* 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>
419 lines
16 KiB
Python
419 lines
16 KiB
Python
"""Tests for attn_implementation: normalization, canonical-value acceptance,
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capability flags, backend registration, and downstream validators.
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"""
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import logging
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from contextlib import contextmanager
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import pytest
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from axolotl.utils.config import validate_config
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from axolotl.utils.dict import DictDefault
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from axolotl.utils.schemas.config import AxolotlInputConfig
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from axolotl.utils.schemas.enums import (
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ATTN_IMPLS_SUPPORTING_PACKING,
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ATTN_IMPLS_USING_FLASH_LIB,
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ATTN_IMPLS_WITHOUT_DTYPE_CAST,
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CANONICAL_ATTN_IMPLS,
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)
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@contextmanager
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def _capture_axolotl_warnings(caplog):
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"""Capture WARNINGs from `axolotl.*` loggers via caplog.
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`axolotl.cli` calls `configure_logging()` at import time, which sets
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`propagate=False` on the `axolotl` logger so records do not reach the root
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logger that pytest's `caplog` hooks. This helper temporarily re-enables
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propagation for the duration of the block.
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"""
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ax_logger = logging.getLogger("axolotl")
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old_propagate = ax_logger.propagate
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ax_logger.propagate = True
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try:
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with caplog.at_level(logging.WARNING, logger="axolotl"):
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yield
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finally:
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ax_logger.propagate = old_propagate
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def _xformers_available():
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try:
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import xformers.ops # noqa: F401
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return True
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except (ImportError, OSError):
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return False
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class TestCapabilityTables:
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"""Backend capability classification via frozensets and computed_field properties."""
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@pytest.mark.parametrize(
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"impl",
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[
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"flash_attention_2",
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"flash_attention_3",
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"flex_attention",
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"xformers",
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"sage",
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],
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)
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def test_supports_packing(self, impl):
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assert impl in ATTN_IMPLS_SUPPORTING_PACKING
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@pytest.mark.parametrize("impl", ["eager", "sdpa", "s2", "fp8"])
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def test_does_not_support_packing(self, impl):
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assert impl not in ATTN_IMPLS_SUPPORTING_PACKING
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@pytest.mark.parametrize("impl", ["flash_attention_2", "flash_attention_3", "s2"])
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def test_uses_flash_lib(self, impl):
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assert impl in ATTN_IMPLS_USING_FLASH_LIB
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@pytest.mark.parametrize(
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"impl", ["eager", "sdpa", "xformers", "flex_attention", "sage", "fp8"]
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)
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def test_does_not_use_flash_lib(self, impl):
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assert impl not in ATTN_IMPLS_USING_FLASH_LIB
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@pytest.mark.parametrize("impl", ["eager", "sdpa"])
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def test_no_dtype_cast(self, impl):
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assert impl in ATTN_IMPLS_WITHOUT_DTYPE_CAST
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@pytest.mark.parametrize(
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"impl",
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[
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"flash_attention_2",
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"flash_attention_3",
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"flex_attention",
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"xformers",
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"sage",
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"s2",
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"fp8",
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],
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)
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def test_needs_dtype_cast(self, impl):
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assert impl not in ATTN_IMPLS_WITHOUT_DTYPE_CAST
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def test_known_hub_kernels_classified(self):
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assert "kernels-community/flash-attn3" in ATTN_IMPLS_SUPPORTING_PACKING
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assert "kernels-community/flash-attn3" in ATTN_IMPLS_USING_FLASH_LIB
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assert "kernels-community/sage-attention" in ATTN_IMPLS_SUPPORTING_PACKING
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def test_computed_flags_readable_on_validated_cfg(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(attn_implementation="sdpa")
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validated = validate_config(cfg)
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assert validated.attn_implementation == "sdpa"
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assert validated.attn_supports_packing is False
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assert validated.attn_uses_flash_lib is False
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assert validated.attn_needs_dtype_cast is False
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def test_computed_flags_not_overridable_from_yaml(self, min_base_cfg):
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"""YAML attempts to override a computed field must not win."""
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cfg = min_base_cfg | DictDefault(
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attn_implementation="eager", attn_uses_flash_lib=True
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)
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validated = validate_config(cfg)
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# The computed field reflects the backend, not the YAML input.
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assert validated.attn_uses_flash_lib is False
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class TestBackendRegistration:
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"""Axolotl-owned backends register under their canonical names in HF's registries."""
