bump transformers to 4.51.3

.coveragerc

@@ -1,14 +0,0 @@
-[run]
-source = axolotl
-omit =
-    */tests/*
-    setup.py
-
-[report]
-exclude_lines =
-    pragma: no cover
-    def __repr__
-    raise NotImplementedError
-    if __name__ == .__main__.:
-    pass
-    raise ImportError

.github/workflows/main.yml

@@ -29,7 +29,7 @@ jobs:
             cuda_version: 12.4.1
             python_version: "3.11"
             pytorch: 2.6.0
-            axolotl_extras: vllm
+            axolotl_extras:
             is_latest: true
     runs-on: axolotl-gpu-runner
     steps:

.github/workflows/tests.yml

@@ -102,16 +102,9 @@ jobs:
 
       - name: Run tests
         run: |
-          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/ --cov=axolotl --cov-report=xml
+          pytest -v -n8 --dist loadfile --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli/ tests/
-          pytest -v tests/patched/ --cov=axolotl --cov-append --cov-report=xml
+          pytest -v tests/patched/
-          pytest -v tests/cli/ --cov=axolotl --cov-append --cov-report=xml
+          pytest -v tests/cli/
-
-      - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v5
-        with:
-          files: ./coverage.xml
-          flags: unittests,pytorch-${{ matrix.pytorch_version }}
-          fail_ci_if_error: false
 
       - name: cleanup pip cache
         run: |

README.md

@@ -9,7 +9,6 @@
 <p align="center">
     <img src="https://img.shields.io/github/license/axolotl-ai-cloud/axolotl.svg?color=blue" alt="GitHub License">
     <img src="https://github.com/axolotl-ai-cloud/axolotl/actions/workflows/tests.yml/badge.svg" alt="tests">
-    <a href="https://codecov.io/gh/axolotl-ai-cloud/axolotl"><img src="https://codecov.io/gh/axolotl-ai-cloud/axolotl/branch/main/graph/badge.svg" alt="codecov"></a>
     <a href="https://github.com/axolotl-ai-cloud/axolotl/releases"><img src="https://img.shields.io/github/release/axolotl-ai-cloud/axolotl.svg" alt="Releases"></a>
     <br/>
     <a href="https://github.com/axolotl-ai-cloud/axolotl/graphs/contributors"><img src="https://img.shields.io/github/contributors-anon/axolotl-ai-cloud/axolotl?color=yellow&style=flat-square" alt="contributors" style="height: 20px;"></a>

cicd/cicd.sh

@@ -3,59 +3,10 @@ set -e
 
 python -c "import torch; assert '$PYTORCH_VERSION' in torch.__version__"
 
-# Run unit tests with initial coverage report
+pytest -v --durations=10 -n8 --ignore=tests/e2e/ --ignore=tests/patched/ --ignore=tests/cli /workspace/axolotl/tests/
-pytest -v --durations=10 -n8 \
+pytest -v --durations=10 /workspace/axolotl/tests/e2e/patched/lora_kernels  # running these with the other patches causes a failure
-  --ignore=tests/e2e/ \
+pytest -v --durations=10 --ignore=tests/e2e/patched/lora_kernels /workspace/axolotl/tests/e2e/patched
-  --ignore=tests/patched/ \
+pytest -v --durations=10 -n1 /workspace/axolotl/tests/e2e/solo/
-  --ignore=tests/cli \
+pytest -v --durations=10 /workspace/axolotl/tests/e2e/integrations/
-  /workspace/axolotl/tests/ \
+pytest -v --durations=10 /workspace/axolotl/tests/cli
-  --cov=axolotl \
+pytest -v --durations=10 --ignore=tests/e2e/solo/ --ignore=tests/e2e/patched/ --ignore=tests/e2e/multigpu/ --ignore=tests/e2e/integrations/ --ignore=tests/cli /workspace/axolotl/tests/e2e/
-  --cov-report=xml:coverage.xml
-
-# Run lora kernels tests with coverage append
-pytest -v --durations=10 \
-  /workspace/axolotl/tests/e2e/patched/lora_kernels \
-  --cov=axolotl \
-  --cov-append
-
-# Run patched tests excluding lora kernels with coverage append
-pytest -v --durations=10 \
-  --ignore=tests/e2e/patched/lora_kernels \
-  /workspace/axolotl/tests/e2e/patched \
-  --cov=axolotl \
-  --cov-append
-
-# Run solo tests with coverage append
-pytest -v --durations=10 -n1 \
-  /workspace/axolotl/tests/e2e/solo/ \
-  --cov=axolotl \
-  --cov-append
-
-# Run integration tests with coverage append
-pytest -v --durations=10 \
-  /workspace/axolotl/tests/e2e/integrations/ \
-  --cov=axolotl \
-  --cov-append
-
-pytest -v --durations=10 /workspace/axolotl/tests/cli \
-  --cov=axolotl \
-  --cov-append
-
-# Run remaining e2e tests with coverage append and final report
-pytest -v --durations=10 \
-  --ignore=tests/e2e/solo/ \
-  --ignore=tests/e2e/patched/ \
-  --ignore=tests/e2e/multigpu/ \
-  --ignore=tests/e2e/integrations/ \
-  --ignore=tests/cli \
-  /workspace/axolotl/tests/e2e/ \
-  --cov=axolotl \
-  --cov-append \
-  --cov-report=xml:coverage.xml
-
-# Upload coverage to Codecov
-if [ -f e2e-coverage.xml ]; then
-  codecov -f e2e-coverage.xml -F e2e,pytorch-${PYTORCH_VERSION}
-else
-  echo "Coverage file not found. Coverage report may have failed."
-fi

cicd/multigpu.sh

@@ -4,22 +4,3 @@ set -e
 # only run one test at a time so as not to OOM the GPU
 pytest -v  --durations=10 -n2 /workspace/axolotl/tests/e2e/multigpu/ --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
 pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/solo/
-
-# Only run two tests at a time to avoid OOM on GPU (with coverage collection)
-pytest -v -n2 \
-  --ignore=/workspace/axolotl/tests/e2e/multigpu/solo/
-  /workspace/axolotl/tests/e2e/multigpu/ \
-  --cov=axolotl \
-  --cov-report=xml:multigpu-coverage.xml
-
-pytest -v  --durations=10 -n1 /workspace/axolotl/tests/e2e/multigpu/solo/ \
-  --cov=axolotl \
-  --cov-append \
-  --cov-report=xml:multigpu-coverage.xml
-
-# Upload coverage to Codecov
-if [ -f multigpu-coverage.xml ]; then
-  codecov -f multigpu-coverage.xml -F multigpu,docker-tests,pytorch-${PYTORCH_VERSION}
-else
-  echo "Coverage file not found. Coverage report may have failed."
-fi

codecov.yml

@@ -1,51 +0,0 @@
-codecov:
-  require_ci_to_pass: yes
-
-coverage:
-  precision: 2
-  round: down
-  range: "70...100"
-  status:
-    project:
-      default:
-        # basic
-        target: auto
-        threshold: 0%
-        base: auto
-        # advanced
-        branches: null
-        if_no_uploads: error
-        if_not_found: success
-        if_ci_failed: error
-        only_pulls: false
-        flags: null
-        paths: null
-    patch:
-      default:
-        # basic
-        target: auto
-        threshold: 0%
-        base: auto
-        # advanced
-        branches: null
-        if_no_uploads: error
-        if_not_found: success
-        if_ci_failed: error
-        only_pulls: false
-        flags: null
-        paths: null
-
-parsers:
-  gcov:
-    branch_detection:
-      conditional: yes
-      loop: yes
-      method: no
-      macro: no
-
-comment:
-  layout: "reach,diff,flags,files,footer"
-  behavior: default
-  require_changes: no
-  require_base: no
-  require_head: yes

docs/config.qmd

@@ -693,9 +693,6 @@ sequence_parallel_degree:
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 # Must evenly divide the number of KV heads in your model.
 heads_k_stride: 1
-# One of "varlen_llama3", "batch_ring", "batch_zigzag", "batch_stripe". Defaults to "varlen_llama3"
-# in the sample packing case, and "batch_ring" in the non-sample packing case.
-ring_attn_func:
 
