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Delete the faulty check on multimodal tokens | {
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} | This PR adds support for minor version in the `Expectations` system and changes the way the score is computed to avoid cross-device comparison. It also uses the revamped system to fix 6 tests on AMD devices.
cc. @mht-sharma | {
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} | # What does this PR do?
Fixes [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes/issues/837)
## Before submitting
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [X] Was this discussed/approved via a Github issue or the [bnb/issue/837](https://github.com/bitsandbytes-foundation/bitsandbytes/issues/837)? Please add a link
to it if that's the case.?
## Who can review?
Integrations:
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
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<img width="712" alt="Image" src="https://github.com/user-attachments/assets/e055d5f5-4800-4eb7-8054-0f41a9be5707" /> | {
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"merged_at": "2025-06-03T07:53:07"
} | # What does this PR do?
As per title, so we can do smth like below. In current state the value will be overwritten with default `None`
Might be breaking if the configs on the hub have a certain attention implementation saved in sub-config level only. Though I don't think any model has it saved that way
```python
config.text_config.attn_implementation = "vllm"
AutoMode.from_config(config)
```
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} | # What does this PR do?
The inference in broken on `main` because modular applied changes in the Talker's attention as well. However the config wasn't updated with the new keys for sliding window | {
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https://api.github.com/repos/huggingface/transformers/issues/38524 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38524/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38524/comments | https://api.github.com/repos/huggingface/transformers/issues/38524/events | https://github.com/huggingface/transformers/issues/38524 | 3,109,016,455 | I_kwDOCUB6oc65T9OH | 38,524 | 404 Client Error when accessing https://router.huggingface.co/nebius/v1/chat/completions endpoint | {
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] | closed | false | null | [] | null | [] | 2025-06-02T07:45:52 | 2025-06-04T09:08:06 | 2025-06-04T09:08:05 | NONE | null | null | null | null | ### Feature request
Hello Hugging Face Team,
I encountered a 404 Client Error when trying to access the following API endpoint:
404 Client Error: Not Found for url: https://router.huggingface.co/nebius/v1/chat/completions
(Request ID: Root=1-683d55ae-4365e822229e0a423f164d56;0912aa19-4d00-4575-b250-5e23c4163bcb)
### Motivation
I'm trying to use the nebius chat completion model via the Hugging Face API, but I consistently get a 404 error when accessing the endpoint https://router.huggingface.co/nebius/v1/chat/completions. This prevents me from integrating the model into my application and disrupts my workflow. It’s unclear whether the endpoint has changed or if there is a bug in the API routing. Clarification or a fix would help me and other users relying on this model.
### Your contribution
I’m currently unable to submit a pull request or code fix, but I’m happy to provide more details or test any solutions you suggest | {
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https://api.github.com/repos/huggingface/transformers/issues/38523 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38523/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38523/comments | https://api.github.com/repos/huggingface/transformers/issues/38523/events | https://github.com/huggingface/transformers/issues/38523 | 3,108,749,697 | I_kwDOCUB6oc65S8GB | 38,523 | "Size mismatch" error when trying to download pretrained ChatGPT-4 using transformers | {
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Hi! I have an error "size mismatch" when trying to download ChatGPT-4 using transformers
```
from transformers import AutoModelForCausalLM
model_p = AutoModelForCausalLM.from_pretrained("OpenAI-ChatGPT/ChatGPT-4" )
```
```
RuntimeError: Error(s) in loading state_dict for Qwen2ForCausalLM:
size mismatch for model.embed_tokens.weight: copying a param with shape torch.Size([151936, 1024]) from checkpoint, the shape in current model is torch.Size([151936, 4096]).
size mismatch for model.layers.0.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.0.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.0.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.0.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.0.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.0.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.0.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.0.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.0.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.0.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.0.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.0.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.1.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.1.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.1.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.1.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.1.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.1.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.1.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.1.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.1.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.1.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.1.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.1.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.2.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.2.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.2.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.2.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.2.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.2.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.2.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.2.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.2.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.2.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.2.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.2.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.3.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.3.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.3.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.3.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.3.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.3.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.3.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.3.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.3.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.3.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.3.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.3.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.4.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.4.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.4.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.4.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.4.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.4.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.4.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.4.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.4.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.4.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.4.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.4.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.5.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.5.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.5.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.5.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.5.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.5.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.5.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.5.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.5.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.5.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.5.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.5.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.6.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.6.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.6.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.6.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.6.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.6.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.6.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.6.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.6.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.6.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.6.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.6.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.7.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.7.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.7.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.7.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.7.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.7.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.7.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.7.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.7.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.7.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.7.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.7.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.8.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.8.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.8.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.8.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.8.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.8.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.8.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.8.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.8.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.8.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.8.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.8.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.9.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.9.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.9.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.9.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.9.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.9.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.9.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.9.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.9.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.9.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.9.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.9.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.10.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.10.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.10.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.10.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.10.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.10.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.10.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.10.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.10.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.10.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.10.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.10.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.11.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.11.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.11.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.11.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.11.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.11.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.11.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.11.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.11.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.11.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.11.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.11.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.12.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.12.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.12.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.12.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.12.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.12.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.12.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.12.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.12.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.12.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.12.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.12.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.13.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.13.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.13.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.13.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.13.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.13.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.13.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.13.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.13.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.13.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.13.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.13.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.14.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.14.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.14.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.14.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.14.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.14.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.14.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.14.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.14.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.14.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.14.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.14.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.15.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.15.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.15.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.15.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.15.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.15.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.15.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.15.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.15.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.15.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.15.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.15.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.16.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.16.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.16.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.16.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.16.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.16.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.16.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.16.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.16.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.16.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.16.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.16.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.17.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.17.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.17.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.17.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.17.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.17.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.17.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.17.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.17.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.17.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.17.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.17.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.18.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.18.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.18.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.18.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.18.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.18.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.18.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.18.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.18.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.18.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.18.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.18.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.19.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.19.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.19.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.19.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.19.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.19.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.19.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.19.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.19.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.19.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.19.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.19.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.20.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.20.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.20.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.20.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.20.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.20.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.20.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.20.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.20.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.20.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.20.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.20.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.21.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.21.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.21.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.21.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.21.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.21.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.21.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.21.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.21.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.21.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.21.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.21.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.22.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.22.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.22.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.22.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.22.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.22.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.22.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.22.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.22.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.22.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.22.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.22.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.23.self_attn.q_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.23.self_attn.q_proj.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.23.self_attn.k_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.23.self_attn.k_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.23.self_attn.v_proj.weight: copying a param with shape torch.Size([256, 1024]) from checkpoint, the shape in current model is torch.Size([1024, 4096]).
size mismatch for model.layers.23.self_attn.v_proj.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([1024]).
size mismatch for model.layers.23.self_attn.o_proj.weight: copying a param with shape torch.Size([1024, 1024]) from checkpoint, the shape in current model is torch.Size([4096, 4096]).
size mismatch for model.layers.23.mlp.gate_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.23.mlp.up_proj.weight: copying a param with shape torch.Size([4864, 1024]) from checkpoint, the shape in current model is torch.Size([16384, 4096]).
size mismatch for model.layers.23.mlp.down_proj.weight: copying a param with shape torch.Size([1024, 4864]) from checkpoint, the shape in current model is torch.Size([4096, 16384]).
size mismatch for model.layers.23.input_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.layers.23.post_attention_layernorm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for model.norm.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([4096]).
size mismatch for lm_head.weight: copying a param with shape torch.Size([151936, 1024]) from checkpoint, the shape in current model is torch.Size([151936, 4096]).
You may consider adding `ignore_mismatched_sizes=True` in the model `from_pretrained` method.
```
If I use suggested parameter ignore_mismatched_sizes=True, I get another error:
```
Some weights of the model checkpoint at OpenAI-ChatGPT[/ChatGPT-4](http://localhost:44537/ChatGPT-4) were not used when initializing Qwen2ForCausalLM:
```
Help is appreciated
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenAI-ChatGPT/ChatGPT-4" )
### Expected behavior
Model is downloaded without errors | {
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https://api.github.com/repos/huggingface/transformers/issues/38522 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38522/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38522/comments | https://api.github.com/repos/huggingface/transformers/issues/38522/events | https://github.com/huggingface/transformers/issues/38522 | 3,108,344,215 | I_kwDOCUB6oc65RZGX | 38,522 | Allow `mlm_probability` to be set to None when `mlm`=False in `DataCollatorForLanguageModeling` | {
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] | closed | false | null | [] | null | [] | 2025-06-02T02:53:50 | 2025-07-11T08:02:30 | 2025-07-11T08:02:30 | CONTRIBUTOR | null | null | null | null | ### System Info
Currently, in the `DataCollatorForLanguageModeling` class, the `mlm_probability` argument is required to be a float between 0 and 1, regardless of whether `mlm` is `True` or `False`.
However, since `mlm_probability` is only used when `mlm=True`, it would make sense to allow `mlm_probability=None` when `mlm=False`, in order to reduce confusion and unnecessary configuration.
