil
 import pytest
 import requests
 import yaml
@@ -24,8 +21,8 @@ from tests.e2e.utils import require_vllm
 
 
 def start_vllm(
-    model: str, env: dict, wait: int | None = None, quiet=False, **kwargs
+    model: str, env: dict | None = None, wait: int | None = None, quiet=False, **kwargs
-) -> subprocess.Popen:
+) -> int:
     """
     helper function to start the VLLM server in the background, mostly for testing purposes
     """
@@ -49,41 +46,10 @@ def start_vllm(
     # print out the command to be executed
     print(" ".join(cmd))
 
-    vllm_logging_json = Path(tempfile.mkdtemp()) / "vllm_logging.json"
-    with open(vllm_logging_json, "w", encoding="utf-8") as temp_file:
-        temp_file.write(
-            """{
-  "formatters": {
-    "json": {
-      "class": "pythonjsonlogger.jsonlogger.JsonFormatter"
-    }
-  },
-  "handlers": {
-    "file": {
-      "class": "logging.FileHandler",
-      "formatter": "json",
-      "level": "DEBUG",
-      "filename": "/tmp/vllm.log",
-      "mode": "a"
-    }
-  },
-  "loggers": {
-    "vllm": {
-      "handlers": ["file"],
-      "level": "DEBUG",
-      "propagate": false
-    }
-  },
-  "version": 1
-}"""
-        )
-
-    cmd_env = env.copy()
-    cmd_env.update({"VLLM_LOGGING_CONFIG_PATH": vllm_logging_json})
     # start `trl vllm-serve` command in the background and capture the process id
     process = subprocess.Popen(  # pylint: disable=consider-using-with
         cmd,
-        env=cmd_env,
+        env=env,
         stdout=subprocess.DEVNULL if quiet else subprocess.PIPE,
         stderr=subprocess.DEVNULL if quiet else subprocess.PIPE,
     )  # nosec B603
@@ -92,51 +58,32 @@ def start_vllm(
     print(f"VLLM server process started (PID: {process.pid})")
 
     # wait until the http server is ready, even if it 404s, but timeout after 60 seconds
-    period_seconds = 5
     started = False
     if wait and host and port:
-        for i in range(0, int(wait), period_seconds):
+        for _ in range(int(wait)):
             try:
                 response = requests.get(f"http://{host}:{port}", timeout=1)
-                print(f"{i}: VLLM server (status: {response.status_code})")
                 if int(response.status_code) in [200, 404]:
                     started = True
                     break
-            except requests.exceptions.RequestException as exc:
+            except requests.exceptions.RequestException:
-                print(f"{i}: VLLM server failed to start: {str(exc)}")
+                pass
 
             # also check if the process.pid is still running
             if not process.poll() is None:
                 break
 
-            time.sleep(period_seconds)
+            time.sleep(1)
 
     if wait and not started:
         print(
             f"VLLM server process did not start within {wait} seconds. Please check your server logs."
         )
-        recursive_kill(process)
+        process.kill()
-        with open("/tmp/vllm.log", "r", encoding="utf-8") as log_file:
-            print(log_file.read())
-        shutil.rmtree("/tmp/vllm.log")
         raise RuntimeError(f"VLLM server process did not start within {wait} seconds.")
 
-    # return the process
+    # return the process id
-    return process
+    return process.pid
-
-
-def recursive_kill(process: subprocess.Popen):
-    """
-    Recursively kill a process and its children
-    """
-    process = psutil.Process(process.pid)
-    for child in psutil.Process(process.pid).children(recursive=True):
-        child.terminate()
-        child.kill()
-        os.kill(child.pid, 9)
-    process.terminate()
-    process.kill()
-    os.kill(process.pid, 9)
 