 # Path to torch distx for optim 'adamw_anyprecision'
 torchdistx_path:

docs/sequence_parallelism.qmd

@@ -27,9 +27,6 @@ To enable sequence parallelism, add the following to your configuration file:
 sequence_parallel_degree: 4  # Split sequences across 4 GPUs
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
-# Optional; one of "varlen_llama3", "batch_ring", "batch_zigzag", "batch_stripe". Defaults to
-# "varlen_llama3" when `sample_packing: true`, and "batch_ring" otherwise.
-ring_attn_func:
 ```
 
 The `sequence_parallel_degree` should be a divisor of the total number of GPUs. For example:

examples/llama-4/README.md

@@ -1,28 +1,16 @@
 # Llama 4 by Meta AI
 
-## Flash Attention vs Flex Attention
-
-While Flash Attention to support is "enabled" for Llama-4, the upstream implementation is not correct and usage of Flex Attention is recommended.
-
 ## Available Examples
 
 ### Llama 4 Scout 17Bx16Experts (109B)
+- [Multi-Modal/Vision QLoRA w/ FSDP1](./scout-vision-qlora-fsdp.yaml)
+- [Text Single GPU (H100) QLoRA](./scout-qlora-single-h100.yaml)
+- [Text Multi GPU QLoRA w/ FSDP1](./scout-qlora-fsdp1.yaml)
 
-Flex Attention
+Our Single H100 implementation for Llama 4 Scout uses only 68.5GB VRAM for post-training with 4k context length @ 546 tokens/second. [WandB logs here](https://wandb.ai/axolotl-ai/llama4-sft/runs/zic56rhd)
-- [Text Single GPU (H100) QLoRA](./scout-qlora-single-h100-flex.yaml)
-- [Text Multi GPU QLoRA w/ FSDP2](./scout-qlora-flexattn-fsdp2.yaml)
-
-[//]: # (Flash Attention (Do not use))
-
-[//]: # (- [Multi-Modal/Vision QLoRA w/ FSDP1](./scout-vision-qlora-fsdp.yaml))
-
-[//]: # (- [Text Single GPU (H100) QLoRA](./scout-qlora-single-h100.yaml))
-
-[//]: # (- [Text Multi GPU QLoRA w/ FSDP1](./scout-qlora-fsdp1.yaml))
-
-Our Single H100 implementation for Llama 4 Scout uses only 64.5GB VRAM for post-training with 4k context length @ 519 tokens/second. [WandB logs here](https://wandb.ai/axolotl-ai/llama4-flexattn-qlora/runs/wpie7dkj)
-Multi-GPU (4xH100) for Llama 4 Scout uses 62.8GB VRAM/GPU @ 4k contenxt length @ 280tps/gpu, [WandB logs here](https://wandb.ai/axolotl-ai/llama4-flexattn-qlora/runs/2lkezdj8)
 
 ### Llama 4 Maverick 17Bx128Experts (400B)
 
-Coming Soon
+- [Text Multi GPU QLoRA w/FSDP1](./maverick-qlora-fsdp1.yaml)
+
+Our 4xH100 implementation for Llama 4 Maverick uses 79.5GB VRAM/GPU for post-training with 4k context length @ 206 tokens/second. [WandB logs here.](https://wandb.ai/axolotl-ai/llama-sft/runs/siyvwuxc?nw=nwuserwinglian)

examples/llama-4/scout-qlora-flexattn-fsdp2.yaml

@@ -1,86 +0,0 @@
-base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
-model_type: Llama4ForConditionalGeneration
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_glu_activation: true
-liger_rms_norm: true
-liger_layer_norm: true
-
-llama4_linearized_experts: true
-load_in_4bit: true
-adapter: qlora
-lora_r: 32
-lora_alpha: 64
-lora_target_modules:
-  - self_attn.q_proj
-  - self_attn.k_proj
-  - self_attn.v_proj
-  - self_attn.o_proj
-  - shared_expert.gate_proj
-  - shared_expert.up_proj
-  - shared_expert.down_proj
-  # - experts.gate_projs.[0-9]+$
-  # - experts.up_projs.[0-9]+$
-  # - experts.down_projs.[0-9]+$
-lora_modules_to_save:
-  # - lm_head
-  # - embed_tokens
-
-chat_template: llama4
-datasets:
-  - path: mlabonne/FineTome-100k
-    type: chat_template
-    split: train[:20%]
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 4096
-sample_packing: true
-pad_to_sequence_len: true
-
-gradient_accumulation_steps: 1
-micro_batch_size: 2
-num_epochs: 3
-optimizer: adamw_torch_4bit
-lr_scheduler: cosine
-learning_rate: 1e-4
-
-bf16: true
-tf32: true
-
-logging_steps: 1
-flex_attention: true
-flex_attn_compile_kwargs:
-  dynamic: false
-  mode: max-autotune-no-cudagraphs
-
-warmup_steps: 10
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-fsdp:
-  - auto_wrap
-  - full_shard
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_reshard_after_forward: true
-  fsdp_activation_checkpointing: true
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot|>

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

@@ -1,85 +0,0 @@
-base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
-model_type: Llama4ForConditionalGeneration
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
-
-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
-adapter: qlora
-lora_r: 32
-lora_alpha: 64
-lora_target_modules:
-  - self_attn.q_proj
-  - self_attn.k_proj
-  - self_attn.v_proj
-  - self_attn.o_proj
-  - shared_expert.gate_proj
-  - shared_expert.up_proj
-  - shared_expert.down_proj
-  # - experts.gate_projs.[0-9]+$  # optionally train the moe experts
-  # - experts.up_projs.[0-9]+$
-  # - experts.down_projs.[0-9]+$
-lora_modules_to_save:
-  # - lm_head  # needed if modifying vocabulary
-  # - embed_tokens
-
-lora_mlp_kernel: true
-lora_qkv_kernel: true
-lora_o_kernel: true
-
-chat_template: llama4
-datasets:
-  - path: mlabonne/FineTome-100k
-    type: chat_template
-    split: train[:20%]
-    field_messages: conversations
-    message_property_mappings:
-      role: from
-      content: value
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-sequence_len: 4096  # up to 8k will work on a single H100
-sample_packing: true
-pad_to_sequence_len: true
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_4bit
-lr_scheduler: cosine
-learning_rate: 1e-4
-
-bf16: true
-tf32: true
-
-torch_compile: true
-flex_attention: true
-flex_attn_compile_kwargs:
-  dynamic: false
-  mode: max-autotune-no-cudagraphs
-
-gradient_checkpointing: offload
-gradient_checkpointing_kwargs:
-  use_reentrant: false
-
-logging_steps: 1
-warmup_steps: 20
-evals_per_epoch: 1
-saves_per_epoch: 1
-
-weight_decay: 0.0
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot|>

examples/llama-4/scout-vision-qlora-fsdp2-flex.yaml

@@ -1,89 +0,0 @@
-base_model: axolotl-quants/Llama-4-Scout-17B-16E-Linearized-bnb-nf4-bf16
-model_type: Llama4ForConditionalGeneration
-processor_type: Llama4Processor
-# Automatically upload checkpoint and final model to HF
-# hub_model_id: username/custom_model_name
-
-# these 3 lines are needed for now to handle vision chat templates w images
-skip_prepare_dataset: true
-remove_unused_columns: false
-sample_packing: false
-
-sequence_len: 4096
-
-plugins:
-  - axolotl.integrations.liger.LigerPlugin
-
-liger_glu_activation: true
-liger_rms_norm: true
-liger_layer_norm: true
-
-llama4_linearized_experts: true  # use Axolotl's customized model
-load_in_4bit: true
-adapter: qlora
-lora_r: 32
-lora_alpha: 64
-lora_target_modules:
-  - self_attn.q_proj
-  - self_attn.k_proj
-  - self_attn.v_proj
-  - self_attn.o_proj
-  - shared_expert.gate_proj
-  - shared_expert.up_proj
-  - shared_expert.down_proj
-  - vision_adapter.mlp.fc1
-  - vision_adapter.mlp.fc2
-  # - experts.gate_projs.[0-9]+$
-  # - experts.up_projs.[0-9]+$
-  # - experts.down_projs.[0-9]+$
-lora_modules_to_save:
-  - lm_head
-  - embed_tokens
-
-chat_template: llama4
-datasets:
-  - path: HuggingFaceH4/llava-instruct-mix-vsft
-    type: chat_template
-    split: train[:1%]
-    field_messages: messages
-
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.0
-output_dir: ./outputs/out
-
-gradient_accumulation_steps: 1
-micro_batch_size: 1
-num_epochs: 1
-optimizer: adamw_torch_4bit
-lr_scheduler: cosine
-learning_rate: 1e-4
-
-bf16: true
-tf32: true
-
-logging_steps: 1
-flex_attention: true
-flex_attn_compile_kwargs:
-  dynamic: false
-  mode: max-autotune-no-cudagraphs
-
-warmup_steps: 10
-evals_per_epoch: 1
-saves_per_epoch: 1
-weight_decay: 0.0
-fsdp:
-  - auto_wrap
-  - full_shard
-fsdp_config:
-  fsdp_version: 2
-  fsdp_offload_params: false
-  fsdp_cpu_ram_efficient_loading: true
-  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
-  fsdp_transformer_layer_cls_to_wrap: Llama4TextDecoderLayer
-  fsdp_state_dict_type: SHARDED_STATE_DICT
-  fsdp_sharding_strategy: FULL_SHARD
-  fsdp_reshard_after_forward: true
-  fsdp_activation_checkpointing: true
-special_tokens:
-  pad_token: <|finetune_right_pad_id|>
-  eos_token: <|eot|>

requirements-dev.txt

@@ -1,6 +1,6 @@
+pre-commit
 black
 mypy
-pre-commit
 types-requests
 quartodoc
 jupyter

requirements-tests.txt

@@ -1,7 +1,5 @@
-codecov
 pytest
-pytest-cov
+pytest-xdist
 pytest-retry
 pytest-sugar
-pytest-xdist
 tbparse

scripts/cutcrossentropy_install.py

@@ -25,5 +25,5 @@ if cce_spec:
 
 print(
     UNINSTALL_PREFIX
-    + 'pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438"'
+    + 'pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"'
 )

setup.py

@@ -67,7 +67,7 @@ def parse_requirements(extras_require_map):
             if (major, minor) >= (2, 6):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 _install_requires.append("xformers==0.0.29.post2")
-                extras_require_map["vllm"] = ["vllm==0.8.3"]
+                extras_require_map["vllm"] = ["vllm==0.8.1"]
             elif (major, minor) >= (2, 5):
                 _install_requires.pop(_install_requires.index(xformers_version))
                 if patch == 0:

src/axolotl/cli/delinearize_llama4.py

@@ -1,156 +0,0 @@
-"""
-CLI tool to delinearize quantized/Linearized Llama-4 models.
-"""
-
-import os
-from pathlib import Path
-from typing import Generator, Union
