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fleshed out _positional_embeddings.py with Qwen2_5_VLRotaryEmbedding class and Qwen2_5_VLCompatibleRotaryEmbedding. Qwen2_5_VLRotaryEmbedding is used in Qwen2_5_VLCompatibleRotaryEmbedding, which inherits from nn.Module. Qwen2_5_VLCompatibleRotaryEmbedding.forward() takes in a query or key tensor, input_pos tensor, and applies MRoPE.
wrapper function around MultiHeadAttention with MRoPE beginnings of implementation for qwen2_5_vl_text_decoder
* Qwen25VLEarlyFusionModel inherits from EarlyFusionModel * forward() calls get_rope_index with input_ids * Incorporated Qwen25VLEarlyFusionModel into _model_builders.py
* incorrect raise condition in _positional_embeddings.py * set bias=False in text decoder MLP
* added batch testing in test_full_model
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/torchtune/2868
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| *, | ||
| mask: Optional[_MaskType] = None, | ||
| input_pos: Optional[torch.Tensor] = None, | ||
| window_index: Optional[torch.Tensor] = None, |
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just a placeholder - a main dilemma we've had was where the unique window and rope index calculations should live in the stack. Currently, we've tried our best to preserve primitives and patterns but clearly not perfect, would appreciate outside input
| from torchtune.modules.model_fusion._early_fusion import EarlyFusionModel | ||
| from torchtune.modules import TransformerDecoder | ||
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| class Qwen25VL(EarlyFusionModel): |
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a bit rocky, early looks appreciated @joecummings |
* created a _call_pos_embedding_safely function in attention.py as a workaround
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| def _call_pos_embedding_safely( | ||
| pos_embedding: nn.Module, | ||
| x: torch.Tensor, | ||
| input_pos: Optional[torch.Tensor] = None, | ||
| window_index: Optional[torch.Tensor] = None, | ||
| ) -> torch.Tensor: | ||
| """ | ||
| Call positional embedding with only the parameters it accepts. | ||
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| Args: | ||
| pos_embedding (nn.Module): The positional embedding module | ||
| x (torch.Tensor): Input tensor | ||
| input_pos (Optional[torch.Tensor]): Optional input position tensor | ||
| window_index (Optional[torch.Tensor]): Optional window index tensor | ||
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| Returns: | ||
| Output tensor from positional embedding | ||
| """ | ||
| sig = inspect.signature(pos_embedding.forward) | ||
| kwargs = {} | ||
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| # Only add parameters that the method accepts | ||
| if "input_pos" in sig.parameters: | ||
| kwargs["input_pos"] = input_pos | ||
| if "window_index" in sig.parameters: | ||
| kwargs["window_index"] = window_index | ||
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| return pos_embedding(x, **kwargs) | ||
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Current workaround for passing window_index into the positional embedding module.
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Context
What is the purpose of this PR? Is it to
Please link to any issues this PR addresses.
#2699
Changelog
What are the changes made in this PR?
*custom modules for Qwen 2.5 VL
*model + component builders for all variants
*Transform + custom collation
*Weight loading
*Added unit tests
Test plan
Please make sure to do each of the following if applicable to your PR. If you're unsure about any one of these just ask and we will happily help. We also have a contributing page for some guidance on contributing.
so far we've done
pre-commit install)pytest testspytest tests -m integration_testUX
If your function changed a public API, please add a dummy example of what the user experience will look like when calling it.
Here is a docstring example
and a tutorial example
co-authored by @lawrencefeng17