 
 class TestGRPO:
@@ -227,17 +174,16 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 
         current_env = os.environ.copy()
         env = {
-            "NCCL_P2P_LEVEL": "NVL",
+            "NCCL_P2P_LEVEL": "LOC",
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_DISABLE_COMPILE_CACHE": "1",
+            "VLLM_USE_V1": "0",
-            # "VLLM_USE_V1": "0",
         }
-        vllm_process = start_vllm(
+        vllm_process_id = start_vllm(
             cfg.base_model,
             env=env,
             quiet=True,
-            wait=300,
+            wait=120,
             gpu_memory_utilization=0.15,
             max_model_len=cfg.vllm.max_model_len,
             enable_prefix_caching=cfg.vllm.enable_prefix_caching,
@@ -256,14 +202,10 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                     "--main-process-port",
                     f"{get_torch_dist_unique_port()}",
                 ],
-                env={
+                env={"NCCL_P2P_LEVEL": "LOC", "NCCL_DEBUG": "INFO", **current_env},
-                    "NCCL_P2P_LEVEL": "NVL",
-                    "NCCL_DEBUG": "INFO",
-                    **current_env,
-                },
             )
         finally:
-            recursive_kill(vllm_process)
+            os.kill(vllm_process_id, 9)
 
     @pytest.mark.parametrize(
         "num_gpus",
@@ -320,17 +262,16 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
 
         current_env = os.environ.copy()
         env = {
-            "NCCL_P2P_LEVEL": "NVL",  # nccl can be brittle, assume P2P isn't reliable
+            "NCCL_P2P_LEVEL": "LOC",  # nccl can be brittle, assume P2P isn't reliable
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_DISABLE_COMPILE_CACHE": "1",
+            "VLLM_USE_V1": "0",
-            # "VLLM_USE_V1": "0",
         }
-        vllm_process = start_vllm(
+        vllm_process_id = start_vllm(
             cfg.base_model,
             env=env,
             quiet=True,
-            wait=300,
+            wait=120,
             gpu_memory_utilization=0.15,
             max_model_len=cfg.vllm.max_model_len,
             enable_prefix_caching=cfg.vllm.enable_prefix_caching,
@@ -349,11 +290,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                     "--main-process-port",
                     f"{get_torch_dist_unique_port()}",
                 ],
-                env={
+                env={"NCCL_P2P_LEVEL": "LOC", "NCCL_DEBUG": "INFO", **current_env},
-                    "NCCL_P2P_LEVEL": "NVL",
-                    "NCCL_DEBUG": "INFO",
-                    **current_env,
-                },
             )
         finally:
-            recursive_kill(vllm_process)
+            os.kill(vllm_process_id, 9)

tests/e2e/patched/test_activation_checkpointing.py

@@ -1,77 +0,0 @@
-"""
-E2E tests for activation checkpointing
-"""
-
-import pytest
-import transformers
-from torch.utils.checkpoint import checkpoint
-
-from axolotl.cli.args import TrainerCliArgs
-from axolotl.common.datasets import load_datasets
-from axolotl.train import train
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-from ..utils import check_model_output_exists
-
-
-@pytest.fixture()
-def fix_checkpoint_after_test():
-    yield
-    transformers.modeling_utils.checkpoint = checkpoint
-
-
-class TestActivationCheckpointing:
-    """
-    E2E tests for activation checkpointing
-    """
-
-    def test_activation_checkpointing_offload(
-        self,
-        temp_dir,
-        fix_checkpoint_after_test,  # pylint: disable=unused-argument,redefined-outer-name
-    ):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "sequence_len": 1024,
-                "val_set_size": 0.0,
-                "special_tokens": {
-                    "pad_token": "<|endoftext|>",
-                    "eos_token": "<|im_end|>",
-                },
-                "datasets": [
-                    {
-                        "chat_template": "chatml",
-                        "path": "mlabonne/FineTome-100k",
-                        "type": "chat_template",
-                        "split": "train[:10%]",
-                        "field_messages": "conversations",
-                        "message_field_role": "from",
-                        "message_field_content": "value",
-                    },
-                ],
-                "num_epochs": 1,
-                "max_steps": 5,
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "adamw_8bit",
-                "lr_scheduler": "cosine",
-                "flash_attention": True,
-                "sample_packing": True,
-                "bf16": True,
-                "save_safetensors": True,
-                "gradient_checkpointing": "offload",
-            }
-        )
-
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, dataset_meta=dataset_meta)
-        check_model_output_exists(temp_dir, cfg)