-
-import fire
-import torch
-from accelerate import init_empty_weights
-from dotenv import load_dotenv
-from transformers import AutoProcessor
-
-
-def iter_convert_patched_to_hf(model_state_dict, num_experts) -> Generator:
-    keys = list(model_state_dict.keys())
-    for key in keys:
-        if ".feed_forward.experts." not in key:
-            yield key, model_state_dict[key]
-        if ".feed_forward.experts.gate_projs" in key:
-            # gate gets fused with up so skip the yield on this and we'll fuse it when asking for the up
-            continue
-        if ".feed_forward.experts.up_projs" in key:
-            if ".feed_forward.experts.up_projs.0." in key:
-                # handle the re-shape and fusing of gate and up, and conversion from linear to parameter
-                prefix = key.split(".up_projs.0.")[0]
-                key = f"{prefix}.gate_up_proj"
-                # grab all the up_projs and gate_projs across all experts
-                gate_stacked = torch.stack(
-                    [
-                        model_state_dict[
-                            f"{prefix}.gate_projs.{expert_idx}.weight"
-                        ].transpose(0, 1)
-                        for expert_idx in range(num_experts)
-                    ]
-                )
-                up_stacked = torch.stack(
-                    [
-                        model_state_dict[
-                            f"{prefix}.up_projs.{expert_idx}.weight"
-                        ].transpose(0, 1)
-                        for expert_idx in range(num_experts)
-                    ]
-                )
-                gate_up_proj = torch.cat((gate_stacked, up_stacked), dim=-1)
-                del gate_stacked, up_stacked
-                yield key, gate_up_proj
-            else:
-                del model_state_dict[key]
-                continue
-        if ".feed_forward.experts.down_projs" in key:
-            if ".feed_forward.experts.down_projs.0." in key:
-                # handle the re-shape and fusing of gate and up, and conversion from linear to parameter
-                prefix = key.split(".down_projs.0.")[0]
-                key = f"{prefix}.down_proj"
-                # grab all the down_projs across all experts
-                down_stacked = torch.stack(
-                    [
-                        model_state_dict[
-                            f"{prefix}.down_projs.{expert_idx}.weight"
-                        ].transpose(0, 1)
-                        for expert_idx in range(num_experts)
-                    ]
-                )
-                yield key, down_stacked
-            else:
-                del model_state_dict[key]
-                continue
-
-
-def do_cli(model: Union[Path, str], output: Union[Path, str]) -> None:
-    """
-    Convert a patched HF format Llama4 model (with separated projections)
-    back to the original HF format (with fused projections).
-
-    Args:
-        model: Path to the patched HF model
-        output: Path to save the converted model
-    """
-    print(f"Loading model from {model}")
-    from axolotl.monkeypatch.models.llama4.modeling import (
-        patch_llama4_linearized_modeling,
-    )
-
-    unpatch_llama4 = patch_llama4_linearized_modeling()
-    from transformers import Llama4ForConditionalGeneration
-
-    model_ = Llama4ForConditionalGeneration.from_pretrained(
-        model, torch_dtype=torch.bfloat16
-    )
-    processor = AutoProcessor.from_pretrained(model)
-    processor.save_pretrained(output)
-
-    device = model_.device.type
-    if device == "cuda":
-        print(
-            f"peak memory allocated: {torch.cuda.max_memory_allocated() / 1024**2} MB"
-        )
-        print(f"peak memory reserved: {torch.cuda.max_memory_reserved() / 1024**2} MB")
-    model_config = model_.config
-    config = model_.config.get_text_config()
-
-    # Get key dimensions from the config
-    hidden_size = config.hidden_size
-    intermediate_size = config.intermediate_size
-    num_experts = config.num_local_experts
-
-    print(
-        f"Model dimensions: hidden_size={hidden_size}, intermediate_size={intermediate_size}, num_experts={num_experts}"
-    )
-
-    # Create output directory if it doesn't exist
-    os.makedirs(output, exist_ok=True)
-
-    # Get state dict
-    state_dict = model_.state_dict()
-    del model_
-
-    # Create a new state dict for the converted model
-    converted_state_dict = {}
-
-    # First, copy all keys that don't need modification
-    for key, value in iter_convert_patched_to_hf(state_dict, num_experts):
-        converted_state_dict[key] = value
-
-    del state_dict
-    if device == "cuda":
-        torch.cuda.empty_cache()
-        print("State dict converted.")
-        print(
-            f"peak memory allocated: {torch.cuda.max_memory_allocated() / 1024**2} MB"
-        )
-        print(f"peak memory reserved: {torch.cuda.max_memory_reserved() / 1024**2} MB")
-    # Ideally re-load the model import to load the converted state dict
-    # Save the converted model
-    with init_empty_weights():
-        unpatch_llama4()
-        model_ = Llama4ForConditionalGeneration(model_config)
-
-    if device == "cuda":
-        print("State dict loaded into model.")
-        print(
-            f"peak memory allocated: {torch.cuda.max_memory_allocated() / 1024**2} MB"
-        )
-        print(f"peak memory reserved: {torch.cuda.max_memory_reserved() / 1024**2} MB")
-    model_.load_state_dict(converted_state_dict, strict=False, assign=True)
-    print(f"Saving converted model to {output}...")
-    model_.save_pretrained(output)
-
-    print(f"Model successfully converted and saved to {output}")
-
-
-if __name__ == "__main__":
-    load_dotenv()
-    fire.Fire(do_cli)

src/axolotl/cli/main.py

@@ -330,15 +330,6 @@ def vllm_serve(config: str, **cli_args: VllmServeCliArgs):
     do_vllm_serve(config, cli_args)
 
 
-@cli.command()
-@click.argument("model", type=click.Path(exists=True, path_type=str))
-@click.argument("output", type=click.Path(exists=False, path_type=str))
-def delinearize_llama4(model: str, output: str) -> None:
-    from axolotl.cli.delinearize_llama4 import do_cli as do_delinearize_llama4
-
-    do_delinearize_llama4(model, output)
-
-
 cli.add_command(lm_eval)
 
 

src/axolotl/cli/merge_lora.py

@@ -40,7 +40,6 @@ def do_merge_lora(*, cfg: DictDefault) -> None:
         LOG.warning("Error raised: %s", e)
 
     model.generation_config.do_sample = True
-    model.config.use_cache = True
 
     if cfg.local_rank == 0:
         LOG.info(f"Saving merged model to: {str(Path(cfg.output_dir) / 'merged')}...")

src/axolotl/common/datasets.py

@@ -129,19 +129,17 @@ def load_preference_datasets(
         total_num_steps = None
 
     if cli_args.debug or cfg.debug:
-        if not cfg.rl == "grpo":
+        LOG.info("check_dataset_labels...")
-            LOG.info("check_dataset_labels...")
 
-            tokenizer = load_tokenizer(cfg)
+        tokenizer = load_tokenizer(cfg)
-            train_samples = sample_dataset(train_dataset, cli_args.debug_num_examples)
+        train_samples = sample_dataset(train_dataset, cli_args.debug_num_examples)
-
+        check_dataset_labels(
-            check_dataset_labels(
+            train_samples,
-                train_samples,
+            tokenizer,
-                tokenizer,
+            num_examples=cli_args.debug_num_examples,
-                num_examples=cli_args.debug_num_examples,
+            text_only=cli_args.debug_text_only,
-                text_only=cli_args.debug_text_only,
+            rl_mode=True,
-                rl_mode=True,
+        )
-            )
 
     return TrainDatasetMeta(
         train_dataset=train_dataset,

src/axolotl/core/trainer_builder.py

@@ -776,7 +776,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         training_arguments_kwargs["sequence_parallel_degree"] = (
             self.cfg.sequence_parallel_degree
         )
-        training_arguments_kwargs["ring_attn_func"] = self.cfg.ring_attn_func
 
         if self.cfg.reward_model:
             training_args_cls = AxolotlRewardConfig
@@ -934,7 +933,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         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,

src/axolotl/core/training_args.py

@@ -9,8 +9,6 @@ from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
-from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
-
 
 @dataclass
 class AxolotlTrainingMixins:
@@ -220,12 +218,6 @@ class AxolotlTrainingMixins:
         default=1,
         metadata={"help": "The number of workers to use in sequence parallelism"},
     )
-    ring_attn_func: Optional[RingAttnFunc] = field(
-        default=None,
-        metadata={
-            "help": "The ring-flash-attn function to use in sequence parallelism"
-        },
-    )
 
     # multi-modal section
 

src/axolotl/integrations/cut_cross_entropy/README.md

@@ -12,14 +12,12 @@ See https://github.com/apple/ml-cross-entropy
 
 Run the following command to install `cut_cross_entropy[transformers]` if you don't have it already.
 
-- If you are in dev environment
 ```bash
+# if you are in dev environment
 python scripts/cutcrossentropy_install.py | sh
-```
 