Relevant code:
[https://github.com/huggingface/transformers/blob/51d732709e5ae424e8fb6c4e58b72057a3e413c2/src/transformers/data/data\_collator.py#L841](https://github.com/huggingface/transformers/blob/51d732709e5ae424e8fb6c4e58b72057a3e413c2/src/transformers/data/data_collator.py#L841)
```python
if self.mlm_probability < 0 or self.mlm_probability > 1:
raise ValueError("mlm_probability should be between 0 and 1.")
```
This condition runs unconditionally and throws an error even if `mlm=False`.
This behavior might be unexpected for users who are not using masked language modeling and do not want to set `mlm_probability` at all.
**Suggested change:**
Wrap the validation and assignment of `mlm_probability` inside a conditional check for `mlm`, like this:
```python
if self.mlm:
if self.tokenizer.mask_token is None:
raise ValueError(...)
if self.mlm_probability is None or self.mlm_probability < 0 or self.mlm_probability > 1:
raise ValueError("mlm_probability should be between 0 and 1.")
self.mlm_probability = float(self.mlm_probability)
```
This would allow users to omit `mlm_probability` (or set it to `None`) when using causal language modeling (`mlm=False`).
-----
I'm asking this because this configuration causes the training execution with the trl library (SFTTrainer, version 0.18.0) to fail.
### Who can help?
@ArthurZucker @zach-huggingface @SunMarc
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```
from trl import SFTTrainer
trainer = SFTTrainer(
model=model,
tokenizer=tokenizer,
data_collator=None, # let this empty and it will create DataCollatorForLanguageModeling with mlm_prob None
train_dataset=dataset,
eval_dataset=eval_dataset,
args=SFTConfig(**config),
)
trainer.train()
```
### Expected behavior
```
if self.mlm:
if self.tokenizer.mask_token is None:
raise ValueError(...)
if self.mlm_probability is None or self.mlm_probability < 0 or self.mlm_probability > 1:
raise ValueError("mlm_probability should be between 0 and 1.")
self.mlm_probability = float(self.mlm_probability)
``` | {
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] | closed | false | null | [] | null | [] | 2025-06-02T02:21:53 | 2025-07-18T12:23:22 | 2025-07-18T12:23:22 | CONTRIBUTOR | null | null | null | null | ### System Info
latest version of transformers, after #37602
### Who can help?
@zucchini-nlp
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Same as #36713 .
Run scripts in https://huggingface.co/HuggingFaceM4/idefics2-8b-chatty/discussions/9.
Besides, I'm not sure the problem is on the transformers source or example script.
### Expected behavior
Expected:
```
# script output
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0]
```
Real output:
```
Traceback (most recent call last):
File "/mnt/home/claude/GitHub/transformers/src/transformers/feature_extraction_utils.py", line 90, in __getattr__
return self.data[item]
~~~~~~~~~^^^^^^
KeyError: 'char_to_token'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/mnt/home/claude/GitHub/closed-world-agents/main.py", line 82, in <module>
inputs = processor.apply_chat_template(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/mnt/home/claude/GitHub/transformers/src/transformers/processing_utils.py", line 1614, in apply_chat_template
start_token = out.char_to_token(i, assistant_start_char)
^^^^^^^^^^^^^^^^^
File "/mnt/home/claude/GitHub/transformers/src/transformers/feature_extraction_utils.py", line 92, in __getattr__
raise AttributeError
AttributeError
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/38520 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38520/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38520/comments | https://api.github.com/repos/huggingface/transformers/issues/38520/events | https://github.com/huggingface/transformers/pull/38520 | 3,107,848,804 | PR_kwDOCUB6oc6YgZEK | 38,520 | Add QuasarV4 model | {
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## What does this PR do?
This PR adds the QuasarV4 model to the transformers library, introducing a new architecture with token temperature mechanism and multi-scale token processing for efficient long context handling.
QuasarV4 extends transformer architecture with two key innovations:
1. **Token Temperature Mechanism**: Dynamically adjusts token importance based on contextual relevance
2. **Multi-Scale Token Processing**: Handles long contexts efficiently by processing tokens at multiple temporal resolutions
These innovations allow QuasarV4 to:
- Maintain computational efficiency by focusing attention on important tokens
- Preserve high-resolution information for recent context while compressing older context
- Achieve better performance on long-context tasks compared to standard transformer models
## Technical Implementation
- Added `modeling_quasarv4.py` with the core model implementation
- Added `configuration_quasarv4.py` with model configuration options
- Implemented token temperature calculation layers
- Added hierarchical compression memory for multi-scale token processing
## Model Architecture
QuasarV4 builds on transformer architecture with several key innovations (Qwen-3 Based):
### Token Temperature Mechanism
The signature feature of QuasarV4 is its token temperature mechanism, which dynamically adjusts the importance of each token based on context:
1. **Multi-dimensional Temperature Calculation**
- 4-layer temperature projection network
- Position-dependent temperature scaling
- Token importance calculation
- Context-aware scaling
2. **Temperature-Guided Attention**
- Attention weights modulated by token temperature
- Efficient focus on contextually important tokens
- Reduced computational waste on irrelevant tokens
### Multi-Scale Token Processing
To efficiently handle long contexts, QuasarV4 implements:
1. **Hierarchical Compression Memory**
- Process tokens at multiple temporal resolutions simultaneously
- Recent tokens at high resolution, older context at progressively lower resolutions
2. **Cross-Resolution Attention**
- Tokens can attend across different resolution levels
- Maintains global context while focusing computational resources
- Enables efficient processing of extremely long sequences
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [yes] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request), Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.
- [yes] Did you make sure to update the documentation with your changes?
- [yes] Did you write any new necessary tests?
| null | {
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https://api.github.com/repos/huggingface/transformers/issues/38519 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38519/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38519/comments | https://api.github.com/repos/huggingface/transformers/issues/38519/events | https://github.com/huggingface/transformers/pull/38519 | 3,107,791,628 | PR_kwDOCUB6oc6YgOIP | 38,519 | Fix `return_dict=False` giving errors in a few VLM models | {
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} | # What does this PR do?
We have some VLM models that have the following patten:
```
@can_return_tuple
def forward(self):
....
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.model( ..., return_dict=return_dict, ...)
....
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
```
However, if a user set `return_dict=False`, `outputs` will be `tuple`, and access to `outputs.hidden_states` will fail.
This PR fixes (some of) them where I found from the failing tests of torchscript tests.
TODO: write a specific test for this and fix those would fail. | {
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https://api.github.com/repos/huggingface/transformers/issues/38518 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38518/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38518/comments | https://api.github.com/repos/huggingface/transformers/issues/38518/events | https://github.com/huggingface/transformers/issues/38518 | 3,107,599,219 | I_kwDOCUB6oc65OjNz | 38,518 | Failed to export PyTorch traced graph of Mixtral-8x7B-Instruct-v0.1 due to the PR #32429 | {
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] | open | false | null | [] | null | [] | 2025-06-01T17:46:08 | 2025-08-04T14:16:00 | null | CONTRIBUTOR | null | null | null | null | ### System Info
Hi ,
I found the recently transformers 4.52.4 merged this PR https://github.com/huggingface/transformers/pull/32429
and it led me failed to run below code snippet which it could run successfully with 4.51.3.