tests/e2e/patched/test_mixtral_samplepack.py

@@ -99,7 +99,6 @@ class TestMixtral(unittest.TestCase):
                 "bf16": "auto",
             }
         )
-        cfg = validate_config(cfg)
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

tests/e2e/patched/test_sp.py

@@ -2,19 +2,14 @@
 
 # pylint: disable=redefined-outer-name,unused-argument
 
-import functools
-import sys
 from unittest.mock import MagicMock, patch
 
 import pytest
 import torch
 from accelerate.state import PartialState
 
-from axolotl.core.trainers.mixins.sequence_parallel import apply_sequence_parallelism
 from axolotl.monkeypatch.attention.ring_attn import (
-    RingAttnFunc,
     get_ring_attn_group,
-    register_ring_attn,
     set_ring_attn_group,
 )
 from axolotl.utils.dict import DictDefault
@@ -52,27 +47,6 @@ def fixture_cfg():
     return cfg
 
 
-@pytest.fixture
-def sequence_parallel_batch():
-    """Create a test batch for sequence parallelism tests."""
-    batch_size = 1
-    seq_len = 8
-
-    # Create test tensors
-    input_ids = torch.arange(batch_size * seq_len).reshape(batch_size, seq_len)
-    attention_mask = torch.ones(batch_size, seq_len)
-    position_ids = torch.arange(seq_len).expand(batch_size, seq_len)
-
-    # Create test batch
-    batch = {
-        "input_ids": input_ids,
-        "attention_mask": attention_mask,
-        "position_ids": position_ids,
-    }
-
-    return batch
-
-
 class TestRingAttention:
     """Tests for the ring attention functionality."""
 
@@ -99,6 +73,11 @@ class TestRingAttention:
         self, mock_world_size, mock_rank, mock_new_group, partial_state
     ):
         """Test that ring attention groups are created correctly."""
+        from axolotl.monkeypatch.attention.ring_attn import (
+            RingAttnFunc,
+            register_ring_attn,
+        )
+
         # Setup mocks
         mock_world_size.return_value = 8  # 8 GPUs total
         mock_rank.return_value = 3  # GPU #3
@@ -122,303 +101,88 @@ class TestRingAttention:
         set_ring_attn_group(None)
 
 
-class TestConfigValidation:
+# Mock a simplified DataCollator test
-    """Tests for validating sequence parallelism configurations."""
+@patch("axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group")
+@patch("torch.distributed.get_rank")
+@patch("torch.distributed.get_world_size")
+def test_sequence_parallel_slicing(
+    mock_world_size, mock_rank, mock_get_group, partial_state
+):
+    """Test the basic sequence slicing logic without full collator instantiation."""
+    # Setup mocks
+    mock_get_group.return_value = MagicMock()
+    mock_rank.return_value = 1  # Second GPU
+    mock_world_size.return_value = 4  # 4 GPUs total
 
-    @pytest.fixture(autouse=True)
+    # Create a sample batch
-    def setup_mocks(self, monkeypatch):
+    batch = {
-        """Set up mocks for all tests in this class."""
+        "input_ids": torch.tensor(
-        # Mock the ring_flash_attn module
+            [
-        monkeypatch.setitem(sys.modules, "ring_flash_attn", MagicMock())
+                [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112],
+                [201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212],
+            ]
+        ),
+        "attention_mask": torch.ones(2, 12),
+    }
 
