e4032fc90f
* upgrade to torchao 0.17.0 * chore: lint * refactor attention handling * replace legacy attention boolean flags with capability properties Replace checks with capability-based properties derived from attn_implementation This separates three concerns that were conflated under flash_attention: 1. Backend selection -> attn_implementation enum 2. Packing capability -> attn_supports_packing property 3. Flash-attn library dependency -> attn_uses_flash_lib property * compute attn capability flags in normalizer instead of properties * make attn_implementation the single source of truth * move attention-dependent validators to mode=after * migrate remaining consumers to canonical attn_implementation * expand attention tests + rewrite docs * migrate example configs to canonical attn_implementation * update doc snippets + reject gemma4-hybrid with non-FA2 backend * remove dead gemma4 branch in _set_attention_config * fix duplicate attn_implementation in gpt-oss yamls and flaky caplog tests * drop "Phase 2" naming from attn-implementation tests * regroup attn_implementation tests by feature concern * clean up verbose comments and remove MD Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai> * fix(collator): pass return_dict=True at apply_chat_template top level for transformers 5.x In transformers 5.x, ProcessorMixin.apply_chat_template gained its own `return_dict` parameter (defaulting to False). When return_dict=False and tokenize=True the method returns out["input_ids"] directly — a 2-D tensor — rather than the full BatchFeature dict. The old code placed `return_dict=True` inside processor_kwargs. In transformers 5.x those kwargs are forwarded to the underlying processor call self(...) where _merge_kwargs silently ignores any key not present in MllamaProcessorKwargs (emitting a warning). The outer return_dict therefore stayed False, apply_chat_template returned the raw input_ids tensor, and the subsequent `batch["input_ids"]` attempted to index a 2-D tensor with the 9-character string "input_ids", producing: IndexError: too many indices for tensor of dimension 2 The fix is to pass return_dict=True as a top-level keyword argument to apply_chat_template (where it is actually consumed) and remove it from processor_kwargs (where it was silently dropped). No version guard is needed: transformers is pinned to ==5.5.4 in pyproject.toml. Adds a unit-level regression test (tests/test_mm_chat_collator.py) that mocks the processor to return a raw tensor when apply_chat_template is called without top-level return_dict=True, verifying the four invariants: process_rows returns a dict, input_ids is 2-D, labels is 2-D, and apply_chat_template receives return_dict=True as a top-level kwarg. Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_multimodal_dataset Fixes: tests/e2e/test_llama_vision.py::TestLlamaVision::test_lora_llama_vision_text_only_dataset Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai> * fix(collator): process_rows returns dict (BatchFeature) shape Two related changes for the multimodal chat collator under transformers 5.x: 1. Wrap apply_chat_template result in dict(...) so process_rows returns a plain dict rather than a BatchFeature instance. BatchFeature is a Mapping but not a dict; downstream code that did batch["labels"] = self.processing_strategy.process_labels(batch["input_ids"]) would index on a tensor when the result wasn't dict-shaped, raising IndexError: too many indices for tensor of dimension 2 2. Soften the regression test's contract from `dict` to `Mapping` so it exercises the actual semantic guarantee (key/value access) rather than the implementation detail (dict vs BatchFeature). Test guards against the original transformers 5.x breakage where apply_chat_template's return_dict default went from True to False. Includes regression test under tests/test_mm_chat_collator.py. Bug surfaced via swarm dispatch task_01KQHPNAYD8XARSNSDJVW1GPF6 against attn-implementation-refactor; squash-merged from agent commits 4de886fd + dc9fcf4f. Signed-off-by: Wing Lian <wing@axolotl.ai> --------- Signed-off-by: Wing Lian <wing@axolotl.ai> Co-authored-by: Axolotl Swarm <no-reply@axolotl.ai>
63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
"""Enforce attn_implementation as the single source of truth.
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Fails if src/ contains a cfg.<legacy>_attention read. Migrate offending sites
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to cfg.attn_implementation or the attn_supports_packing/attn_uses_flash_lib/
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attn_needs_dtype_cast computed flags.
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"""
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from __future__ import annotations
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import re
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from pathlib import Path
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LEGACY_FLAGS = (
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"flash_attention",
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"sdp_attention",
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"xformers_attention",
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"flex_attention",
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"sage_attention",
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"s2_attention",
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"eager_attention",
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)
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# The normalizer is allowed to read the legacy keys (that's its job).
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# lm_eval/cli.py is a raw-YAML entry point (bypasses AxolotlInputConfig) that
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# honors both forms during the deprecation period — when we remove the legacy
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# flags entirely, drop this allowlist entry and the BC branch in that file.
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ALLOWED_FILES = {
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Path("src/axolotl/utils/schemas/config.py"),
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Path("src/axolotl/integrations/lm_eval/cli.py"),
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}
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# `cfg.<flag>`, `self.cfg.<flag>`, `data.get("<flag>")`, `data["<flag>"]`
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_PATTERNS = [re.compile(rf"\bcfg\.{flag}\b") for flag in LEGACY_FLAGS] + [
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re.compile(rf'\bdata\.get\("{flag}"\)') for flag in LEGACY_FLAGS
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]
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def _repo_root() -> Path:
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return Path(__file__).resolve().parent.parent
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def test_no_legacy_attn_reads_in_src():
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root = _repo_root()
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src = root / "src"
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offenders: list[str] = []
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for py_file in src.rglob("*.py"):
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rel = py_file.relative_to(root)
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if rel in ALLOWED_FILES:
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continue
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text = py_file.read_text(encoding="utf-8")
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for pattern in _PATTERNS:
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for match in pattern.finditer(text):
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# Line number for the user's convenience.
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line_no = text.count("\n", 0, match.start()) + 1
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offenders.append(f"{rel}:{line_no} {match.group(0)}")
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assert not offenders, (
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"Found legacy attention-flag reads in src/. Migrate to "
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"`cfg.attn_implementation` / capability flags:\n "
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+ "\n ".join(sorted(offenders))
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)
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