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@pytest.mark.skipif(not _xformers_available(), reason="xformers not available")
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def test_register_xformers(self):
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from transformers.masking_utils import ALL_MASK_ATTENTION_FUNCTIONS
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
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from axolotl.monkeypatch.attention import register_xformers_attn
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register_xformers_attn()
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assert "xformers" in ALL_ATTENTION_FUNCTIONS
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assert "xformers" in ALL_MASK_ATTENTION_FUNCTIONS
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assert (
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ALL_MASK_ATTENTION_FUNCTIONS["xformers"]
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== ALL_MASK_ATTENTION_FUNCTIONS["flash_attention_2"]
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)
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def test_register_sage(self):
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from transformers.masking_utils import ALL_MASK_ATTENTION_FUNCTIONS
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
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from axolotl.monkeypatch.attention import register_sage_attn
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register_sage_attn()
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assert "sage" in ALL_ATTENTION_FUNCTIONS
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assert "sage" in ALL_MASK_ATTENTION_FUNCTIONS
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assert (
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ALL_MASK_ATTENTION_FUNCTIONS["sage"]
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== ALL_MASK_ATTENTION_FUNCTIONS["flash_attention_2"]
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)
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@pytest.mark.skipif(not _xformers_available(), reason="xformers not available")
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def test_xformers_does_not_overwrite_fa2(self):
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
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original_fa2 = ALL_ATTENTION_FUNCTIONS["flash_attention_2"]
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from axolotl.monkeypatch.attention import register_xformers_attn
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register_xformers_attn()
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assert ALL_ATTENTION_FUNCTIONS["flash_attention_2"] is original_fa2
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def test_sage_does_not_overwrite_fa2(self):
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
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original_fa2 = ALL_ATTENTION_FUNCTIONS["flash_attention_2"]
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from axolotl.monkeypatch.attention import register_sage_attn
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register_sage_attn()
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assert ALL_ATTENTION_FUNCTIONS["flash_attention_2"] is original_fa2
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class TestLegacyFlagDeprecation:
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"""Legacy boolean flags (flash_attention, sdp_attention, ...) map to a
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canonical attn_implementation value, are stripped from the validated
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config, and cannot be combined with an explicit canonical value.
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"""
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@staticmethod
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def _normalize(data):
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return AxolotlInputConfig.normalize_attn_implementation(data)
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@pytest.mark.parametrize(
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"flag,expected",
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[
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("flash_attention", "flash_attention_2"),
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("sdp_attention", "sdpa"),
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("xformers_attention", "xformers"),
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("flex_attention", "flex_attention"),
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("sage_attention", "sage"),
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("eager_attention", "eager"),
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("s2_attention", "s2"),
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],
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)
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def test_legacy_flag_maps_to_canonical(self, flag, expected):
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result = self._normalize({flag: True})
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assert result["attn_implementation"] == expected
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def test_legacy_flags_are_stripped_after_mapping(self):
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result = self._normalize({"flash_attention": True})
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for flag in [
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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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"eager_attention",
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"s2_attention",
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]:
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assert flag not in result
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def test_s2_plus_flash_priority_is_s2(self):
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result = self._normalize({"s2_attention": True, "flash_attention": True})
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assert result["attn_implementation"] == "s2"
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def test_sage_plus_flash_priority_is_sage(self):
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result = self._normalize({"sage_attention": True, "flash_attention": True})
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assert result["attn_implementation"] == "sage"
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def test_canonical_plus_legacy_flag_raises(self):
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with pytest.raises(ValueError, match="cannot be combined with legacy"):
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self._normalize(
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{"attn_implementation": "flash_attention_2", "flash_attention": True}
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)
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def test_canonical_plus_unrelated_legacy_flag_raises(self):
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with pytest.raises(ValueError, match="cannot be combined with legacy"):
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self._normalize(
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{"attn_implementation": "xformers", "flash_attention": True}
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)
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def test_legacy_flag_stripped_on_validated_cfg(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(flash_attention=True)
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validated = validate_config(cfg)
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assert validated.attn_implementation == "flash_attention_2"
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# Legacy flag must not survive to the validated DictDefault
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# (normalizer pops it, model_dump excludes Nones).
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assert "flash_attention" not in dict(validated)
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def test_canonical_plus_legacy_rejected_on_full_validation(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(
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attn_implementation="flash_attention_2", flash_attention=True
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)
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with pytest.raises(ValueError, match="cannot be combined with legacy"):
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validate_config(cfg)
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def test_s2_plus_flash_maps_to_s2_on_full_validation(self, min_base_cfg):
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"""Priority resolution applies through the full validator chain too."""
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cfg = min_base_cfg | DictDefault(s2_attention=True, flash_attention=True)
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validated = validate_config(cfg)
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assert validated.attn_implementation == "s2"
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class TestCanonicalValueAcceptance:
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"""`attn_implementation` accepts canonical names and `org/name` hub-kernel
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paths. Short-form aliases (`flash`, `flex`, `sdp`) and unknown bare names
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are rejected. Absent input is a noop.