-    @pytest.fixture
+    # Simplified slicing logic from SequenceParallelDataCollator
-    def base_cfg(self):
+    def slice_batch(batch, rank, world_size):
-        """Create a base configuration for testing."""
+        result = {}
-        return DictDefault(
+        for key in batch:
-            {
+            seq_len = batch[key].shape[1]
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
+            slice_size = seq_len // world_size
-                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+            start_idx = rank * slice_size
-                "micro_batch_size": 1,
+            end_idx = start_idx + slice_size if rank < world_size - 1 else seq_len
-                "gradient_accumulation_steps": 1,
+            result[key] = batch[key][:, start_idx:end_idx]
-                "learning_rate": 1e-3,
+        return result
-                "output_dir": "./model-out",
-                "sequence_len": 512,
-                "special_tokens": {"pad_token": "<|endoftext|>"},
-            }
-        )
 
-    @pytest.mark.parametrize(
+    # Slice the batch
-        "config_updates, expected_values, should_pass, error_msg",
+    result = slice_batch(
-        [
+        batch, rank=mock_rank.return_value, world_size=mock_world_size.return_value
-            # Valid configuration
-            (
-                {"sequence_parallel_degree": 2, "flash_attention": True},
-                {"sequence_parallel_degree": 2, "flash_attention": True},
-                True,
-                None,
-            ),
-            # Default sequence_parallel_degree
-            ({}, {"sequence_parallel_degree": 1}, True, None),
-            # Invalid: sequence_parallel_degree > 1 without flash_attention
-            (
-                {"sequence_parallel_degree": 2, "flash_attention": False},
-                None,
-                False,
-                "flash_attention: true must be set",
-            ),
-            # Invalid: sequence_parallel_degree > 1 with sample_packing and micro_batch_size > 1
-            (
-                {
-                    "sequence_parallel_degree": 2,
-                    "flash_attention": True,
-                    "sample_packing": True,
-                    "micro_batch_size": 2,
-                    "pad_to_sequence_len": True,
-                },
-                None,
-                False,
-                "micro_batch_size must be set to 1",
-            ),
-        ],
-        ids=[
-            "valid_config",
-            "default_sp_degree",
-            "without_flash_attention",
-            "sample_packing_with_large_batch",
-        ],
     )
-    def test_sequence_parallel_config_validation(
-        self, base_cfg, config_updates, expected_values, should_pass, error_msg
-    ):
-        """Test various sequence parallelism configuration scenarios."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
 
-        # Apply updates to base config
+    # Check slicing
-        cfg = base_cfg
+    assert result["input_ids"].shape == (2, 3)  # 12 tokens / 4 GPUs = 3 tokens per GPU
-        cfg.update(config_updates)
+    expected_input_ids = torch.tensor(
-
-        if should_pass:
-            # Should validate without errors
-            config = AxolotlInputConfig(**cfg)
-
-            # Check expected values
-            for key, value in expected_values.items():
-                assert getattr(config, key) == value
-        else:
-            # Should raise exception
-            with pytest.raises(ValueError) as excinfo:
-                AxolotlInputConfig(**cfg)
-            assert error_msg in str(excinfo.value)
-
-    @pytest.mark.parametrize(
-        "ring_attn_func, sample_packing, expected_func",
         [
-            (None, True, RingAttnFunc.VARLEN_LLAMA3),
+            [104, 105, 106],  # Second slice of first sequence
-            (None, False, RingAttnFunc.BATCH_RING),
+            [204, 205, 206],  # Second slice of second sequence
-        ],
+        ]
-        ids=["default_with_sample_packing", "default_without_sample_packing"],
     )
-    def test_ring_attn_func_validation(
+    assert torch.all(result["input_ids"] == expected_input_ids)
-        self, base_cfg, ring_attn_func, sample_packing, expected_func
-    ):
-        """Test ring_attn_func validation and defaults."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
-
-        # Apply updates to base config
-        cfg = base_cfg | {
-            "sequence_parallel_degree": 2,
-            "flash_attention": True,
-            "sample_packing": sample_packing,
-        }
-
-        if ring_attn_func is not None:
-            cfg["ring_attn_func"] = ring_attn_func
-
-        # Should validate without errors
-        config = AxolotlInputConfig(**cfg)
-
-        # Check ring_attn_func value
-        assert config.ring_attn_func.value == expected_func
-
-    def test_invalid_ring_attn_func(self, base_cfg):
-        """Test that an invalid ring_attn_func is rejected."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
-
-        # Invalid configuration with invalid ring_attn_func
-        cfg = base_cfg | {
-            "sequence_parallel_degree": 2,
-            "flash_attention": True,
-            "ring_attn_func": "INVALID_FUNC",
-        }
-
-        # Should raise ValidationError
-        with pytest.raises(ValueError) as excinfo:
-            AxolotlInputConfig(**cfg)
-
-        # Verify error message
-        assert "ring_attn_func: INVALID_FUNC must be in" in str(excinfo.value)
 
