pytest

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 SequenceParallelMixin
+from .sequence_parallel import SequenceParallelContextManager, SequenceParallelMixin

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

@@ -2,6 +2,7 @@
 Module for Axolotl trainer sequence parallelism mixin and training context manager
 """
 
+import functools
 import logging
 
 import torch
@@ -20,6 +21,66 @@ from axolotl.monkeypatch.attention.ring_attn import (
 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.
@@ -125,11 +186,20 @@ class SequenceParallelContextManager:
         # 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(kwargs)
+            kwargs = self.apply_sequence_parallelism(batch=kwargs)
             return args, kwargs
 
         # Forward post-hook to gather outputs
@@ -155,61 +225,6 @@ class SequenceParallelContextManager:
             handle.remove()
         self.hook_handles = []
 
-    def apply_sequence_parallelism(
-        self, batch: dict[str, torch.Tensor]
-    ) -> dict[str, torch.Tensor]:
-        """
-        Apply sequence parallelism slicing to a batch.
-
-        Args:
-            batch: Batch dictionary (e.g., input_ids, attention_mask, etc.)
-
-        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 self.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(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:
-                    # 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
-
     def gather_outputs(self, output):
         """Gather sharded outputs from all ranks and reconstruct the full tensor."""
         # Handle different output formats (dict, tensor, etc.)

tests/e2e/patched/test_sp.py

@@ -2,14 +2,19 @@
 
 # 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
@@ -47,6 +52,27 @@ 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."""
 
@@ -73,11 +99,6 @@ 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
@@ -101,88 +122,308 @@ class TestRingAttention:
         set_ring_attn_group(None)
 
 
-# Mock a simplified DataCollator test
-@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
+class TestConfigValidation:
+    """Tests for validating sequence parallelism configurations."""
 
-    # Create a sample batch
-    batch = {
-        "input_ids": torch.tensor(
-            [
-                [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(autouse=True)
+    def setup_mocks(self, monkeypatch):
+        """Set up mocks for all tests in this class."""
+        # Mock the ring_flash_attn module
+        monkeypatch.setitem(sys.modules, "ring_flash_attn", MagicMock())
 
-    # Simplified slicing logic from SequenceParallelDataCollator
-    def slice_batch(batch, rank, world_size):
-        result = {}
-        for key in batch:
-            seq_len = batch[key].shape[1]
-            slice_size = seq_len // world_size
-            start_idx = rank * slice_size
-            end_idx = start_idx + slice_size if rank < world_size - 1 else seq_len
-            result[key] = batch[key][:, start_idx:end_idx]
-        return result
+        # Mock the is_main_process function to return True
+        monkeypatch.setattr(
+            "axolotl.utils.schemas.config.is_main_process", lambda: True
+        )
 
-    # Slice the batch
-    result = slice_batch(
-        batch, rank=mock_rank.return_value, world_size=mock_world_size.return_value
-    )
+    @pytest.fixture
+    def base_cfg(self):
+        """Create a base configuration for testing."""
+        return DictDefault(
+            {
+                "base_model": "HuggingFaceTB/SmolLM2-135M",
+                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+                "micro_batch_size": 1,
+                "gradient_accumulation_steps": 1,
+                "learning_rate": 1e-3,
+                "output_dir": "./model-out",
+                "sequence_len": 512,
+                "special_tokens": {"pad_token": "<|endoftext|>"},
+            }
+        )
 
-    # Check slicing
-    assert result["input_ids"].shape == (2, 3)  # 12 tokens / 4 GPUs = 3 tokens per GPU
-    expected_input_ids = torch.tensor(
+    @pytest.mark.parametrize(
+        "config_updates, expected_values, should_pass, error_msg",
         [
-            [104, 105, 106],  # Second slice of first sequence
-            [204, 205, 206],  # Second slice of second sequence
-        ]
+            # 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",
+        ],
     )
-    assert torch.all(result["input_ids"] == expected_input_ids)
+    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
+        cfg = base_cfg
+        cfg.update(config_updates)
+
+        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),
+            (None, False, RingAttnFunc.BATCH_RING),
+        ],
+        ids=["default_with_sample_packing", "default_without_sample_packing"],
+    )
+    def test_ring_attn_func_validation(
+        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)
 
 
-@patch.dict("sys.modules", {"ring_flash_attn": MagicMock()})
-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
+class TestApplySequenceParallelism:
+    """Tests for the apply_sequence_parallelism function."""
 
-    # Valid configuration: sequence_parallel_degree > 1 and flash_attention is True
-    cfg = cfg | {
-        "sequence_parallel_degree": 2,
-        "flash_attention": True,
-    }
+    @pytest.fixture(autouse=True)
+    def mock_distributed(self, monkeypatch):
+        """Mock torch.distributed functions for testing."""
+        # Mock is_initialized to return True
+        monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
 
-    # Should validate without errors
-    config = AxolotlInputConfig(**cfg)
-    assert config.sequence_parallel_degree == 2
-    assert config.flash_attention is True
+        # Mock get_rank to return 0 by default
+        monkeypatch.setattr(torch.distributed, "get_rank", lambda *args, **kwargs: 0)
 
+        # Mock get_world_size to return 2 by default
+        monkeypatch.setattr(
+            torch.distributed, "get_world_size", lambda *args, **kwargs: 2
+        )
 
-def test_config_validation_with_invalid_inputs(cfg):
-    """Test that invalid sequence parallelism configurations fail validation."""
-    from axolotl.utils.schemas.config import AxolotlInputConfig
+        # Mock the process group
+        monkeypatch.setattr(
+            "axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group",
+            MagicMock,
+        )
 
-    # Invalid configuration: sequence_parallel_degree > 1 but flash_attention is False
-    cfg = cfg | {
-        "sequence_parallel_degree": 2,
-        "flash_attention": False,
-    }
+        # Mock update_ring_attn_params
+        monkeypatch.setattr(
+            "axolotl.monkeypatch.attention.ring_attn.update_ring_attn_params",
+            lambda **kwargs: None,
+        )
 
-    # Should raise ValidationError
-    with pytest.raises(ValueError) as excinfo:
-        AxolotlInputConfig(**cfg)
+    def test_world_size_one(self, sequence_parallel_batch):
+        """Test that function returns original batch when world size is 1."""
+        result = apply_sequence_parallelism(
+            batch=sequence_parallel_batch,
+            local_rank=0,
+            local_world_size=1,
+            ring_attn_func=RingAttnFunc.BATCH_RING,
+        )
 
-    # Verify error message
-    assert "flash_attention: true must be set" in str(excinfo.value)
+        # Should return the original batch unchanged
+        assert result == sequence_parallel_batch
+
+    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