```python
import transformers
import torch.export as te
import torch
from contextlib import nullcontext
torch.autocast = lambda *args, **kwargs: nullcontext()
mixtral = transformers.AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1", device_map="meta")
ep = te.export(mixtral,
args=(torch.randint(0, 100, (2, 4),device="meta", dtype=torch.int32),
torch.randint(0, 100, (2, 4),device="meta", dtype=torch.int32)
),
kwargs={}, strict=False
).module()
```
and this is the error call stack:
```bash
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████| 19/19 [00:24<00:00, 1.27s/it]
W0601 17:35:32.549000 19842 torch/fx/experimental/symbolic_shapes.py:6661] failed during evaluate_expr(u0, hint=None, size_oblivious=False, forcing_spec=False
E0601 17:35:32.550000 19842 torch/fx/experimental/recording.py:299] failed while running evaluate_expr(*(u0, None, False, False), **{})
W0601 17:35:32.552000 19842 torch/fx/experimental/symbolic_shapes.py:7208] Unable to find user code corresponding to {u0}
def forward(self, arg0_1: "f32[32000, 4096]", arg1_1: "f32[4096, 4096]", arg2_1: "f32[1024, 4096]", arg3_1: "f32[1024, 4096]", arg4_1: "f32[4096, 4096]", arg5_1: "f32[8, 4096]", arg6_1: "f32[14336, 4096]", arg7_1: "f32[4096, 14336]", arg8_1: "f32[14336, 4096]", arg9_1: "f32[14336, 4096]", arg10_1: "f32[4096, 14336]", arg11_1: "f32[14336, 4096]", arg12_1: "f32[14336, 4096]", arg13_1: "f32[4096, 14336]", arg14_1: "f32[14336, 4096]", arg15_1: "f32[14336, 4096]", arg16_1: "f32[4096, 14336]", arg17_1: "f32[14336, 4096]", arg18_1: "f32[14336, 4096]", arg19_1: "f32[4096, 14336]", arg20_1: "f32[14336, 4096]", arg21_1: "f32[14336, 4096]", arg22_1: "f32[4096, 14336]", arg23_1: "f32[14336, 4096]", arg24_1: "f32[14336, 4096]", arg25_1: "f32[4096, 14336]", arg26_1: "f32[14336, 4096]", arg27_1: "f32[14336, 4096]", arg28_1: "f32[4096, 14336]", arg29_1: "f32[14336, 4096]", arg30_1: "f32[4096]", arg31_1: "f32[4096]", arg32_1: "f32[4096, 4096]", arg33_1: "f32[1024, 4096]", arg34_1: "f32[1024, 4096]", 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"f32[14336, 4096]", arg71_1: "f32[14336, 4096]", arg72_1: "f32[4096, 14336]", arg73_1: "f32[14336, 4096]", arg74_1: "f32[14336, 4096]", arg75_1: "f32[4096, 14336]", arg76_1: "f32[14336, 4096]", arg77_1: "f32[14336, 4096]", arg78_1: "f32[4096, 14336]", arg79_1: "f32[14336, 4096]", arg80_1: "f32[14336, 4096]", arg81_1: "f32[4096, 14336]", arg82_1: "f32[14336, 4096]", arg83_1: "f32[14336, 4096]", arg84_1: "f32[4096, 14336]", arg85_1: "f32[14336, 4096]", arg86_1: "f32[14336, 4096]", arg87_1: "f32[4096, 14336]", arg88_1: "f32[14336, 4096]", arg89_1: "f32[14336, 4096]", arg90_1: "f32[4096, 14336]", arg91_1: "f32[14336, 4096]", arg92_1: "f32[4096]", arg93_1: "f32[4096]", arg94_1: "f32[4096, 4096]", arg95_1: "f32[1024, 4096]", arg96_1: "f32[1024, 4096]", arg97_1: "f32[4096, 4096]", arg98_1: "f32[8, 4096]", arg99_1: "f32[14336, 4096]", arg100_1: "f32[4096, 14336]", arg101_1: "f32[14336, 4096]", arg102_1: "f32[14336, 4096]", arg103_1: "f32[4096, 14336]", arg104_1: "f32[14336, 4096]", arg105_1: 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"f32[14336, 4096]", arg174_1: "f32[4096, 14336]", arg175_1: "f32[14336, 4096]", arg176_1: "f32[14336, 4096]", arg177_1: "f32[4096, 14336]", arg178_1: "f32[14336, 4096]", arg179_1: "f32[14336, 4096]", arg180_1: "f32[4096, 14336]", arg181_1: "f32[14336, 4096]", arg182_1: "f32[14336, 4096]", arg183_1: "f32[4096, 14336]", arg184_1: "f32[14336, 4096]", arg185_1: "f32[4096]", arg186_1: "f32[4096]", arg187_1: "f32[4096, 4096]", arg188_1: "f32[1024, 4096]", arg189_1: "f32[1024, 4096]", arg190_1: "f32[4096, 4096]", arg191_1: "f32[8, 4096]", arg192_1: "f32[14336, 4096]", arg193_1: "f32[4096, 14336]", arg194_1: "f32[14336, 4096]", arg195_1: "f32[14336, 4096]", arg196_1: "f32[4096, 14336]", arg197_1: "f32[14336, 4096]", arg198_1: "f32[14336, 4096]", arg199_1: "f32[4096, 14336]", arg200_1: "f32[14336, 4096]", arg201_1: "f32[14336, 4096]", arg202_1: "f32[4096, 14336]", arg203_1: "f32[14336, 4096]", arg204_1: "f32[14336, 4096]", arg205_1: "f32[4096, 14336]", arg206_1: "f32[14336, 4096]", arg207_1: "f32[14336, 4096]", arg208_1: "f32[4096, 14336]", arg209_1: "f32[14336, 4096]", arg210_1: "f32[14336, 4096]", arg211_1: "f32[4096, 14336]", arg212_1: "f32[14336, 4096]", arg213_1: "f32[14336, 4096]", arg214_1: "f32[4096, 14336]", arg215_1: "f32[14336, 4096]", arg216_1: "f32[4096]", arg217_1: "f32[4096]", arg218_1: "f32[4096, 4096]", arg219_1: "f32[1024, 4096]", arg220_1: "f32[1024, 4096]", arg221_1: "f32[4096, 4096]", arg222_1: "f32[8, 4096]", arg223_1: "f32[14336, 4096]", arg224_1: "f32[4096, 14336]", arg225_1: "f32[14336, 4096]", arg226_1: "f32[14336, 4096]", arg227_1: "f32[4096, 14336]", arg228_1: "f32[14336, 4096]", arg229_1: "f32[14336, 4096]", arg230_1: "f32[4096, 14336]", arg231_1: "f32[14336, 4096]", arg232_1: "f32[14336, 4096]", arg233_1: "f32[4096, 14336]", arg234_1: "f32[14336, 4096]", arg235_1: "f32[14336, 4096]", arg236_1: "f32[4096, 14336]", arg237_1: "f32[14336, 4096]", arg238_1: "f32[14336, 4096]", arg239_1: "f32[4096, 14336]", arg240_1: "f32[14336, 4096]", arg241_1: "f32[14336, 4096]", arg242_1: "f32[4096, 14336]", arg243_1: "f32[14336, 4096]", arg244_1: "f32[14336, 4096]", arg245_1: "f32[4096, 14336]", arg246_1: "f32[14336, 4096]", arg247_1: "f32[4096]", arg248_1: "f32[4096]", arg249_1: "f32[4096, 4096]", arg250_1: "f32[1024, 4096]", arg251_1: "f32[1024, 4096]", arg252_1: "f32[4096, 4096]", arg253_1: "f32[8, 4096]", arg254_1: "f32[14336, 4096]", arg255_1: "f32[4096, 14336]", arg256_1: "f32[14336, 4096]", arg257_1: "f32[14336, 4096]", arg258_1: "f32[4096, 14336]", arg259_1: "f32[14336, 4096]", arg260_1: "f32[14336, 4096]", arg261_1: "f32[4096, 14336]", arg262_1: "f32[14336, 4096]", arg263_1: "f32[14336, 4096]", arg264_1: "f32[4096, 14336]", arg265_1: "f32[14336, 4096]", arg266_1: "f32[14336, 4096]", arg267_1: "f32[4096, 14336]", arg268_1: "f32[14336, 4096]", arg269_1: "f32[14336, 4096]", arg270_1: "f32[4096, 14336]", arg271_1: "f32[14336, 4096]", arg272_1: "f32[14336, 4096]", arg273_1: "f32[4096, 14336]", arg274_1: "f32[14336, 4096]", arg275_1: "f32[14336, 4096]", arg276_1: "f32[4096, 14336]", arg277_1: "f32[14336, 4096]", arg278_1: "f32[4096]", arg279_1: "f32[4096]", arg280_1: "f32[4096, 4096]", arg281_1: "f32[1024, 4096]", arg282_1: "f32[1024, 4096]", arg283_1: "f32[4096, 4096]", arg284_1: "f32[8, 4096]", arg285_1: "f32[14336, 4096]", arg286_1: "f32[4096, 14336]", arg287_1: "f32[14336, 4096]", arg288_1: "f32[14336, 4096]", arg289_1: "f32[4096, 14336]", arg290_1: "f32[14336, 4096]", arg291_1: "f32[14336, 4096]", arg292_1: "f32[4096, 14336]", arg293_1: "f32[14336, 4096]", arg294_1: "f32[14336, 4096]", arg295_1: "f32[4096, 14336]", arg296_1: "f32[14336, 4096]", arg297_1: "f32[14336, 4096]", arg298_1: "f32[4096, 14336]", arg299_1: "f32[14336, 4096]", arg300_1: "f32[14336, 4096]", arg301_1: "f32[4096, 14336]", arg302_1: "f32[14336, 4096]", arg303_1: "f32[14336, 4096]", arg304_1: "f32[4096, 14336]", arg305_1: "f32[14336, 4096]", arg306_1: "f32[14336, 4096]", arg307_1: "f32[4096, 14336]", arg308_1: "f32[14336, 4096]", arg309_1: "f32[4096]", arg310_1: "f32[4096]", arg311_1: "f32[4096, 4096]", arg312_1: "f32[1024, 4096]", arg313_1: "f32[1024, 4096]", arg314_1: "f32[4096, 4096]", arg315_1: "f32[8, 