 
-class TestApplySequenceParallelism:
+@patch.dict("sys.modules", {"ring_flash_attn": MagicMock()})
-    """Tests for the apply_sequence_parallelism function."""
+def test_config_validation_with_valid_inputs(cfg):
+    """Test that valid sequence parallelism configurations pass validation."""
+    # Import the actual model class with appropriate mocks
+    from axolotl.utils.schemas.config import AxolotlInputConfig
 
-    @pytest.fixture(autouse=True)
+    # Valid configuration: sequence_parallel_degree > 1 and flash_attention is True
-    def mock_distributed(self, monkeypatch):
+    cfg = cfg | {
-        """Mock torch.distributed functions for testing."""
+        "sequence_parallel_degree": 2,
-        # Mock is_initialized to return True
+        "flash_attention": True,
-        monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
+    }
 
-        # Mock get_rank to return 0 by default
+    # Should validate without errors
-        monkeypatch.setattr(torch.distributed, "get_rank", lambda *args, **kwargs: 0)
+    config = AxolotlInputConfig(**cfg)
+    assert config.sequence_parallel_degree == 2
+    assert config.flash_attention is True
 
-        # Mock get_world_size to return 2 by default
-        monkeypatch.setattr(
-            torch.distributed, "get_world_size", lambda *args, **kwargs: 2
-        )
 
-        # Mock the process group
+def test_config_validation_with_invalid_inputs(cfg):
-        monkeypatch.setattr(
+    """Test that invalid sequence parallelism configurations fail validation."""
-            "axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group",
+    from axolotl.utils.schemas.config import AxolotlInputConfig
-            MagicMock,
-        )
 
-        # Mock update_ring_attn_params
+    # Invalid configuration: sequence_parallel_degree > 1 but flash_attention is False
-        monkeypatch.setattr(
+    cfg = cfg | {
-            "axolotl.monkeypatch.attention.ring_attn.update_ring_attn_params",
+        "sequence_parallel_degree": 2,
-            lambda **kwargs: None,
+        "flash_attention": False,
-        )
+    }
 
-    def test_world_size_one(self, sequence_parallel_batch):
+    # Should raise ValidationError
-        """Test that function returns original batch when world size is 1."""
+    with pytest.raises(ValueError) as excinfo:
-        result = apply_sequence_parallelism(
+        AxolotlInputConfig(**cfg)
-            batch=sequence_parallel_batch,
-            local_rank=0,
-            local_world_size=1,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
 