-- If you are installing from pip
+# if you are not in dev environment
-```bash
+pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"
-pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438"
 ```
 
 ## Usage

src/axolotl/integrations/cut_cross_entropy/__init__.py

@@ -33,7 +33,7 @@ LOG = logging.getLogger("axolotl.integrations.cut_cross_entropy")
 
 _CCE_INSTALL_MESSAGE = (
     "Please install cut_cross_entropy with transformers support using "
-    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@bad6f7b49c75fdec69471abb71b4cddd0f0c6438"`'
+    '`pip install "cut-cross-entropy[transformers] @ git+https://github.com/apple/ml-cross-entropy.git@24fbe4b5dab9a6c250a014573613c1890190536c"`'
 )
 
 

src/axolotl/integrations/cut_cross_entropy/monkeypatch/llama4.py

@@ -165,7 +165,7 @@ def cce_forward(
 )
 def cce_forward_multimodal(
     self,
-    input_ids: torch.LongTensor | None = None,  # type: ignore
+    input_ids: torch.LongTensor | None = None,
     pixel_values: torch.FloatTensor | None = None,
     attention_mask: Optional[torch.Tensor] = None,
     position_ids: Optional[torch.LongTensor] = None,
@@ -254,7 +254,7 @@ def cce_forward_multimodal(
         )
 
     if inputs_embeds is None:
-        inputs_embeds = self.get_input_embeddings()(input_ids)  # type: ignore
+        inputs_embeds = self.get_input_embeddings()(input_ids)
 
     if pixel_values is not None:
         image_features = self.get_image_features(
@@ -263,13 +263,13 @@ def cce_forward_multimodal(
             vision_feature_select_strategy=vision_feature_select_strategy,
             image_sizes=image_sizes,
         )
-        original_inputs_embeds_shape = inputs_embeds.shape  # type: ignore
+        original_inputs_embeds_shape = inputs_embeds.shape
 
         vision_flat = image_features.view(-1, image_features.size(-1))
         projected_vision_flat = self.multi_modal_projector(vision_flat)
 
         special_image_mask = (input_ids == self.config.image_token_index).unsqueeze(-1)
-        final_mask = special_image_mask.to(inputs_embeds.device)  # type: ignore
+        final_mask = special_image_mask.to(inputs_embeds.device)
         inputs_embeds = inputs_embeds.view(-1, inputs_embeds.size(-1))  # type: ignore
 
         final_mask_1d = final_mask[..., 0].reshape(-1)

src/axolotl/monkeypatch/accelerate/fsdp2.py

@@ -49,7 +49,7 @@ def fsdp2_load_full_state_dict(accelerator, model: torch.nn.Module, full_sd: dic
             )
             sharded_sd[param_name] = sharded_tensor
 
-    model.load_state_dict(sharded_sd, assign=True)
+    model.load_state_dict(sharded_sd)
 
 
 def patch_accelerate_fsdp_utils():

src/axolotl/monkeypatch/attention/flex_attn.py

@@ -7,11 +7,12 @@ import torch
 import transformers
 
 
-def patch_flex_wrapper(**flex_attn_compile_kwargs):
+def patch_flex_wrapper():
     # TODO remove this patch when transformers#37285 is merged and in a release
     is_torch_2_6 = torch.__version__.startswith("2.6")
+    is_transformers_below_4_51 = transformers.__version__ < "4.51.0"
 
-    if not is_torch_2_6:
+    if not (is_torch_2_6 and is_transformers_below_4_51):
         return
 
     from torch.nn.attention.flex_attention import flex_attention
@@ -31,24 +32,17 @@ def patch_flex_wrapper(**flex_attn_compile_kwargs):
                 cls._instance = super().__new__(cls)
             return cls._instance
 
-        @classmethod
-        def del_singleton(cls):
-            cls._instance = None
-
         @torch.compiler.disable(recursive=False)
-        def __init__(self, training):
+        def __init__(self):
             """
             Initialize or update the singleton instance.
             """
-            self.training = None
+            if not self._is_flex_compiled:
-            if not self._is_flex_compiled or training != self.training:
-                # In PyTorch 2.6.0, there's a known issue with flex attention compilation which may
-                # cause errors. The suggested fix is to compile with "max-autotune-no-cudagraphs"
-                # see https://github.com/pytorch/pytorch/issues/146260 for training
-                self.training = training
                 self._compiled_flex_attention = torch.compile(
                     flex_attention,
-                    **flex_attn_compile_kwargs,
+                    dynamic=False,
+                    mode="max-autotune-no-cudagraphs",
+                    fullgraph=True,
                 )
                 self._is_flex_compiled = True
 
@@ -56,22 +50,15 @@ def patch_flex_wrapper(**flex_attn_compile_kwargs):
             return self._compiled_flex_attention
 
     transformers.integrations.flex_attention.WrappedFlexAttention = WrappedFlexAttention
-    setattr(
-        sys.modules["transformers.integrations.flex_attention"],
-        "WrappedFlexAttention",
-        WrappedFlexAttention,
-    )
 
 
 def patch_flex_make_mask():
     is_torch_2_6 = torch.__version__.startswith("2.6")
+    is_transformers_eq_4_51 = transformers.__version__ == "4.51.0"
 
-    if not is_torch_2_6:
+    if not (is_torch_2_6 and is_transformers_eq_4_51):
         return
 
-    from torch.nn.attention.flex_attention import (
-        _DEFAULT_SPARSE_BLOCK_SIZE as flex_default_block_size,
-    )
     from torch.nn.attention.flex_attention import (
         BlockMask,
     )
@@ -117,16 +104,14 @@ def patch_flex_make_mask():
         if not query_length:
             query_length = total_seq_len
         attention_mask_2d = torch.nn.functional.pad(
-            attention_mask_2d,
+            attention_mask_2d, value=0, pad=(0, key_length)
-            value=0,
-            pad=(0, abs(total_seq_len - max(key_length, flex_default_block_size))),
         )
         device = attention_mask_2d.device
         document_ids = attention_mask_2d.clone()
 
         if attention_chunk_size is not None:
             # we create an arange, then we just // by chunk size to get [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
-            chunk_idxs = (document_ids.clone().fill_(1).cumsum(-1) - 1) // (
+            document_ids = (document_ids.fill_(1).cumsum(-1) - 1) // (
                 attention_chunk_size
             )
 
@@ -153,18 +138,6 @@ def patch_flex_make_mask():
             final_mask = causal_mask & padding_mask & document_mask
             return final_mask
 
-        def chunk_causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx):
-            """
-            Combines the chunk mask with the causal mask for chunked attention.
-            """
-            chunk_mask = chunk_idxs[batch_idx, q_idx] == chunk_idxs[batch_idx, kv_idx]
-            causal_doc_mask = causal_mask_mod(batch_idx, head_idx, q_idx, kv_idx)
-            return chunk_mask & causal_doc_mask
-
-        mask_mod_maybe_combined = (
-            causal_mask_mod if attention_chunk_size is None else chunk_causal_mask_mod
-        )
-
         if offsets is not None:
             q_offset = offsets[0]
             kv_offset = offsets[1]
@@ -172,10 +145,10 @@ def patch_flex_make_mask():
             def mask_mod(batch_idx, head_idx, q_idx, kv_idx):
                 offset_q = q_idx + q_offset
                 offset_kv = kv_idx + kv_offset
-                return mask_mod_maybe_combined(batch_idx, head_idx, offset_q, offset_kv)
+                return causal_mask_mod(batch_idx, head_idx, offset_q, offset_kv)
 
         else:
-            mask_mod = mask_mod_maybe_combined
+            mask_mod = causal_mask_mod
         return create_block_causal_mask_flex(
             mask_mod=mask_mod,
             B=batch_size,
@@ -187,16 +160,11 @@ def patch_flex_make_mask():
         )
 
     for n in tuple(sys.modules):
-        if ".modeling_" in n:
+        if ".modeling_" in n and "llama4" not in n:
             if hasattr(sys.modules[n], "make_flex_block_causal_mask"):
                 sys.modules[n].make_flex_block_causal_mask = (
                     patched_make_flex_block_causal_mask
                 )
-                setattr(
-                    sys.modules[n],
-                    "make_flex_block_causal_mask",
-                    patched_make_flex_block_causal_mask,
-                )
 
     transformers.integrations.flex_attention.make_flex_block_causal_mask = (
         patched_make_flex_block_causal_mask

src/axolotl/monkeypatch/attention/ring_attn.py

@@ -6,8 +6,6 @@ package, specifically the `hf_adapter.substitute_hf_flash_attn` function to patc
 their sequence parallel version of Flash Attention 2.
 """
 
-from enum import Enum
-
 import torch
 import torch.distributed as dist
 from accelerate.logging import get_logger
@@ -18,7 +16,6 @@ from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
 configure_logging()
 LOG = get_logger(__name__)
 
-
 RING_ATTN_GROUP = None
 
 
@@ -43,22 +40,7 @@ def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
     RING_ATTN_GROUP = ring_attn_group
 
 
-class RingAttnFunc(str, Enum):
+def register_ring_attn(sequence_parallel_degree: int, heads_k_stride: int | None):
-    """Enum class for supported `ring-flash-attn` implementations"""
-
-    # VARLEN_RING = "varlen_ring"
-    # VARLEN_ZIGZAG = "varlen_zigzag"
-    VARLEN_LLAMA3 = "varlen_llama3"
-    BATCH_RING = "batch_ring"
-    BATCH_ZIGZAG = "batch_zigzag"
-    BATCH_STRIPE = "batch_stripe"
-
-
-def register_ring_attn(
-    sequence_parallel_degree: int,
-    heads_k_stride: int | None,
-    ring_attn_func: RingAttnFunc | None,
-):
     """
     Create ring attention group and substitute flash attn with ring flash attn.
 
@@ -66,9 +48,6 @@ def register_ring_attn(
