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b/src/megatron/bridge/recipes/gemma/gemma2.py index 8987202ee..98c80e3e5 100644 --- a/src/megatron/bridge/recipes/gemma/gemma2.py +++ b/src/megatron/bridge/recipes/gemma/gemma2.py @@ -247,7 +247,7 @@ def _gemma2_common( reset_attention_mask=False, reset_position_ids=False, eod_mask_loss=False, - sequence_length=seq_length, + seq_length=seq_length, num_dataset_builder_threads=1, blend=blend, blend_per_split=blend_per_split, diff --git a/src/megatron/bridge/training/config.py b/src/megatron/bridge/training/config.py index 70420c8aa..42762e6ca 100644 --- a/src/megatron/bridge/training/config.py +++ b/src/megatron/bridge/training/config.py @@ -316,19 +316,22 @@ class GPTDatasetConfig(MCoreGPTDatasetConfig, DataloaderConfig): def __init__( self, - seq_length: int, + seq_length: int | None = None, skip_getting_attention_mask_from_dataset: bool = True, *args, **kwargs, ): """ Args: - seq_length (int): the sequence length. + seq_length (int): the sequence length. If not provided, `sequence_length` must be in kwargs. skip_getting_attention_mask_from_dataset (bool): if set, the dataset will pass a None attention mask and the attention mask is autogenerated from the attn backend. """ self.skip_getting_attention_mask_from_dataset = skip_getting_attention_mask_from_dataset - kwargs["sequence_length"] = seq_length + if seq_length is not None: + kwargs["sequence_length"] = seq_length + elif "sequence_length" not in kwargs: + raise ValueError("Either `seq_length` or `sequence_length` must be provided.") dataloader_kwargs = {k: kwargs.pop(k) for k in list(kwargs) if k in DataloaderConfig.__dataclass_fields__} MCoreGPTDatasetConfig.__init__(self, *args, **kwargs) diff --git a/tests/functional_tests/quantization/models/qwen/test_qwen3_moe_quantization_workflow.py b/tests/functional_tests/quantization/models/qwen/test_qwen3_moe_quantization_workflow.py index 4b90cd780..6d39e4eef 100644 --- a/tests/functional_tests/quantization/models/qwen/test_qwen3_moe_quantization_workflow.py +++ b/tests/functional_tests/quantization/models/qwen/test_qwen3_moe_quantization_workflow.py @@ -21,6 +21,9 @@ from transformers import AutoTokenizer, Qwen3MoeConfig, Qwen3MoeForCausalLM +pytestmark = pytest.mark.pleasefixme(reason="Blocked on upstream quantization dependency issue; re-enable once fixed.") + + HF_QWEN3_MOE_TOY_MODEL_CONFIG = { "architectures": ["Qwen3MoeForCausalLM"], "attention_bias": False, diff --git a/tests/functional_tests/quantization/test_export_workflow.py b/tests/functional_tests/quantization/test_export_workflow.py index af8de56ad..e648fb78f 100644 --- a/tests/functional_tests/quantization/test_export_workflow.py +++ b/tests/functional_tests/quantization/test_export_workflow.py @@ -21,6 +21,9 @@ from safetensors import safe_open +pytestmark = pytest.mark.pleasefixme(reason="Blocked on upstream quantization dependency issue; re-enable once fixed.") + + class TestExportWorkflow: """ Test complete export workflow: quantize HuggingFace models to Megatron format, diff --git a/tests/functional_tests/quantization/test_qat_workflow.py b/tests/functional_tests/quantization/test_qat_workflow.py index 0522ee3bb..21b0a3310 100644 --- a/tests/functional_tests/quantization/test_qat_workflow.py +++ b/tests/functional_tests/quantization/test_qat_workflow.py @@ -20,6 +20,9 @@ import pytest + +pytestmark = pytest.mark.pleasefixme(reason="Blocked on upstream quantization dependency issue; re-enable once fixed.") + from megatron.bridge.training.utils.checkpoint_utils import ( TRACKER_PREFIX, get_checkpoint_name, diff --git a/tests/functional_tests/quantization/test_quantization_workflow.py b/tests/functional_tests/quantization/test_quantization_workflow.py index ad4d1bd11..ddbd1fb44 100644 --- a/tests/functional_tests/quantization/test_quantization_workflow.py +++ b/tests/functional_tests/quantization/test_quantization_workflow.py @@ -18,6 +18,9 @@ import pytest +pytestmark = pytest.mark.pleasefixme(reason="Blocked on upstream quantization dependency issue; re-enable once fixed.") + + class TestQuantizationWorkflow: """ Test complete quantization workflow: quantize HuggingFace models to Megatron format,