4096]", arg316_1: "f32[14336, 4096]", arg317_1: "f32[4096, 14336]", arg318_1: "f32[14336, 4096]", arg319_1: "f32[14336, 4096]", arg320_1: "f32[4096, 14336]", arg321_1: "f32[14336, 4096]", arg322_1: "f32[14336, 4096]", arg323_1: "f32[4096, 14336]", arg324_1: "f32[14336, 4096]", arg325_1: "f32[14336, 4096]", arg326_1: "f32[4096, 14336]", arg327_1: "f32[14336, 4096]", arg328_1: "f32[14336, 4096]", arg329_1: "f32[4096, 14336]", arg330_1: "f32[14336, 4096]", arg331_1: "f32[14336, 4096]", arg332_1: "f32[4096, 14336]", arg333_1: "f32[14336, 4096]", arg334_1: "f32[14336, 4096]", arg335_1: "f32[4096, 14336]", arg336_1: "f32[14336, 4096]", arg337_1: "f32[14336, 4096]", arg338_1: "f32[4096, 14336]", arg339_1: "f32[14336, 4096]", arg340_1: "f32[4096]", arg341_1: "f32[4096]", arg342_1: "f32[4096, 4096]", arg343_1: "f32[1024, 4096]", arg344_1: "f32[1024, 4096]", arg345_1: "f32[4096, 4096]", arg346_1: "f32[8, 4096]", arg347_1: "f32[14336, 4096]", arg348_1: "f32[4096, 14336]", arg349_1: "f32[14336, 4096]", arg350_1: "f32[14336, 4096]", arg351_1: "f32[4096, 14336]", arg352_1: "f32[14336, 4096]", arg353_1: "f32[14336, 4096]", arg354_1: "f32[4096, 14336]", arg355_1: "f32[14336, 4096]", arg356_1: "f32[14336, 4096]", arg357_1: "f32[4096, 14336]", arg358_1: "f32[14336, 4096]", arg359_1: "f32[14336, 4096]", arg360_1: "f32[4096, 14336]", arg361_1: "f32[14336, 4096]", arg362_1: "f32[14336, 4096]", arg363_1: "f32[4096, 14336]", arg364_1: "f32[14336, 4096]", arg365_1: "f32[14336, 4096]", arg366_1: "f32[4096, 14336]", arg367_1: "f32[14336, 4096]", arg368_1: "f32[14336, 4096]", arg369_1: "f32[4096, 14336]", arg370_1: "f32[14336, 4096]", arg371_1: "f32[4096]", arg372_1: "f32[4096]", arg373_1: "f32[4096, 4096]", arg374_1: "f32[1024, 4096]", arg375_1: "f32[1024, 4096]", arg376_1: "f32[4096, 4096]", arg377_1: "f32[8, 4096]", arg378_1: "f32[14336, 4096]", arg379_1: "f32[4096, 14336]", arg380_1: "f32[14336, 4096]", arg381_1: "f32[14336, 4096]", arg382_1: "f32[4096, 14336]", arg383_1: "f32[14336, 4096]", arg384_1: "f32[14336, 4096]", arg385_1: "f32[4096, 14336]", arg386_1: "f32[14336, 4096]", arg387_1: "f32[14336, 4096]", arg388_1: "f32[4096, 14336]", arg389_1: "f32[14336, 4096]", arg390_1: "f32[14336, 4096]", arg391_1: "f32[4096, 14336]", arg392_1: "f32[14336, 4096]", arg393_1: "f32[14336, 4096]", arg394_1: "f32[4096, 14336]", arg395_1: "f32[14336, 4096]", arg396_1: "f32[14336, 4096]", arg397_1: "f32[4096, 14336]", arg398_1: "f32[14336, 4096]", arg399_1: "f32[14336, 4096]", arg400_1: "f32[4096, 14336]", arg401_1: "f32[14336, 4096]", arg402_1: "f32[4096]", arg403_1: "f32[4096]", arg404_1: "f32[4096, 4096]", arg405_1: "f32[1024, 4096]", arg406_1: "f32[1024, 4096]", arg407_1: "f32[4096, 4096]", arg408_1: "f32[8, 4096]", arg409_1: "f32[14336, 4096]", arg410_1: "f32[4096, 14336]", arg411_1: "f32[14336, 4096]", arg412_1: "f32[14336, 4096]", arg413_1: "f32[4096, 14336]", arg414_1: "f32[14336, 4096]", arg415_1: "f32[14336, 4096]", arg416_1: "f32[4096, 14336]", arg417_1: "f32[14336, 4096]", arg418_1: "f32[14336, 4096]", arg419_1: "f32[4096, 14336]", arg420_1: "f32[14336, 4096]", arg421_1: "f32[14336, 4096]", arg422_1: "f32[4096, 14336]", arg423_1: "f32[14336, 4096]", arg424_1: "f32[14336, 4096]", arg425_1: "f32[4096, 14336]", arg426_1: "f32[14336, 4096]", arg427_1: "f32[14336, 4096]", arg428_1: "f32[4096, 14336]", arg429_1: "f32[14336, 4096]", arg430_1: "f32[14336, 4096]", arg431_1: "f32[4096, 14336]", arg432_1: "f32[14336, 4096]", arg433_1: "f32[4096]", arg434_1: "f32[4096]", arg435_1: "f32[4096, 4096]", arg436_1: "f32[1024, 4096]", arg437_1: "f32[1024, 4096]", arg438_1: "f32[4096, 4096]", arg439_1: "f32[8, 4096]", arg440_1: "f32[14336, 4096]", arg441_1: "f32[4096, 14336]", arg442_1: "f32[14336, 4096]", arg443_1: "f32[14336, 4096]", arg444_1: "f32[4096, 14336]", arg445_1: "f32[14336, 4096]", arg446_1: "f32[14336, 4096]", arg447_1: "f32[4096, 14336]", arg448_1: "f32[14336, 4096]", arg449_1: "f32[14336, 4096]", arg450_1: "f32[4096, 14336]", arg451_1: "f32[14336, 4096]", arg452_1: "f32[14336, 4096]", arg453_1: "f32[4096, 14336]", arg454_1: "f32[14336, 4096]", arg455_1: "f32[14336, 4096]", arg456_1: "f32[4096, 14336]", arg457_1: "f32[14336, 4096]", arg458_1: "f32[14336, 4096]", arg459_1: "f32[4096, 14336]", arg460_1: "f32[14336, 4096]", arg461_1: "f32[14336, 4096]", arg462_1: "f32[4096, 14336]", arg463_1: "f32[14336, 4096]", arg464_1: "f32[4096]", arg465_1: "f32[4096]", arg466_1: "f32[4096, 4096]", arg467_1: "f32[1024, 4096]", arg468_1: "f32[1024, 4096]", arg469_1: "f32[4096, 4096]", arg470_1: "f32[8, 4096]", arg471_1: "f32[14336, 4096]", arg472_1: "f32[4096, 14336]", arg473_1: "f32[14336, 4096]", arg474_1: "f32[14336, 4096]", arg475_1: "f32[4096, 14336]", arg476_1: "f32[14336, 4096]", arg477_1: "f32[14336, 4096]", arg478_1: "f32[4096, 14336]", arg479_1: "f32[14336, 4096]", arg480_1: "f32[14336, 4096]", arg481_1: "f32[4096, 14336]", arg482_1: "f32[14336, 4096]", arg483_1: "f32[14336, 4096]", arg484_1: "f32[4096, 14336]", arg485_1: "f32[14336, 4096]", arg486_1: "f32[14336, 4096]", arg487_1: "f32[4096, 14336]", arg488_1: "f32[14336, 4096]", arg489_1: "f32[14336, 4096]", arg490_1: "f32[4096, 14336]", arg491_1: "f32[14336, 4096]", arg492_1: "f32[14336, 4096]", arg493_1: "f32[4096, 14336]", arg494_1: "f32[14336, 4096]", arg495_1: "f32[4096]", arg496_1: "f32[4096]", arg497_1: "f32[4096, 4096]", arg498_1: "f32[1024, 4096]", arg499_1: "f32[1024, 4096]", arg500_1: "f32[4096, 4096]", arg501_1: "f32[8, 4096]", arg502_1: "f32[14336, 4096]", arg503_1: "f32[4096, 14336]", arg504_1: "f32[14336, 4096]", arg505_1: "f32[14336, 4096]", arg506_1: "f32[4096, 14336]", arg507_1: "f32[14336, 4096]", arg508_1: "f32[14336, 4096]", arg509_1: "f32[4096, 14336]", arg510_1: "f32[14336, 4096]", arg511_1: "f32[14336, 4096]", arg512_1: "f32[4096, 14336]", arg513_1: "f32[14336, 4096]", arg514_1: "f32[14336, 4096]", arg515_1: "f32[4096, 14336]", arg516_1: "f32[14336, 4096]", arg517_1: "f32[14336, 4096]", arg518_1: "f32[4096, 14336]", arg519_1: "f32[14336, 4096]", arg520_1: "f32[14336, 4096]", arg521_1: "f32[4096, 14336]", arg522_1: "f32[14336, 4096]", arg523_1: "f32[14336, 4096]", arg524_1: "f32[4096, 14336]", arg525_1: "f32[14336, 4096]", arg526_1: "f32[4096]", arg527_1: "f32[4096]", arg528_1: "f32[4096, 4096]", arg529_1: "f32[1024, 4096]", arg530_1: "f32[1024, 4096]", arg531_1: "f32[4096, 4096]", arg532_1: "f32[8, 4096]", arg533_1: "f32[14336, 4096]", arg534_1: "f32[4096, 14336]", arg535_1: "f32[14336, 4096]", arg536_1: "f32[14336, 4096]", arg537_1: "f32[4096, 14336]", arg538_1: "f32[14336, 4096]", arg539_1: "f32[14336, 4096]", arg540_1: "f32[4096, 14336]", arg541_1: "f32[14336, 4096]", arg542_1: "f32[14336, 4096]", arg543_1: "f32[4096, 14336]", arg544_1: "f32[14336, 4096]", arg545_1: "f32[14336, 4096]", arg546_1: "f32[4096, 14336]", arg547_1: "f32[14336, 4096]", arg548_1: "f32[14336, 4096]", arg549_1: "f32[4096, 14336]", arg550_1: "f32[14336, 4096]", arg551_1: "f32[14336, 4096]", arg552_1: "f32[4096, 14336]", arg553_1: "f32[14336, 4096]", arg554_1: "f32[14336, 4096]", arg555_1: "f32[4096, 14336]", arg556_1: "f32[14336, 4096]", arg557_1: "f32[4096]", arg558_1: "f32[4096]", arg559_1: "f32[4096, 4096]", arg560_1: "f32[1024, 4096]", arg561_1: "f32[1024, 4096]", arg562_1: "f32[4096, 4096]", arg563_1: "f32[8, 4096]", arg564_1: "f32[14336, 4096]", arg565_1: "f32[4096, 14336]", arg566_1: "f32[14336, 4096]", arg567_1: "f32[14336, 4096]", arg568_1: "f32[4096, 14336]", arg569_1: "f32[14336, 4096]", arg570_1: "f32[14336, 4096]", arg571_1: "f32[4096, 14336]", arg572_1: "f32[14336, 4096]", arg573_1: "f32[14336, 4096]", arg574_1: "f32[4096, 14336]", arg575_1: "f32[14336, 4096]", arg576_1: "f32[14336, 4096]", arg577_1: "f32[4096, 14336]", arg578_1: "f32[14336, 4096]", arg579_1: "f32[14336, 4096]", arg580_1: "f32[4096, 14336]", arg581_1: "f32[14336, 4096]", arg582_1: "f32[14336, 4096]", arg583_1: "f32[4096, 14336]", arg584_1: "f32[14336, 4096]", arg585_1: "f32[14336, 4096]", arg586_1: "f32[4096, 14336]", arg587_1: "f32[14336, 4096]", arg588_1: "f32[4096]", arg589_1: "f32[4096]", arg590_1: "f32[4096, 4096]", arg591_1: "f32[1024, 4096]", arg592_1: "f32[1024, 4096]", arg593_1: "f32[4096, 4096]", arg594_1: "f32[8, 4096]", arg595_1: "f32[14336, 4096]", arg596_1: "f32[4096, 14336]", arg597_1: "f32[14336, 4096]", arg598_1: "f32[14336, 4096]", arg599_1: "f32[4096, 14336]", arg600_1: "f32[14336, 4096]", arg601_1: "f32[14336, 4096]", arg602_1: "f32[4096, 14336]", arg603_1: "f32[14336, 4096]", arg604_1: "f32[14336, 4096]", arg605_1: "f32[4096, 14336]", arg606_1: "f32[14336, 4096]", arg607_1: "f32[14336, 4096]", arg608_1: "f32[4096, 14336]", arg609_1: "f32[14336, 4096]", arg610_1: "f32[14336, 4096]", arg611_1: "f32[4096, 14336]", arg612_1: "f32[14336, 4096]", arg613_1: "f32[14336, 4096]", arg614_1: "f32[4096, 14336]", arg615_1: "f32[14336, 4096]", arg616_1: "f32[14336, 4096]", arg617_1: "f32[4096, 14336]", arg618_1: "f32[14336, 4096]", arg619_1: "f32[4096]", arg620_1: "f32[4096]", arg621_1: "f32[4096, 4096]", arg622_1: "f32[1024, 4096]", arg623_1: "f32[1024, 4096]", arg624_1: "f32[4096, 4096]", arg625_1: "f32[8, 4096]", arg626_1: "f32[14336, 4096]", arg627_1: "f32[4096, 14336]", arg628_1: "f32[14336, 4096]", arg629_1: "f32[14336, 4096]", arg630_1: "f32[4096, 14336]", arg631_1: "f32[14336, 4096]", arg632_1: "f32[14336, 4096]", arg633_1: "f32[4096, 14336]", arg634_1: "f32[14336, 4096]", arg635_1: "f32[14336, 4096]", arg636_1: "f32[4096, 14336]", arg637_1: "f32[14336, 4096]", arg638_1: "f32[14336, 4096]", arg639_1: "f32[4096, 14336]", arg640_1: "f32[14336, 4096]", arg641_1: "f32[14336, 4096]", arg642_1: "f32[4096, 14336]", arg643_1: "f32[14336, 4096]", arg644_1: "f32[14336, 4096]", arg645_1: "f32[4096, 14336]", arg646_1: "f32[14336, 4096]", arg647_1: "f32[14336, 4096]", arg648_1: "f32[4096, 14336]", arg649_1: "f32[14336, 4096]", arg650_1: "f32[4096]", arg651_1: "f32[4096]", arg652_1: "f32[4096, 4096]", arg653_1: "f32[1024, 4096]", arg654_1: "f32[1024, 4096]", arg655_1: "f32[4096, 4096]", arg656_1: "f32[8, 4096]", arg657_1: "f32[14336, 4096]", arg658_1: "f32[4096, 14336]", arg659_1: "f32[14336, 4096]", arg660_1: "f32[14336, 4096]", arg661_1: "f32[4096, 14336]", arg662_1: "f32[14336, 4096]", arg663_1: "f32[14336, 4096]", arg664_1: "f32[4096, 14336]", arg665_1: "f32[14336, 4096]", arg666_1: "f32[14336, 4096]", arg667_1: "f32[4096, 14336]", arg668_1: "f32[14336, 4096]", arg669_1: "f32[14336, 4096]", arg670_1: "f32[4096, 14336]", arg671_1: "f32[14336, 4096]", arg672_1: "f32[14336, 4096]", arg673_1: "f32[4096, 14336]", arg674_1: "f32[14336, 4096]", arg675_1: "f32[14336, 4096]", arg676_1: "f32[4096, 14336]", arg677_1: "f32[14336, 4096]", arg678_1: "f32[14336, 4096]", arg679_1: "f32[4096, 14336]", arg680_1: "f32[14336, 4096]", arg681_1: "f32[4096]", arg682_1: "f32[4096]", arg683_1: "f32[4096, 4096]", arg684_1: "f32[1024, 4096]", arg685_1: "f32[1024, 4096]", arg686_1: "f32[4096, 4096]", arg687_1: "f32[8, 4096]", arg688_1: "f32[14336, 4096]", arg689_1: "f32[4096, 14336]", arg690_1: "f32[14336, 4096]", arg691_1: "f32[14336, 4096]", arg692_1: "f32[4096, 14336]", arg693_1: "f32[14336, 4096]", arg694_1: "f32[14336, 4096]", arg695_1: "f32[4096, 14336]", arg696_1: "f32[14336, 4096]", arg697_1: "f32[14336, 4096]", arg698_1: "f32[4096, 14336]", arg699_1: "f32[14336, 4096]", arg700_1: "f32[14336, 4096]", arg701_1: "f32[4096, 14336]", arg702_1: "f32[14336, 4096]", arg703_1: "f32[14336, 4096]", arg704_1: "f32[4096, 14336]", arg705_1: "f32[14336, 4096]", arg706_1: "f32[14336, 4096]", arg707_1: "f32[4096, 14336]", arg708_1: "f32[14336, 4096]", arg709_1: "f32[14336, 4096]", arg710_1: "f32[4096, 14336]", arg711_1: "f32[14336, 4096]", arg712_1: "f32[4096]", arg713_1: "f32[4096]", arg714_1: "f32[4096, 4096]", arg715_1: "f32[1024, 4096]", arg716_1: "f32[1024, 4096]", arg717_1: "f32[4096, 4096]", arg718_1: "f32[8, 4096]", arg719_1: "f32[14336, 4096]", arg720_1: "f32[4096, 14336]", arg721_1: "f32[14336, 4096]", arg722_1: "f32[14336, 4096]", arg723_1: "f32[4096, 14336]", arg724_1: "f32[14336, 4096]", arg725_1: "f32[14336, 4096]", arg726_1: "f32[4096, 14336]", arg727_1: "f32[14336, 4096]", arg728_1: "f32[14336, 4096]", arg729_1: "f32[4096, 14336]", arg730_1: "f32[14336, 4096]", arg731_1: "f32[14336, 4096]", arg732_1: "f32[4096, 14336]", arg733_1: "f32[14336, 4096]", arg734_1: "f32[14336, 4096]", arg735_1: "f32[4096, 14336]", arg736_1: "f32[14336, 4096]", arg737_1: "f32[14336, 4096]", arg738_1: "f32[4096, 14336]", arg739_1: "f32[14336, 4096]", arg740_1: "f32[14336, 4096]", arg741_1: "f32[4096, 14336]", arg742_1: "f32[14336, 4096]", arg743_1: "f32[4096]", arg744_1: "f32[4096]", arg745_1: "f32[4096, 4096]", arg746_1: "f32[1024, 4096]", arg747_1: "f32[1024, 4096]", arg748_1: "f32[4096, 4096]", arg749_1: "f32[8, 4096]", arg750_1: "f32[14336, 4096]", arg751_1: "f32[4096, 14336]", arg752_1: "f32[14336, 4096]", arg753_1: "f32[14336, 4096]", arg754_1: "f32[4096, 14336]", arg755_1: "f32[14336, 4096]", arg756_1: "f32[14336, 4096]", arg757_1: "f32[4096, 14336]", arg758_1: "f32[14336, 4096]", arg759_1: "f32[14336, 4096]", arg760_1: "f32[4096, 14336]", arg761_1: "f32[14336, 4096]", arg762_1: "f32[14336, 4096]", arg763_1: "f32[4096, 14336]", arg764_1: "f32[14336, 4096]", arg765_1: "f32[14336, 4096]", arg766_1: "f32[4096, 14336]", arg767_1: "f32[14336, 4096]", arg768_1: "f32[14336, 4096]", arg769_1: "f32[4096, 14336]", arg770_1: "f32[14336, 4096]", arg771_1: "f32[14336, 4096]", arg772_1: "f32[4096, 14336]", arg773_1: "f32[14336, 4096]", arg774_1: "f32[4096]", arg775_1: "f32[4096]", arg776_1: "f32[4096, 4096]", arg777_1: "f32[1024, 4096]", arg778_1: "f32[1024, 4096]", arg779_1: "f32[4096, 4096]", arg780_1: "f32[8, 4096]", arg781_1: "f32[14336, 4096]", arg782_1: "f32[4096, 14336]", arg783_1: "f32[14336, 4096]", arg784_1: "f32[14336, 4096]", arg785_1: "f32[4096, 14336]", arg786_1: "f32[14336, 4096]", arg787_1: "f32[14336, 4096]", arg788_1: "f32[4096, 14336]", arg789_1: "f32[14336, 4096]", arg790_1: "f32[14336, 4096]", arg791_1: "f32[4096, 14336]", arg792_1: "f32[14336, 4096]", arg793_1: "f32[14336, 4096]", arg794_1: "f32[4096, 14336]", arg795_1: "f32[14336, 4096]", arg796_1: "f32[14336, 4096]", arg797_1: "f32[4096, 14336]", arg798_1: "f32[14336, 4096]", arg799_1: "f32[14336, 4096]", arg800_1: "f32[4096, 14336]", arg801_1: "f32[14336, 4096]", arg802_1: "f32[14336, 4096]", arg803_1: "f32[4096, 14336]", arg804_1: "f32[14336, 4096]", arg805_1: "f32[4096]", arg806_1: "f32[4096]", arg807_1: "f32[4096, 4096]", arg808_1: "f32[1024, 4096]", arg809_1: "f32[1024, 4096]", arg810_1: "f32[4096, 4096]", arg811_1: "f32[8, 4096]", arg812_1: "f32[14336, 