         sequence_parallel_degree: Sequence parallelism factor.
         heads_k_stride: Sequence parallelism K head stride size. Passed
             through to `ring_flash_attn.substitute_hf_flash_attn`.
-        ring_attn_func: `ring_flash_attn` ring attention implemention. If sample
-            packing is enabled, it must be a `varlen` function; otherwise, it must be a
-            `batch` function.
     """
     if get_ring_attn_group() is not None:
         LOG.info("Ring attention already registered, exiting early...")
@@ -79,9 +58,7 @@ def register_ring_attn(
         f"each sequence will be processed across {sequence_parallel_degree} GPUs"
     )
 
-    rank = dist.get_rank()
     world_size = dist.get_world_size()
-
     assert sequence_parallel_degree <= world_size, (
         f"sequence_parallel_degree ({sequence_parallel_degree}) "
         f"must be less than or equal to world_size ({world_size})"
@@ -91,8 +68,10 @@ def register_ring_attn(
         f"must evenly divide world_size ({world_size})"
     )
 
-    # Assign ranks to sequence parallel groups
+    # Detailed logging of group formation
+    rank = dist.get_rank()
     group_assignments = {}
+
     for i in range(world_size // sequence_parallel_degree):
         ring_attn_ranks = list(
             range(
@@ -113,37 +92,35 @@ def register_ring_attn(
     if rank == 0:
         LOG.info(f"Sequence parallel group assignments: {group_assignments}")
 
-    if ring_attn_func is RingAttnFunc.VARLEN_LLAMA3:
+    if heads_k_stride is None:
-        from ring_flash_attn import substitute_hf_flash_attn
+        heads_k_stride = 1
 
-        substitute_hf_flash_attn(
+    from ring_flash_attn import substitute_hf_flash_attn
-            process_group=get_ring_attn_group(), heads_k_stride=heads_k_stride or 1
-        )
-    elif ring_attn_func in [
-        RingAttnFunc.BATCH_RING,
-        RingAttnFunc.BATCH_ZIGZAG,
-        RingAttnFunc.BATCH_STRIPE,
-    ]:
-        from axolotl.monkeypatch.attention.ring_attn.adapters.batch import (
-            substitute_hf_flash_attn,
-        )
 
-        substitute_hf_flash_attn(
+    substitute_hf_flash_attn(
-            process_group=get_ring_attn_group(),
+        process_group=get_ring_attn_group(), heads_k_stride=heads_k_stride
-            ring_attn_func=ring_attn_func,
+    )
-        )
 
 
-def update_ring_attn_params(position_ids: torch.Tensor | None):
+def update_ring_attn_params(batch: dict[str, torch.Tensor]):
     """
     Calculate the cumulative sequence lengths for the current forward pass and pass the
     value to the substituted `ring_flash_attn`.
 
     Args:
-        position_ids: Optional tensor of position IDs (for sample packed data).
+        batch: A dictionary with a batch of data. May or may not contain `position_ids`
+            data; if not, we compute it.
     """
     from ring_flash_attn import update_ring_flash_attn_params
 
+    input_ids = batch["input_ids"]
+    position_ids = batch.get("position_ids")
+    if position_ids is None:
+        seq_len = input_ids.shape[1]
+        position_ids = torch.arange(
+            0, seq_len, dtype=torch.long, device=input_ids.device
+        ).unsqueeze(0)
+
     cu_seqlens, _ = get_cu_seqlens_from_pos_ids(position_ids)
     cu_seqlens = cu_seqlens.squeeze().to(device=torch.cuda.current_device())
     update_ring_flash_attn_params(cu_seqlens, get_ring_attn_group())

src/axolotl/monkeypatch/attention/ring_attn/__init__.py

@@ -1,12 +0,0 @@
-"""Init for ring attention monkeypatch module"""
-
-# pylint: disable=unused-import
-# flake8: noqa
-
-from .patch import (
-    RingAttnFunc,
-    get_ring_attn_group,
-    register_ring_attn,
-    set_ring_attn_group,
-    update_ring_attn_params,
-)

src/axolotl/monkeypatch/attention/ring_attn/adapters/batch.py

@@ -1,192 +0,0 @@
-"""
-HuggingFace flash attention adapter for basic ring attention (batch API).
-
-Inspired by
-https://github.com/zhuzilin/ring-flash-attention/blob/ce9fd3935ca0e5f0592bb0826cbed18ec69da729/ring_flash_attn/adapters/hf_adapter.py.
-Our implementation closely follows the structure of that module, but we've minified it
-somewhat to support only the latest versions of transformers.
-"""
-
-# pylint: disable=protected-access,cyclic-import
-
-import os
-from typing import Callable
-
-import torch
-import torch.distributed as dist
-import transformers
-import transformers.modeling_flash_attention_utils
-from ring_flash_attn import (
-    ring_flash_attn_func,
-    stripe_flash_attn_func,
-    zigzag_ring_flash_attn_func,
-)
-from ring_flash_attn.adapters.hf_adapter import check_params
-from transformers.modeling_flash_attention_utils import (
-    _flash_supports_window_size,
-    is_flash_attn_greater_or_equal,
-)
-from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
-
-from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
-
-RING_ATTN_FUNC_MAPPING = {
-    RingAttnFunc.BATCH_RING: ring_flash_attn_func,
-    RingAttnFunc.BATCH_ZIGZAG: zigzag_ring_flash_attn_func,
-    RingAttnFunc.BATCH_STRIPE: stripe_flash_attn_func,
-}
-
-
-def create_flash_attn_forward(
-    process_group: dist.ProcessGroup, ring_attn_func: RingAttnFunc
-) -> Callable:
-    """
-    Create a ring flash attention forward function compatible with HuggingFace's
-    interface.
-
-    Args:
-        process_group: A PyTorch distributed process group.
-        ring_attn_func: Function from `ring_flash_attention` to replace HF flash
-            attention with.
-
-    Returns:
-        A function that implements the ring flash attention forward pass with the
-            signature expected by HuggingFace Transformers.
-    """
-
-    # transformers 4.48+
-    # pylint: disable=unused-argument
-    def _flash_attention_forward(
-        query_states: torch.Tensor,
-        key_states: torch.Tensor,
-        value_states: torch.Tensor,
-        attention_mask: torch.Tensor,
-        query_length: int,
-        is_causal: bool,
-        dropout: float = 0.0,
-        position_ids: torch.Tensor | None = None,
-        softmax_scale: float | None = None,
-        sliding_window: int | None = None,
-        use_top_left_mask: bool = False,
-        softcap: float | None = None,
-        deterministic: bool = None,
-        cu_seq_lens_q: torch.LongTensor | None = None,
-        cu_seq_lens_k: torch.LongTensor | None = None,
-        max_length_q: int | None = None,
-        max_length_k: int | None = None,
-        target_dtype: torch.dtype | None = None,
-        **kwargs,
-    ):
-        """
-        Calls the forward method of Ring Flash Attention.
-
-        Args:
-            query_states: Tensor containing the query vectors.
-            key_states: Tensor containing the key vectors.
-            value_states: Tensor containing the value vectors.
-            attention_mask: Not used in this implementation.
-            query_length: Integer representing the length of the query sequence.
-            is_causal: Boolean indicating whether to apply a causal mask to the attention.
-            dropout: Float representing the dropout probability. Default is 0.0.
-            position_ids: Not used in this implementation.
-            softmax_scale: Optional float value for the softmax scaling factor. Default is None.
-            sliding_window: Optional integer defining the size of the sliding attention window.
-                Default is None.
-            use_top_left_mask: Boolean indicating whether to use a top-left mask for the attention.
-                Default is False.
-            softcap: Not used in this implementation.
-            deterministic: Optional boolean to enforce deterministic computation. Default is None.
-            cu_seq_lens_q: Not used in this implementation.
-            cu_seq_lens_k: Not used in this implementation.
-            max_length_q: Not used in this implementation.
-            max_length_k: Not used in this implementation.
-            target_dtype: Not used in this implementation.
-            **kwargs: Additional keyword arguments. Not used in this implementation.
-
-        Returns:
-            torch.Tensor: The output of the attention mechanism, with shape
-                `[batch_size, query_length, num_heads, head_dim]`.
-        """
-        if not use_top_left_mask:
-            causal = is_causal
-        else:
-            causal = is_causal and query_length != 1
-
-        # Handle sliding window
-        use_sliding_windows = (
-            _flash_supports_window_size
-            and sliding_window is not None
-            and key_states.shape[1] > sliding_window
-        )
-        window_size = (
-            (sliding_window, sliding_window) if use_sliding_windows else (-1, -1)
-        )
-
-        # Handle deterministic mode
-        if is_flash_attn_greater_or_equal("2.4.1"):
-            if deterministic is None:
-                deterministic = (
-                    os.environ.get("FLASH_ATTENTION_DETERMINISTIC", "0") == "1"
-                )
-
-        # Call ring flash attention function
-        attn_output = RING_ATTN_FUNC_MAPPING[ring_attn_func](
-            query_states,
-            key_states,
-            value_states,
-            dropout_p=dropout,
-            softmax_scale=softmax_scale,
-            causal=causal,
-            window_size=window_size,
-            alibi_slopes=None,
-            deterministic=deterministic,
-            return_attn_probs=False,
-            group=process_group,
-        )
-
-        return attn_output
-
-    return _flash_attention_forward
-
-
-def substitute_hf_flash_attn(
-    process_group: dist.ProcessGroup, ring_attn_func: RingAttnFunc