-        # Should return the original batch unchanged
+    # Verify error message
-        assert result == sequence_parallel_batch
+    assert "flash_attention: true must be set" in str(excinfo.value)
-
-    def test_batch_ring_rank0(self, sequence_parallel_batch):
-        """Test BATCH_RING sharding for rank 0 in a 2-process group."""
-        batch = sequence_parallel_batch
-        seq_len = batch["input_ids"].size(1)
-
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Check that sequence dimension was sharded correctly
-        assert result["input_ids"].shape[1] == seq_len // 2
-        assert result["attention_mask"].shape[1] == seq_len // 2
-
-        # Verify content: rank 0 should get the first half of the sequence
-        assert torch.equal(result["input_ids"], batch["input_ids"][:, : seq_len // 2])
-        assert torch.equal(
-            result["position_ids"], batch["position_ids"][:, : seq_len // 2]
-        )
-
-    def test_batch_ring_rank1(self, sequence_parallel_batch):
-        """Test BATCH_RING sharding for rank 1 in a 2-process group."""
-        batch = sequence_parallel_batch
-        seq_len = batch["input_ids"].size(1)
-        original_input_ids = batch["input_ids"].clone()
-
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=1,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Verify content: rank 1 should get the second half of the sequence
-        assert torch.equal(result["input_ids"], original_input_ids[:, seq_len // 2 :])
-
-    def test_batch_zigzag(self, sequence_parallel_batch):
-        """Test BATCH_ZIGZAG sharding pattern."""
-        batch = sequence_parallel_batch
-        original_input_ids = batch["input_ids"].clone()
-        seq_len = batch["input_ids"].size(1)
-
-        # Test rank 0
-        result_rank0 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
-
-        # Test rank 1
-        result_rank1 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=1,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
-
-        # Checks for both ranks
-        assert result_rank0["input_ids"].shape[1] == seq_len // 2
-        assert result_rank1["input_ids"].shape[1] == seq_len // 2
-
-        # For a 2-rank system with 8 tokens, check specific zigzag pattern
-        # Rank 0 should get chunks [0, 1] and [6, 7]
-        # Rank 1 should get chunks [2, 3] and [4, 5]
-        if seq_len == 8:
-            # Create expected tensors for comparison
-            rank0_expected = torch.cat(
-                [original_input_ids[:, :2], original_input_ids[:, 6:8]], dim=1
-            )
-
-            rank1_expected = torch.cat(
-                [original_input_ids[:, 2:4], original_input_ids[:, 4:6]], dim=1
-            )
-
-            assert torch.equal(result_rank0["input_ids"], rank0_expected)
-            assert torch.equal(result_rank1["input_ids"], rank1_expected)
-
-    def test_partial_application(self, sequence_parallel_batch):
-        """Test that we can create a partially applied version of the function."""
-        batch = sequence_parallel_batch
-        original_input_ids = batch["input_ids"].clone()
-
-        # Create a partially applied function
-        rank0_ring_parallel = functools.partial(
-            apply_sequence_parallelism,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Use the partially applied function
-        result = rank0_ring_parallel(batch=batch)
-
-        # Verify it works as expected
-        assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2
-        assert torch.equal(
-            result["input_ids"],
-            original_input_ids[:, : original_input_ids.shape[1] // 2],
-        )
-
-    def test_missing_position_ids(self, sequence_parallel_batch):
-        """Test handling of batch without position_ids."""
-        # Create a batch without position_ids
-        batch = {
-            k: v for k, v in sequence_parallel_batch.items() if k != "position_ids"
-        }
-        original_input_ids = batch["input_ids"].clone()
-
-        # This should run without error even though position_ids is missing
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Verification should pass
-        assert "position_ids" not in result
-        assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2

tests/e2e/utils.py

@@ -109,24 +109,6 @@ def require_vllm(test_case):
     )(test_case)
 
 
-def require_llmcompressor(test_case):
-    """
-    Decorator marking a test that requires a llmcompressor to be installed
-    """
-
-    def is_llmcompressor_installed():
-        try:
-            import llmcompressor  # pylint: disable=unused-import  # noqa: F401
-
-            return True
-        except ImportError:
-            return False
-
-    return unittest.skipUnless(
-        is_llmcompressor_installed(), "test requires a llmcompressor to be installed"
-    )(test_case)
-
-
 def is_hopper():
     compute_capability = torch.cuda.get_device_capability()
     return compute_capability == (9, 0)