4096]", arg813_1: "f32[4096, 14336]", arg814_1: "f32[14336, 4096]", arg815_1: "f32[14336, 4096]", arg816_1: "f32[4096, 14336]", arg817_1: "f32[14336, 4096]", arg818_1: "f32[14336, 4096]", arg819_1: "f32[4096, 14336]", arg820_1: "f32[14336, 4096]", arg821_1: "f32[14336, 4096]", arg822_1: "f32[4096, 14336]", arg823_1: "f32[14336, 4096]", arg824_1: "f32[14336, 4096]", arg825_1: "f32[4096, 14336]", arg826_1: "f32[14336, 4096]", arg827_1: "f32[14336, 4096]", arg828_1: "f32[4096, 14336]", arg829_1: "f32[14336, 4096]", arg830_1: "f32[14336, 4096]", arg831_1: "f32[4096, 14336]", arg832_1: "f32[14336, 4096]", arg833_1: "f32[14336, 4096]", arg834_1: "f32[4096, 14336]", arg835_1: "f32[14336, 4096]", arg836_1: "f32[4096]", arg837_1: "f32[4096]", arg838_1: "f32[4096, 4096]", arg839_1: "f32[1024, 4096]", arg840_1: "f32[1024, 4096]", arg841_1: "f32[4096, 4096]", arg842_1: "f32[8, 4096]", arg843_1: "f32[14336, 4096]", arg844_1: "f32[4096, 14336]", arg845_1: "f32[14336, 4096]", arg846_1: "f32[14336, 4096]", arg847_1: "f32[4096, 14336]", arg848_1: "f32[14336, 4096]", arg849_1: "f32[14336, 4096]", arg850_1: "f32[4096, 14336]", arg851_1: "f32[14336, 4096]", arg852_1: "f32[14336, 4096]", arg853_1: "f32[4096, 14336]", arg854_1: "f32[14336, 4096]", arg855_1: "f32[14336, 4096]", arg856_1: "f32[4096, 14336]", arg857_1: "f32[14336, 4096]", arg858_1: "f32[14336, 4096]", arg859_1: "f32[4096, 14336]", arg860_1: "f32[14336, 4096]", arg861_1: "f32[14336, 4096]", arg862_1: "f32[4096, 14336]", arg863_1: "f32[14336, 4096]", arg864_1: "f32[14336, 4096]", arg865_1: "f32[4096, 14336]", arg866_1: "f32[14336, 4096]", arg867_1: "f32[4096]", arg868_1: "f32[4096]", arg869_1: "f32[4096, 4096]", arg870_1: "f32[1024, 4096]", arg871_1: "f32[1024, 4096]", arg872_1: "f32[4096, 4096]", arg873_1: "f32[8, 4096]", arg874_1: "f32[14336, 4096]", arg875_1: "f32[4096, 14336]", arg876_1: "f32[14336, 4096]", arg877_1: "f32[14336, 4096]", arg878_1: "f32[4096, 14336]", arg879_1: "f32[14336, 4096]", arg880_1: "f32[14336, 4096]", arg881_1: "f32[4096, 14336]", arg882_1: "f32[14336, 4096]", arg883_1: "f32[14336, 4096]", arg884_1: "f32[4096, 14336]", arg885_1: "f32[14336, 4096]", arg886_1: "f32[14336, 4096]", arg887_1: "f32[4096, 14336]", arg888_1: "f32[14336, 4096]", arg889_1: "f32[14336, 4096]", arg890_1: "f32[4096, 14336]", arg891_1: "f32[14336, 4096]", arg892_1: "f32[14336, 4096]", arg893_1: "f32[4096, 14336]", arg894_1: "f32[14336, 4096]", arg895_1: "f32[14336, 4096]", arg896_1: "f32[4096, 14336]", arg897_1: "f32[14336, 4096]", arg898_1: "f32[4096]", arg899_1: "f32[4096]", arg900_1: "f32[4096, 4096]", arg901_1: "f32[1024, 4096]", arg902_1: "f32[1024, 4096]", arg903_1: "f32[4096, 4096]", arg904_1: "f32[8, 4096]", arg905_1: "f32[14336, 4096]", arg906_1: "f32[4096, 14336]", arg907_1: "f32[14336, 4096]", arg908_1: "f32[14336, 4096]", arg909_1: "f32[4096, 14336]", arg910_1: "f32[14336, 4096]", arg911_1: "f32[14336, 4096]", arg912_1: "f32[4096, 14336]", arg913_1: "f32[14336, 4096]", arg914_1: "f32[14336, 4096]", arg915_1: "f32[4096, 14336]", arg916_1: "f32[14336, 4096]", arg917_1: "f32[14336, 4096]", arg918_1: "f32[4096, 14336]", arg919_1: "f32[14336, 4096]", arg920_1: "f32[14336, 4096]", arg921_1: "f32[4096, 14336]", arg922_1: "f32[14336, 4096]", arg923_1: "f32[14336, 4096]", arg924_1: "f32[4096, 14336]", arg925_1: "f32[14336, 4096]", arg926_1: "f32[14336, 4096]", arg927_1: "f32[4096, 14336]", arg928_1: "f32[14336, 4096]", arg929_1: "f32[4096]", arg930_1: "f32[4096]", arg931_1: "f32[4096, 4096]", arg932_1: "f32[1024, 4096]", arg933_1: "f32[1024, 4096]", arg934_1: "f32[4096, 4096]", arg935_1: "f32[8, 4096]", arg936_1: "f32[14336, 4096]", arg937_1: "f32[4096, 14336]", arg938_1: "f32[14336, 4096]", arg939_1: "f32[14336, 4096]", arg940_1: "f32[4096, 14336]", arg941_1: "f32[14336, 4096]", arg942_1: "f32[14336, 4096]", arg943_1: "f32[4096, 14336]", arg944_1: "f32[14336, 4096]", arg945_1: "f32[14336, 4096]", arg946_1: "f32[4096, 14336]", arg947_1: "f32[14336, 4096]", arg948_1: "f32[14336, 4096]", arg949_1: "f32[4096, 14336]", arg950_1: "f32[14336, 4096]", arg951_1: "f32[14336, 4096]", arg952_1: "f32[4096, 14336]", arg953_1: "f32[14336, 4096]", arg954_1: "f32[14336, 4096]", arg955_1: "f32[4096, 14336]", arg956_1: "f32[14336, 4096]", arg957_1: "f32[14336, 4096]", arg958_1: "f32[4096, 14336]", arg959_1: "f32[14336, 4096]", arg960_1: "f32[4096]", arg961_1: "f32[4096]", arg962_1: "f32[4096, 4096]", arg963_1: "f32[1024, 4096]", arg964_1: "f32[1024, 4096]", arg965_1: "f32[4096, 4096]", arg966_1: "f32[8, 4096]", arg967_1: "f32[14336, 4096]", arg968_1: "f32[4096, 14336]", arg969_1: "f32[14336, 4096]", arg970_1: "f32[14336, 4096]", arg971_1: "f32[4096, 14336]", arg972_1: "f32[14336, 4096]", arg973_1: "f32[14336, 4096]", arg974_1: "f32[4096, 14336]", arg975_1: "f32[14336, 4096]", arg976_1: "f32[14336, 4096]", arg977_1: "f32[4096, 14336]", arg978_1: "f32[14336, 4096]", arg979_1: "f32[14336, 4096]", arg980_1: "f32[4096, 14336]", arg981_1: "f32[14336, 4096]", arg982_1: "f32[14336, 4096]", arg983_1: "f32[4096, 14336]", arg984_1: "f32[14336, 4096]", arg985_1: "f32[14336, 4096]", arg986_1: "f32[4096, 14336]", arg987_1: "f32[14336, 4096]", arg988_1: "f32[14336, 4096]", arg989_1: "f32[4096, 14336]", arg990_1: "f32[14336, 4096]", arg991_1: "f32[4096]", arg992_1: "f32[4096]", arg993_1: "f32[4096]", arg994_1: "f32[32000, 4096]", arg995_1: "f32[64]", arg996_1: "i32[2, 4]", arg997_1: "i32[2, 4]"):
# File: /usr/local/lib/python3.12/dist-packages/torch/nn/modules/sparse.py:190 in forward, code: return F.embedding(
embedding: "f32[2, 4, 4096]" = torch.ops.aten.embedding.default(arg0_1, arg996_1); arg0_1 = arg996_1 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:622 in forward, code: cache_position = torch.arange(
arange: "i64[4]" = torch.ops.aten.arange.start(0, 4, device = device(type='meta'), pin_memory = False)
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:626 in forward, code: position_ids = cache_position.unsqueeze(0)
unsqueeze: "i64[1, 4]" = torch.ops.aten.unsqueeze.default(arange, 0)
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:628 in forward, code: causal_mask = self._update_causal_mask(
full: "f32[4, 4]" = torch.ops.aten.full.default([4, 4], -3.4028234663852886e+38, dtype = torch.float32, device = device(type='meta'), pin_memory = False)
arange_1: "i64[4]" = torch.ops.aten.arange.default(4, device = device(type='meta'), pin_memory = False)
reshape: "i64[4, 1]" = torch.ops.aten.reshape.default(arange, [-1, 1]); arange = None
gt: "b8[4, 4]" = torch.ops.aten.gt.Tensor(arange_1, reshape); arange_1 = reshape = None
mul_: "f32[4, 4]" = torch.ops.aten.mul_.Tensor(full, gt); full = gt = None
unsqueeze_1: "f32[1, 4, 4]" = torch.ops.aten.unsqueeze.default(mul_, 0); mul_ = None
unsqueeze_2: "f32[1, 1, 4, 4]" = torch.ops.aten.unsqueeze.default(unsqueeze_1, 1); unsqueeze_1 = None
slice_1: "f32[1, 1, 4, 4]" = torch.ops.aten.slice.Tensor(unsqueeze_2, 2, 0, 9223372036854775807); unsqueeze_2 = None
slice_2: "f32[1, 1, 4, 4]" = torch.ops.aten.slice.Tensor(slice_1, 3, 0, 9223372036854775807); slice_1 = None
expand: "f32[2, 1, 4, 4]" = torch.ops.aten.expand.default(slice_2, [2, 1, -1, -1]); slice_2 = None
clone: "f32[2, 1, 4, 4]" = torch.ops.aten.clone.default(expand); expand = None
slice_3: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807)