-):
-    """
-    Substitute HuggingFace's flash attention implementation with ring-based implementation.
-
-    Args:
-        process_group: PyTorch distributed process group for communication.
-        ring_attn_func: Function from `ring_flash_attention` to replace HF flash
-            attention with.
-    """
-    try:
-        # Substitute flash attention
-        old_flash_attention_forward = (
-            transformers.modeling_flash_attention_utils._flash_attention_forward
-        )
-        new_flash_attention_forward = create_flash_attn_forward(
-            process_group=process_group, ring_attn_func=ring_attn_func
-        )
-
-        if check_params(old_flash_attention_forward, new_flash_attention_forward):
-            transformers.modeling_flash_attention_utils._flash_attention_forward = (
-                new_flash_attention_forward
-            )
-        else:
-            raise ValueError(
-                "The signature of the new flash attention forward function does not match the old one."
-            )
-    except Exception as exception:
-        raise ValueError(
-            f"The current transformer version {transformers.__version__} is not supported. "
-            "Please use pip install -U transformers to upgrade to the latest version. "
-            "If the code failed with the latest version, "
-            f"please file an issue."
-        ) from exception
-
-    # Register with ALL_ATTENTION_FUNCTIONS if available
-    if ALL_ATTENTION_FUNCTIONS is not None:
-        from ring_flash_attn.adapters.hf_adapter import flash_attention_forward
-
-        ALL_ATTENTION_FUNCTIONS["flash_attention_2"] = flash_attention_forward

src/axolotl/monkeypatch/models/llama4/modeling.py

@@ -93,20 +93,9 @@ def patch_llama4_linearized_modeling():
     """
     from transformers.models.llama4 import modeling_llama4
 
-    old_lamma_4_text_experts = modeling_llama4.Llama4TextExperts
     modeling_llama4.Llama4TextExperts = Llama4TextExperts
     setattr(
         sys.modules["transformers.models.llama4"],
         "Llama4TextExperts",
         Llama4TextExperts,
     )
-
-    def unpatch():
-        modeling_llama4.Llama4TextExperts = old_lamma_4_text_experts
-        setattr(
-            sys.modules["transformers.models.llama4"],
-            "Llama4TextExperts",
-            old_lamma_4_text_experts,
-        )
-
-    return unpatch

src/axolotl/monkeypatch/trainer_eval_guard.py

@@ -1,78 +0,0 @@
-"""
-fix for FSDP2 evals when using torch.compile
-"""
-
-import inspect
-import logging
-
-from transformers import Trainer
-
-from axolotl.monkeypatch.utils import detab_code
-
-LOG = logging.getLogger(__name__)
-
-ORIGINAL_TRAINER_CODE = """
-    model.eval()
-"""
-
-PATCHED_TRAINER_CODE = """
-    if hasattr(model, "eval") and callable(model.eval):
-        self.model.eval()
-"""
-
-
-def get_evaluation_loop_code() -> str:
-    training_loop = inspect.getsource(Trainer.evaluation_loop)
-    return training_loop
-
-
-def check_evaluation_loop_is_patchable() -> bool:
-    eval_loop = get_evaluation_loop_code()
-    eval_loop, _ = detab_code(eval_loop)
-    return ORIGINAL_TRAINER_CODE in eval_loop
-
-
-def patch_evaluation_loop_for_fsdp2():
-    """
-    monkeypatch for fixing the eval loop for fsdp2 with torch.compile
-    """
-
-    try:
-        evaluation_loop = get_evaluation_loop_code()
-    except OSError:
-        return
-    Trainer._original_evaluation_loop = (  # pylint: disable=protected-access
-        evaluation_loop
-    )
-    evaluation_loop, _ = detab_code(evaluation_loop)
-    if ORIGINAL_TRAINER_CODE not in evaluation_loop:
-        return
-
-    evaluation_loop = evaluation_loop.replace(
-        ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE
-    )
-    evaluation_loop = evaluation_loop.replace(
-        "def evaluation_loop(",
-        "def _fixed_evaluation_loop(",
-        1,
-    )
-
-    # load imports necessary
-    import transformers.trainer
-
-    items_to_import = []
-    for item in dir(transformers.trainer):
-        if item in evaluation_loop:
-            items_to_import.append(item)
-
-    exec(  # pylint: disable=exec-used  # nosec B102
-        "from transformers.trainer import ("
-        + ", ".join(x for x in items_to_import)
-        + ")",
-        globals(),
-    )
-    exec(evaluation_loop, globals())  # pylint: disable=exec-used  # nosec B102
-    LOG.info("patching _inner_training_loop for fsdp optimizer save")
-    Trainer.evaluation_loop = (  # pylint: disable=protected-access
-        _fixed_evaluation_loop  # pylint: disable=undefined-variable  # noqa: F821
-    )

src/axolotl/prompt_strategies/base.py

@@ -4,73 +4,30 @@ module for base dataset transform strategies
 
 import importlib
 import logging
-import sys
 
 LOG = logging.getLogger("axolotl")
 
 
-def import_from_path(module_name: str, file_path: str):
-    """
-    Import a module from a file path.
-
-    Args:
-        module_name: Name of the module.
-        file_path: Path to the file.
-
-    Returns:
-        module: The imported module.
-    """
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Could not create module spec for: {file_path}")
-    module = importlib.util.module_from_spec(spec)
-
-    sys.modules[module_name] = module
-    loader = importlib.machinery.SourceFileLoader(module_name, file_path)
-    spec.loader = loader
-    loader.exec_module(module)
-    return module
-
-
 def load(strategy, cfg, module_base=None, **kwargs):
-    if len(strategy.split(".")) == 1:
+    try:
-        strategy = strategy + ".default"
+        if len(strategy.split(".")) == 1:
-    load_fn = strategy.split(".")[-1]
+            strategy = strategy + ".default"
-    func = None
+        load_fn = strategy.split(".")[-1]
-    if len(strategy.split(".")) > 1:
+        if len(strategy.split(".")) > 1:
-        try:
+            try:
-            mod = importlib.import_module(
+                importlib.import_module(
-                strategy.split(".")[-2],
+                    strategy.split(".")[-2],
-                ".".join(strategy.split(".")[:-2]),
+                    ".".join(strategy.split(".")[:-2]),
-            )
+                )
-            func = getattr(mod, load_fn)
+                module_base = ".".join(strategy.split(".")[:-2])
-            return func(cfg, **kwargs)
+                strategy = strategy.split(".")[-2]
-        except ModuleNotFoundError:
+            except ModuleNotFoundError:
-            pass
+                strategy = "." + ".".join(strategy.split(".")[:-1])
-
+        else:
-        try:
+            strategy = "." + ".".join(strategy.split(".")[:-1])
-            mod = importlib.import_module(
-                "." + ".".join(strategy.split(".")[:-1]), module_base
-            )
-            func = getattr(mod, load_fn)
-            return func(cfg, **kwargs)
-        except ModuleNotFoundError:
-            pass
-
-        try:
-            file_path = "/".join(strategy.split(".")[:-1]) + ".py"
-            module_name = strategy.split(".")[-2]
-            mod = import_from_path(module_name, file_path)
-            func = getattr(mod, load_fn)
-            if func is not None:
-                return func(cfg, **kwargs)
-        except FileNotFoundError:
-            pass
-    else:
-        strategy = "." + ".".join(strategy.split(".")[:-1])
         mod = importlib.import_module(strategy, module_base)
         func = getattr(mod, load_fn)
         return func(cfg, **kwargs)
-
+    except Exception:  # pylint: disable=broad-exception-caught
-    LOG.warning(f"unable to load strategy {strategy}")
+        LOG.warning(f"unable to load strategy {strategy}")
-    return func
+        return None

src/axolotl/train.py

@@ -81,11 +81,6 @@ def setup_model_and_tokenizer(
     # Apply freezing if specified
     if cfg.unfrozen_parameters:
         freeze_layers_except(model, cfg.unfrozen_parameters)
-        if any(
-            any(embed in param for embed in ["lm_head", "embed_tokens"])
-            for param in cfg.unfrozen_parameters
-        ):
-            model.enable_input_require_grads()
 
     return model, tokenizer, peft_config, processor
 

src/axolotl/utils/collators/batching.py

@@ -4,7 +4,7 @@ includes logic for handling sequence parallelism collation.
 """
 
 from dataclasses import dataclass
-from typing import Any
+from typing import Any, Optional, Union
 
 import numpy as np
 import torch
@@ -13,7 +13,6 @@ 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
@@ -54,15 +53,14 @@ class DataCollatorForSeq2Seq:
     """
 
     tokenizer: PreTrainedTokenizerBase
-    model: Any | None = None
+    model: Optional[Any] = None
-    padding: bool | str | PaddingStrategy = True
+    padding: Union[bool, str, PaddingStrategy] = True
-    max_length: int | None = None
+    max_length: Optional[int] = None
-    pad_to_multiple_of: int | None = None
+    pad_to_multiple_of: Optional[int] = None
     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:
@@ -159,41 +157,19 @@ class DataCollatorForSeq2Seq:
             Sliced batch dictionary.
         """
         # Get local (start, end) for sequence parallelism slicing
-        total_seq_len = batch["input_ids"].size(1)
+        total_seq_len = batch["input_ids"].shape[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
+        # Update params for ring attention calculation
-        if batch.get("position_ids") is not None:
+        update_ring_attn_params(batch=batch)
-            update_ring_attn_params(position_ids=batch["position_ids"])
 