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"""
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@staticmethod
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def _normalize(data):
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return AxolotlInputConfig.normalize_attn_implementation(data)
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def test_canonical_value_is_passthrough(self):
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data = {"attn_implementation": "flash_attention_2"}
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result = self._normalize(data)
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assert result["attn_implementation"] == "flash_attention_2"
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def test_hub_kernel_is_passthrough(self):
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data = {"attn_implementation": "kernels-community/flash-attn3"}
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result = self._normalize(data)
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assert result["attn_implementation"] == "kernels-community/flash-attn3"
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def test_no_attention_set_is_noop(self):
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result = self._normalize({"some_other_config": True})
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assert result.get("attn_implementation") is None
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def test_field_validator_accepts_all_canonical(self):
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for impl in CANONICAL_ATTN_IMPLS:
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assert AxolotlInputConfig.validate_attn_implementation(impl) == impl
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def test_field_validator_accepts_hub_kernels(self):
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for impl in (
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"kernels-community/flash-attn3",
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"kernels-community/sage-attention",
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"someorg/custom-kernel",
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):
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assert AxolotlInputConfig.validate_attn_implementation(impl) == impl
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def test_field_validator_accepts_none(self):
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assert AxolotlInputConfig.validate_attn_implementation(None) is None
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@pytest.mark.parametrize("alias", ["flash", "flex", "sdp"])
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def test_short_form_alias_rejected(self, alias):
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with pytest.raises(ValueError, match="is not accepted"):
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AxolotlInputConfig.validate_attn_implementation(alias)
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def test_unknown_bare_name_rejected(self):
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with pytest.raises(ValueError, match="not a recognized backend"):
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AxolotlInputConfig.validate_attn_implementation("not_a_real_backend")
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def test_canonical_value_passes_through_full_validation(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(attn_implementation="flash_attention_3")
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validated = validate_config(cfg)
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assert validated.attn_implementation == "flash_attention_3"
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assert validated.attn_uses_flash_lib is True
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assert validated.attn_supports_packing is True
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def test_hub_kernel_passes_through_full_validation(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(
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attn_implementation="kernels-community/flash-attn3"
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)
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validated = validate_config(cfg)
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assert validated.attn_implementation == "kernels-community/flash-attn3"
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assert validated.attn_uses_flash_lib is True
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assert validated.attn_supports_packing is True
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def test_short_form_alias_rejected_on_full_validation(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(attn_implementation="flash")
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with pytest.raises(ValueError, match="is not accepted"):
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validate_config(cfg)
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class TestGemma4HybridMode:
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"""`gemma4_hybrid_attn_impl` pins `attn_implementation` to `flash_attention_2`."""
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@staticmethod
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def _normalize(data):
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return AxolotlInputConfig.normalize_attn_implementation(data)
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def test_defaults_to_flash_attention_2(self):
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result = self._normalize({"gemma4_hybrid_attn_impl": True})
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assert result["attn_implementation"] == "flash_attention_2"
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def test_explicit_fa2_passes(self):
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result = self._normalize(
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{
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"gemma4_hybrid_attn_impl": True,
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"attn_implementation": "flash_attention_2",
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}
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)
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assert result["attn_implementation"] == "flash_attention_2"
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def test_non_fa2_raises(self):
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with pytest.raises(
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ValueError, match="requires attn_implementation=flash_attention_2"
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):
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self._normalize(
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{"gemma4_hybrid_attn_impl": True, "attn_implementation": "sdpa"}
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)
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class TestSamplePackingValidation:
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"""`sample_packing` warns for non-varlen backends; s2 raises outright."""
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def test_eager_warns(self, min_base_cfg, caplog):
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cfg = min_base_cfg | DictDefault(
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attn_implementation="eager", sample_packing=True
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)
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with _capture_axolotl_warnings(caplog):
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validate_config(cfg)
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assert any(
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"does not handle cross-sample decontamination" in r.getMessage()
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for r in caplog.records
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)
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def test_sdpa_warns(self, min_base_cfg, caplog):
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cfg = min_base_cfg | DictDefault(
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attn_implementation="sdpa", sample_packing=True
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)
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with _capture_axolotl_warnings(caplog):
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validate_config(cfg)
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assert any(
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"does not handle cross-sample decontamination" in r.getMessage()
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for r in caplog.records
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)
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def test_flash_attention_2_does_not_warn(self, min_base_cfg, caplog):
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cfg = min_base_cfg | DictDefault(
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attn_implementation="flash_attention_2", sample_packing=True
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)
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with _capture_axolotl_warnings(caplog):
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validate_config(cfg)
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assert not any(
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"does not handle cross-sample decontamination" in r.getMessage()
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for r in caplog.records
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)
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def test_s2_raises(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(attn_implementation="s2", sample_packing=True)
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with pytest.raises(
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ValueError, match="shifted-sparse attention does not currently support"
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):
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validate_config(cfg)
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class TestScalingSoftmaxValidation:
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"""`scaling_softmax` is only implemented under flex_attention."""
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def test_non_flex_raises(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(
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attn_implementation="flash_attention_2", scaling_softmax=True
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)
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with pytest.raises(ValueError, match="scaling_softmax requires flex"):
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validate_config(cfg)
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def test_flex_passes(self, min_base_cfg):
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cfg = min_base_cfg | DictDefault(
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attn_implementation="flex_attention", scaling_softmax=True
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)
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validated = validate_config(cfg)
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assert validated.attn_implementation == "flex_attention"
|