slice_4: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(slice_3, 1, 0, 9223372036854775807); slice_3 = None
slice_5: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(slice_4, 2, 0, 9223372036854775807); slice_4 = None
slice_6: "i32[2, 4]" = torch.ops.aten.slice.Tensor(arg997_1, 0, 0, 9223372036854775807); arg997_1 = None
unsqueeze_3: "i32[2, 1, 4]" = torch.ops.aten.unsqueeze.default(slice_6, 1); slice_6 = None
unsqueeze_4: "i32[2, 1, 1, 4]" = torch.ops.aten.unsqueeze.default(unsqueeze_3, 2); unsqueeze_3 = None
slice_7: "i32[2, 1, 1, 4]" = torch.ops.aten.slice.Tensor(unsqueeze_4, 3, 0, 9223372036854775807); unsqueeze_4 = None
to: "i32[2, 1, 1, 4]" = torch.ops.aten.to.dtype_layout(slice_7, dtype = torch.int32, layout = torch.strided, device = device(type='meta')); slice_7 = None
add: "f32[2, 1, 4, 4]" = torch.ops.aten.add.Tensor(slice_5, to); slice_5 = to = None
eq: "b8[2, 1, 4, 4]" = torch.ops.aten.eq.Scalar(add, 0); add = None
slice_8: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807)
slice_9: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(slice_8, 1, 0, 9223372036854775807); slice_8 = None
slice_10: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(slice_9, 2, 0, 9223372036854775807); slice_9 = None
masked_fill: "f32[2, 1, 4, 4]" = torch.ops.aten.masked_fill.Scalar(slice_10, eq, -3.4028234663852886e+38); slice_10 = eq = None
slice_11: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(clone, 0, 0, 9223372036854775807)
slice_12: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(slice_11, 1, 0, 9223372036854775807); slice_11 = None
slice_13: "f32[2, 1, 4, 4]" = torch.ops.aten.slice.Tensor(slice_12, 2, 0, 9223372036854775807); slice_12 = None
copy_: "f32[2, 1, 4, 4]" = torch.ops.aten.copy_.default(slice_13, masked_fill); slice_13 = masked_fill = copy_ = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:635 in forward, code: position_embeddings = self.rotary_emb(hidden_states, position_ids)
_set_grad_enabled = torch._C._set_grad_enabled(False); _set_grad_enabled = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:418 in forward, code: inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
unsqueeze_5: "f32[1, 64]" = torch.ops.aten.unsqueeze.default(arg995_1, 0); arg995_1 = None
slice_14: "f32[1, 64]" = torch.ops.aten.slice.Tensor(unsqueeze_5, 1, 0, 9223372036854775807); unsqueeze_5 = None
unsqueeze_6: "f32[1, 64, 1]" = torch.ops.aten.unsqueeze.default(slice_14, 2); slice_14 = None
to_1: "f32[1, 64, 1]" = torch.ops.aten.to.dtype(unsqueeze_6, torch.float32); unsqueeze_6 = None
expand_1: "f32[1, 64, 1]" = torch.ops.aten.expand.default(to_1, [1, -1, 1]); to_1 = None
to_2: "f32[1, 64, 1]" = torch.ops.aten.to.dtype_layout(expand_1, dtype = torch.float32, layout = torch.strided, device = device(type='meta')); expand_1 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:419 in forward, code: position_ids_expanded = position_ids[:, None, :].float()
slice_15: "i64[1, 4]" = torch.ops.aten.slice.Tensor(unsqueeze, 0, 0, 9223372036854775807); unsqueeze = None
unsqueeze_7: "i64[1, 1, 4]" = torch.ops.aten.unsqueeze.default(slice_15, 1); slice_15 = None
slice_16: "i64[1, 1, 4]" = torch.ops.aten.slice.Tensor(unsqueeze_7, 2, 0, 9223372036854775807); unsqueeze_7 = None
to_3: "f32[1, 1, 4]" = torch.ops.aten.to.dtype(slice_16, torch.float32); slice_16 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:423 in forward, code: freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
to_4: "f32[1, 64, 1]" = torch.ops.aten.to.dtype(to_2, torch.float32); to_2 = None
to_5: "f32[1, 1, 4]" = torch.ops.aten.to.dtype(to_3, torch.float32); to_3 = None
matmul: "f32[1, 64, 4]" = torch.ops.aten.matmul.default(to_4, to_5); to_4 = to_5 = None
transpose: "f32[1, 4, 64]" = torch.ops.aten.transpose.int(matmul, 1, 2); matmul = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:424 in forward, code: emb = torch.cat((freqs, freqs), dim=-1)
cat: "f32[1, 4, 128]" = torch.ops.aten.cat.default([transpose, transpose], -1); transpose = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:425 in forward, code: cos = emb.cos() * self.attention_scaling
cos: "f32[1, 4, 128]" = torch.ops.aten.cos.default(cat)
mul: "f32[1, 4, 128]" = torch.ops.aten.mul.Tensor(cos, 1.0); cos = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:426 in forward, code: sin = emb.sin() * self.attention_scaling
sin: "f32[1, 4, 128]" = torch.ops.aten.sin.default(cat); cat = None
mul_1: "f32[1, 4, 128]" = torch.ops.aten.mul.Tensor(sin, 1.0); sin = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:428 in forward, code: return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
to_6: "f32[1, 4, 128]" = torch.ops.aten.to.dtype(mul, torch.float32); mul = None
to_7: "f32[1, 4, 128]" = torch.ops.aten.to.dtype(mul_1, torch.float32); mul_1 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:635 in forward, code: position_embeddings = self.rotary_emb(hidden_states, position_ids)
_set_grad_enabled_1 = torch._C._set_grad_enabled(True); _set_grad_enabled_1 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:167 in forward, code: hidden_states = hidden_states.to(torch.float32)
to_8: "f32[2, 4, 4096]" = torch.ops.aten.to.dtype(embedding, torch.float32); embedding = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:168 in forward, code: variance = hidden_states.pow(2).mean(-1, keepdim=True)
pow_1: "f32[2, 4, 4096]" = torch.ops.aten.pow.Tensor_Scalar(to_8, 2)
mean: "f32[2, 4, 1]" = torch.ops.aten.mean.dim(pow_1, [-1], True); pow_1 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:169 in forward, code: hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
add_1: "f32[2, 4, 1]" = torch.ops.aten.add.Tensor(mean, 1e-05); mean = None
rsqrt: "f32[2, 4, 1]" = torch.ops.aten.rsqrt.default(add_1); add_1 = None
mul_2: "f32[2, 4, 4096]" = torch.ops.aten.mul.Tensor(to_8, rsqrt); rsqrt = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:170 in forward, code: return self.weight * hidden_states.to(input_dtype)
to_9: "f32[2, 4, 4096]" = torch.ops.aten.to.dtype(mul_2, torch.float32); mul_2 = None
mul_3: "f32[2, 4, 4096]" = torch.ops.aten.mul.Tensor(arg30_1, to_9); arg30_1 = to_9 = None
# File: /usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear: "f32[2, 4, 4096]" = torch.ops.aten.linear.default(mul_3, arg1_1); arg1_1 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:277 in forward, code: query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view: "f32[2, 4, 32, 128]" = torch.ops.aten.view.default(linear, [2, 4, -1, 128]); linear = None
transpose_1: "f32[2, 32, 4, 128]" = torch.ops.aten.transpose.int(view, 1, 2); view = None
# File: /usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_1: "f32[2, 4, 1024]" = torch.ops.aten.linear.default(mul_3, arg2_1); arg2_1 = None
# File: /home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py:278 in forward, code: key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
view_1: "f32[2, 4, 8, 128]" = torch.ops.aten.view.default(linear_1, [2, 4, -1, 128]); linear_1 = None
transpose_2: "f32[2, 8, 4, 128]" = torch.ops.aten.transpose.int(view_1, 1, 2); view_1 = None
# File: /usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py:125 in forward, code: return F.linear(input, self.weight, self.bias)
linear_2: "f32[2, 4, 1024]" = torch.ops.aten.linear.default(mul_3, arg3_1); mul_3 = arg3_1 = None