         # Slice batch for sequence parallel processing
-        for key in batch:
+        keys_to_slice = ["input_ids", "attention_mask", "labels", "position_ids"]
-            if batch[key].size(1) == total_seq_len:
+        for key in keys_to_slice:
-                if self.ring_attn_func in [
+            if key in batch:
-                    RingAttnFunc.VARLEN_LLAMA3,
+                batch[key] = batch[key][:, start:end]
-                    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
 

src/axolotl/utils/data/sft.py

@@ -332,23 +332,16 @@ def load_tokenized_prepared_datasets(
         if cfg.local_rank == 0 and not cfg.skip_prepare_dataset:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
             if isinstance(dataset, IterableDataset):
-                num_workers = cfg.dataset_processes
 
-                def gen_from_iter_ds(_ds, worker_id: List[int], num_workers: List[int]):
+                def gen_from_iter_ds(_ds, _=None):
-                    """Generator function to correctly splice the dataset for each worker"""
+                    yield from _ds
-                    for i, item in enumerate(_ds):
-                        if i % num_workers[0] == worker_id[0]:
-                            yield item
 
                 ds_from_iter = Dataset.from_generator(
                     functools.partial(gen_from_iter_ds, dataset),
                     features=dataset.features,
-                    num_proc=num_workers,
+                    num_proc=cfg.dataset_processes,
                     split=split,
-                    gen_kwargs={
+                    gen_kwargs={"_": list(range(cfg.dataset_processes))},
-                        "worker_id": list(range(num_workers)),
-                        "num_workers": [num_workers] * num_workers,
-                    },
                 )
                 ds_from_iter.save_to_disk(str(prepared_ds_path))
             else:

src/axolotl/utils/freeze.py

@@ -2,14 +2,13 @@
 module to freeze/unfreeze parameters by name
 """
 
+import logging
 import re
 from typing import Callable, List, Tuple, Union
 
-from accelerate.logging import get_logger
-
 from axolotl.utils.distributed import is_main_process
 
-LOG = get_logger(__name__)
+LOG = logging.getLogger("axolotl.utils.freeze")
 
 
 def freeze_layers_except(model, regex_patterns):
@@ -185,7 +184,7 @@ class LayerNamePattern:
         """
         self.raw_pattern = pattern
         name_pattern, self.range = self._parse_pattern(pattern)
-        self.name_regex = re.compile(re.sub(r"\.(?!\+)", "\\.", name_pattern))
+        self.name_regex = re.compile(name_pattern.replace(".", "\\."))
 
     def match(self, name: str) -> bool:
         """

src/axolotl/utils/models.py

@@ -542,17 +542,6 @@ class ModelLoader:
             from axolotl.monkeypatch.accelerate.fsdp2 import patch_accelerate_fsdp_utils
 
             patch_accelerate_fsdp_utils()
-
-        if self.cfg.flex_attention:
-            from axolotl.monkeypatch.attention.flex_attn import (
-                patch_flex_make_mask,
-                patch_flex_wrapper,
-            )
-
-            flex_attn_compile_kwargs = self.cfg.flex_attn_compile_kwargs or {}
-            patch_flex_wrapper(**flex_attn_compile_kwargs)
-            patch_flex_make_mask()
-
         # patch gemma3 conditional generation forward before loading plugins
         # as it could be overridden by plugins
         if self.cfg.model_config_type == "llama4":
@@ -655,7 +644,6 @@ class ModelLoader:
             register_ring_attn(
                 sequence_parallel_degree=self.cfg.sequence_parallel_degree,
                 heads_k_stride=self.cfg.heads_k_stride,
-                ring_attn_func=self.cfg.ring_attn_func,
             )
 
     def patch_attention(self) -> None:
@@ -917,6 +905,13 @@ class ModelLoader:
             self.model_config._attn_implementation = (  # pylint: disable=protected-access
                 "flex_attention"
             )
+            from axolotl.monkeypatch.attention.flex_attn import (
+                patch_flex_make_mask,
+                patch_flex_wrapper,
+            )
+
+            patch_flex_wrapper()
+            patch_flex_make_mask()
 
         elif self.cfg.flash_attention:
             if not self.cfg.sample_packing and self.cfg.s2_attention:
@@ -1120,7 +1115,7 @@ class ModelLoader:
 
         return skip_move_to_device
 
-    def adjust_model_config(self) -> None:
+    def ajust_model_config(self) -> None:
         if (
             hasattr(self.model, "config")
             and hasattr(self.model.config, "max_position_embeddings")
@@ -1280,7 +1275,7 @@ class ModelLoader:
         else:
             self.model.tie_weights()
 
-        self.adjust_model_config()
+        self.ajust_model_config()
 
         # log device memory usage
         if hasattr(self.model, "device") and self.model.device.type in (

src/axolotl/utils/schemas/config.py

@@ -225,7 +225,6 @@ class AxolotlInputConfig(
     sdp_attention: bool | None = None
     s2_attention: bool | None = None
     flex_attention: bool | None = None
-    flex_attn_compile_kwargs: dict[str, Any] | None = None
     flash_attention: bool | None = None
     flash_attn_cross_entropy: bool | None = None
     flash_attn_rms_norm: bool | None = None
@@ -259,7 +258,6 @@ class AxolotlInputConfig(
 
     sequence_parallel_degree: int | None = None
     heads_k_stride: int | None = None
-    ring_attn_func: str | None = None
 
     special_tokens: SpecialTokensConfig | None = None
     tokens: list[str] | None = None
@@ -1148,7 +1146,7 @@ class AxolotlInputConfig(
 
         return data
 
-    @field_validator("sequence_parallel_degree", mode="after")
+    @field_validator("sequence_parallel_degree", mode="before")
     @classmethod
     def check_sequence_parallel_degree(cls, value, info):
         if not value:
@@ -1160,12 +1158,9 @@ class AxolotlInputConfig(
                     "flash_attention: true must be set with sequence_parallel_degree > 1"
                 )
 
-            if (
+            if not info.data["micro_batch_size"] == 1:
-                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"
+                    "micro_batch_size must be set to 1 "
                     "due to a `ring-flash-attn` requirement"
                 )
 
@@ -1192,34 +1187,6 @@ class AxolotlInputConfig(
 
         return value
 
-    @field_validator("ring_attn_func", mode="after")
-    @classmethod
-    def check_ring_attn_func(cls, value, info):
-        if not info.data.get("sequence_parallel_degree", 1) > 1:
-            return value
-
-        from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
-
-        if value is not None:
-            # Set the ring attention function if passed in config
-            valid_funcs = list(RingAttnFunc)
-            if value in valid_funcs:
-                value = RingAttnFunc(value)
-            else:
-                raise ValueError(
-                    f"ring_attn_func: {value} must be one of {valid_funcs}"
-                )
-        else:
-            # Default ring attention function selection
-            sample_packing = info.data.get("sample_packing")
-            value = (
-                RingAttnFunc.VARLEN_LLAMA3
-                if sample_packing
-                else RingAttnFunc.BATCH_RING
-            )
-
-        return value
-
     @model_validator(mode="before")
     @classmethod
     def check_muon_deepspeed_fsdp(cls, data):
@@ -1309,14 +1276,11 @@ class AxolotlConfigWCapabilities(AxolotlInputConfig):
         ):
             capabilities = data.get("capabilities")
             is_fsdp = data.get("fsdp") is not None
-            is_fsdp2 = (
+
-                data.get("fsdp_config") is not None
+            if capabilities and capabilities.get("n_gpu", 0) > 1:
-                and str(data.get("fsdp_config").get("fsdp_version")) == "2"
-            )
-            if capabilities and capabilities.get("n_gpu", 0) > 1 and not is_fsdp2:
                 if is_fsdp:
                     raise ValueError(
-                        "lora_mlp_kernel, lora_qkv_kernel, and lora_o_kernel are not compatible with FSDP1."
+                        "lora_mlp_kernel, lora_qkv_kernel, and lora_o_kernel are not compatible with FSDP."
                     )
         return data
 

src/axolotl/utils/trainer.py

@@ -17,7 +17,6 @@ from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
-from axolotl.monkeypatch.trainer_eval_guard import patch_evaluation_loop_for_fsdp2
 from axolotl.utils.distributed import reduce_and_broadcast
 from axolotl.utils.environment import check_cuda_p2p_ib_support
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
@@ -236,8 +235,7 @@ def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    drop_attn_mask = cfg.model_config_type in ["mamba", "gemma3"]
+    if cfg.model_config_type in ["mamba", "gemma3"]:
-    if drop_attn_mask:
         LOG.info("dropping attention_mask column")
         train_dataset = train_dataset.remove_columns("attention_mask")
         if eval_dataset:
@@ -627,12 +625,6 @@ def setup_trainer(
         A trainer instance (either `HFRLTrainer` or `HFCausalTrainer`) configured based
             on the provided parameters.
     """
-    if (
-        cfg.torch_compile
-        and cfg.fsdp_config
-        and str(cfg.fsdp_config.fsdp_version) == "2"
-    ):
-        patch_evaluation_loop_for_fsdp2()
     if cfg.rl:
         trainer_builder = HFRLTrainerBuilder(cfg, model, tokenizer, processor)
         trainer_builder.model_ref = model_ref

tests/e2e/multigpu/solo/test_flex.py

@@ -56,12 +56,11 @@ class TestPackedFlex:
                 "num_epochs": 1,
                 "micro_batch_size": 2,
                 "gradient_accumulation_steps": 2,
-                "gradient_checkpointing": True,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
                 "optimizer": "adamw_torch_fused",
                 "lr_scheduler": "cosine",
-                "max_steps": 2,
+                "max_steps": 5,
                 "use_tensorboard": True,
                 "save_strategy": "no",
             }

tests/e2e/multigpu/solo/test_grpo.py

@@ -177,7 +177,6 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
             "NCCL_P2P_LEVEL": "LOC",
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_USE_V1": "0",
         }
         vllm_process_id = start_vllm(
             cfg.base_model,
@@ -265,7 +264,6 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
             "NCCL_P2P_LEVEL": "LOC",  # nccl can be brittle, assume P2P isn't reliable
             **current_env,
             "CUDA_VISIBLE_DEVICES": "1",
-            "VLLM_USE_V1": "0",
         }
         vllm_process_id = start_vllm(
             cfg.base_model,

tests/e2e/multigpu/test_llama.py

@@ -621,6 +621,12 @@ class TestMultiGPULlama:
             temp_dir + "/runs", "train/train_loss", 2.3, "Train Loss is too high"
         )
 
+    # TODO: remove skip once deepspeed regression is fixed
+    # see https://github.com/huggingface/transformers/pull/37324
+    @pytest.mark.skipif(
+        transformers_version_eq("4.51.0"),
+        reason="zero3 is not supported with transformers==4.51.0",
+    )
     @pytest.mark.parametrize(
         "gradient_accumulation_steps",
         [1, 2],

tests/e2e/multigpu/test_sp.py

@@ -3,7 +3,6 @@
 import os
 from pathlib import Path
 
-import pytest
 import yaml
 from accelerate.test_utils import execute_subprocess_async
 from transformers.testing_utils import get_torch_dist_unique_port
@@ -18,15 +17,8 @@ os.environ["WANDB_DISABLED"] = "true"
 class TestSequenceParallelism:
     """Test case for training with sequence parallelism enabled"""
 
-    def _run_sequence_parallel_test(
+    def test_sequence_parallel_training(self, temp_dir):
-        self,
+        # pylint: disable=duplicate-code
-        temp_dir,
-        sample_packing=True,
-        micro_batch_size=1,
-        pad_to_sequence_len=True,
-        ring_attn_func=None,
-    ):
-        """Helper method to run sequence parallel tests with different configurations"""
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
@@ -35,9 +27,9 @@ class TestSequenceParallelism:
                 "strict": False,
                 "sequence_len": 2048,
                 "adapter": "qlora",
-                "sample_packing": sample_packing,
+                "sample_packing": True,
-                "eval_sample_packing": sample_packing,
+                "eval_sample_packing": True,
-                "pad_to_sequence_len": pad_to_sequence_len,
+                "pad_to_sequence_len": True,
                 "lora_r": 8,
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
@@ -53,7 +45,7 @@ class TestSequenceParallelism:
                 ],
                 "num_epochs": 1,
                 "max_steps": 8,
-                "micro_batch_size": micro_batch_size,
+                "micro_batch_size": 1,
                 "gradient_accumulation_steps": 2,
                 "output_dir": temp_dir,
                 "learning_rate": 0.00001,
@@ -69,7 +61,6 @@ class TestSequenceParallelism:
                 "weight_decay": 0.0,
                 "use_tensorboard": True,
                 "sequence_parallel_degree": 2,
-                "ring_attn_func": ring_attn_func,
             }
         )
 
@@ -95,35 +86,3 @@ class TestSequenceParallelism:
         check_tensorboard(
             temp_dir + "/runs", "train/train_loss", 2.6, "Train Loss is too high"
         )
-
-    @pytest.mark.parametrize(
-        "sample_packing, micro_batch_size, pad_to_sequence_len, ring_attn_func",
-        [
-            (True, 1, True, None),  # defaults to varlen_llama3 ring_attn_func
-            (False, 2, True, None),  # defaults to batch_ring ring_attn_func
-            (False, 2, True, "batch_zigzag"),
-            # (False, 2, False),  # not yet working
-        ],
-        ids=[
-            "sample_packing, varlen_llama3 ring_attn_func",
-            "no sample_packing, no pad_to_sequence_len, batch_ring ring_attn_func",
-            "no sample_packing, no pad_to_sequence_len, batch_zigzag ring_attn_func",
-            # "no sample_packing, pad_to_sequence_len",  # not yet working
-        ],
-    )
-    def test_sequence_parallel_training(
-        self,
-        temp_dir,
-        sample_packing,
-        micro_batch_size,
-        pad_to_sequence_len,
-        ring_attn_func,
-    ):
-        """Test sequence parallel training with different configurations"""
-        self._run_sequence_parallel_test(
-            temp_dir,
-            sample_packing=sample_packing,
-            micro_batch_size=micro_batch_size,
-            pad_to_sequence_len=pad_to_sequence_len,
-            ring_attn_func=ring_attn_func,
-        )

tests/e2e/patched/lora_kernels/test_lora_kernel_patching.py

@@ -144,7 +144,7 @@ def test_swiglu_mlp_integration(small_llama_model):
 def test_geglu_model_integration():
     """Test GeGLU activation with Gemma model."""
     model = AutoModelForCausalLM.from_pretrained(
-        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="cuda:0"
+        "mhenrichsen/gemma-2b", torch_dtype=torch.float16, device_map="auto"
     )
     peft_config = get_peft_config(
         {
@@ -347,7 +347,7 @@ def test_model_architecture(model_config):
     """Test LoRA kernel patches across different model architectures."""
     # Load model with appropriate dtype
     model = AutoModelForCausalLM.from_pretrained(
-        model_config["name"], torch_dtype=model_config["dtype"], device_map="cuda:0"
+        model_config["name"], torch_dtype=model_config["dtype"], device_map="auto"
     )
 
     # Apply LoRA configuration

tests/e2e/patched/test_sp.py

@@ -73,10 +73,7 @@ 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 (
+        from axolotl.monkeypatch.attention.ring_attn import register_ring_attn
-            RingAttnFunc,
-            register_ring_attn,
-        )
 
         # Setup mocks
         mock_world_size.return_value = 8  # 8 GPUs total
@@ -85,11 +82,7 @@ class TestRingAttention:
         mock_new_group.return_value = mock_group
 
         # Call register_ring_attn with size 4
-        register_ring_attn(
+        register_ring_attn(sequence_parallel_degree=4, heads_k_stride=1)
-            sequence_parallel_degree=4,
-            heads_k_stride=1,
-            ring_attn_func=RingAttnFunc.VARLEN_LLAMA3,
-        )
 
         # Verify the number of calls without examining the arguments
         assert mock_new_group.call_count == 2

tests/e2e/solo/test_preprocess.py

@@ -1,85 +0,0 @@
-"""
-E2E tests for preprocessing
-"""
-
-import logging
-import os
-import unittest
-
-import transformers
-
-from axolotl.cli.args import PreprocessCliArgs
-from axolotl.common.datasets import load_preference_datasets
-from axolotl.utils.config import normalize_config, validate_config
-from axolotl.utils.dict import DictDefault
-
-from ..utils import with_temp_dir
-
-LOG = logging.getLogger("axolotl.tests.e2e")
-os.environ["WANDB_DISABLED"] = "true"
-
-
-class TestCustomRewardFunctionLoading(unittest.TestCase):
-    """
-    Test case for GRPO training using single GPU
-    """
-
-    def _utils_write_rewards(self):
-        # write cfg to yaml file
-        with open("rewards.py", "w", encoding="utf-8") as fout:
-            fout.write(
-                """import random
-def rand_reward_func(completions, **kwargs) -> list[float]:
-    return [random.uniform(0, 1) for _ in completions]
-
-def oai_gsm8k_transform(cfg, *args, **kwargs):
-    def transform_fn(example, tokenizer=None):
-        label = example["answer"].split("####")[-1].strip().replace(",", "")
-        return {
-            "prompt": [{"role": "user", "content": example["question"]},],
-            "answer": label,
-        }
-    return transform_fn, {"remove_columns": ["question"]}
-"""
-            )
-
-    @with_temp_dir
-    def test_custom_rewards_fn_preprocess(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "strict": False,
-                "rl": "grpo",
-                "trl": {
-                    "beta": 0.001,
-                    "max_completion_length": 256,
-                    "use_vllm": True,
-                    "num_generations": 4,
-                    "reward_funcs": [
-                        "rewards.rand_reward_func"
-                    ],  # format: '{file_name}.{fn_name}'
-                    "reward_weights": [1.0],
-                },
-                "datasets": [
-                    {
-                        "path": "openai/gsm8k",
-                        "name": "main",
-                        "type": "rewards.oai_gsm8k_transform",
-                    },
-                ],
-                "dataset_prepared_path": temp_dir,
-                "gradient_accumulation_steps": 1,
-                "micro_batch_size": 1,
-                "learning_rate": 0.000005,
-            }
-        )
-
-        self._utils_write_rewards()
-
-        cfg = validate_config(cfg)
-        normalize_config(cfg)
-        parser = transformers.HfArgumentParser(PreprocessCliArgs)
-        cli_args, _ = parser.parse_args_into_dataclasses(return_remaining_strings=True)
-
-        load_preference_datasets(cfg=cfg, cli_args=cli_args)