......
I
......
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 1379, in __torch_dispatch__
return proxy_call(self, func, self.pre_dispatch, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/proxy_tensor.py", line 914, in proxy_call
out = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 756, in __call__
return self._op(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/utils/_stats.py", line 27, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 1282, in __torch_dispatch__
return self.dispatch(func, types, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 1823, in dispatch
return self._cached_dispatch_impl(func, types, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 1393, in _cached_dispatch_impl
output = self._dispatch_impl(func, types, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 2333, in _dispatch_impl
decomposition_table[func](*args, **kwargs)
File "/usr/local/lib/python3.12/dist-packages/torch/_refs/__init__.py", line 4002, in unbind
torch.squeeze(s, dim) for s in torch.tensor_split(t, t.shape[dim], dim)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/utils/_stats.py", line 27, in wrapper
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 1282, in __torch_dispatch__
return self.dispatch(func, types, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 1823, in dispatch
return self._cached_dispatch_impl(func, types, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 1393, in _cached_dispatch_impl
output = self._dispatch_impl(func, types, args, kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_subclasses/fake_tensor.py", line 2338, in _dispatch_impl
r = func.decompose(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/_ops.py", line 799, in decompose
return self._op_dk(dk, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/sym_node.py", line 500, in guard_int
r = self.evaluate()
^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/sym_node.py", line 494, in evaluate
return self.shape_env.evaluate_sym_node(self, size_oblivious)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/symbolic_shapes.py", line 6637, in evaluate_sym_node
return self.evaluate_expr(
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/recording.py", line 263, in wrapper
return retlog(fn(*args, **kwargs))
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/symbolic_shapes.py", line 6653, in evaluate_expr
return self._evaluate_expr(
^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/torch/fx/experimental/symbolic_shapes.py", line 6870, in _evaluate_expr
raise self._make_data_dependent_error(
torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode: Could not extract specialized integer from data-dependent expression u0 (unhinted: u0). (Size-like symbols: u0)
Caused by: (_ops.py:799 in decompose)
For more information, run with TORCH_LOGS="dynamic"
For extended logs when we create symbols, also add TORCHDYNAMO_EXTENDED_DEBUG_CREATE_SYMBOL="u0"
If you suspect the guard was triggered from C++, add TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
For more debugging help, see https://docs.google.com/document/d/1HSuTTVvYH1pTew89Rtpeu84Ht3nQEFTYhAX3Ypa_xJs/edit?usp=sharing
For C++ stack trace, run with TORCHDYNAMO_EXTENDED_DEBUG_CPP=1
The following call raised this error:
File "/home/guomingz/.local/lib/python3.12/site-packages/transformers/models/mixtral/modeling_mixtral.py", line 138, in forward
expert_hitted = (expert_mask.sum(dim=(-1, -2)) > 0).nonzero(as_tuple=True)[0].tolist()
```
os: linux
transformer version: 4.52.4
torch version: 2.7
gpu: Nvidia H100
cuda: 12.9
driver: 575.57.08
### Who can help?
@ArthurZucker @Coco58323 @Cyrilvallez
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. pip install transformers==4.52.4
2. run below code snippet
```python
import transformers
import torch.export as te
import torch
from contextlib import nullcontext
torch.autocast = lambda *args, **kwargs: nullcontext()
mixtral = transformers.AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1",
device_map="meta",
)
ep = te.export(mixtral,
args=(torch.randint(0, 100, (2, 4),device="meta", dtype=torch.int32),
torch.randint(0, 100, (2, 4),device="meta", dtype=torch.int32)
),
kwargs={}, strict=False
).module()
```
### Expected behavior
The above code snippets could run successfully. | null | {
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] | closed | false | null | [] | null | [] | 2025-06-01T17:30:57 | 2025-07-16T08:02:53 | 2025-07-16T08:02:53 | NONE | null | null | null | null | ### System Info
File "/anaconda/envs/openrlhf/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py", line 767, in __getitem__
model_type = self._reverse_config_mapping[key.__name__]
KeyError: 'Qwen2RMConfig'
transformers version is 4.51.3
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
we use the lastest version of openrlhf、vllm、transformers
### Expected behavior
Can you merge QwenRMconfig into the lastest version of transformers
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https://api.github.com/repos/huggingface/transformers/issues/38516 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38516/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38516/comments | https://api.github.com/repos/huggingface/transformers/issues/38516/events | https://github.com/huggingface/transformers/issues/38516 | 3,107,066,987 | I_kwDOCUB6oc65MhRr | 38,516 | Hang in quantized_phi::ModelWeights::forward() with Phi-2 GGUF on CPU (Candle main branch) | {
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Description:
The program hangs indefinitely when calling the forward() method on a quantized_phi::ModelWeights instance loaded from a Phi-2 GGUF file. This occurs during the first iteration of token generation, specifically after printing a log message "Calling model.forward()..." and before any indication that the forward() call has completed. The test is performed using a minimal, standalone Candle program on CPU.
Environment:
Candle Version: main branch (commit https://github.com/huggingface/candle/commit/0224a749f0b2082f19831256ced6afe284c56457 as per user's cargo build log for the test project)
candle-core v0.9.1 (https://github.com/huggingface/candle.git?branch=main#0224a749)
candle-nn v0.9.1 (https://github.com/huggingface/candle.git?branch=main#0224a749)
candle-transformers v0.9.1 (https://github.com/huggingface/candle.git?branch=main#0224a749)
OS: Windows 11 Pro
CPU: Intel(R) Core(TM) i5-9400 CPU @ 2.90GHz 2.90 GHz
Rust Version: rustc 1.87.0
Target: CPU (explicitly set Device::Cpu)
SIMD Features Reported by Test: avx: false, neon: false, simd128: false, f16c: false
Model and Tokenizer Information:
Model Type: candle_transformers::models::quantized_phi::ModelWeights
GGUF File: phi-2.Q4_K_M.gguf
Source: Downloaded from a Hugging Face Hub repository. (User: Please specify which exact repository if possible when submitting, e.g., "TheBloke/phi-2-GGUF" or "microsoft/phi-2".)
Tokenizer File: tokenizer.json (corresponding to Phi-2)
Source: Downloaded from a Hugging Face Hub repository. (User: Please specify which exact repository if possible, e.g., from "microsoft/phi-2" or the same repo as the GGUF.)
Minimal Reproducible Example (MRE):
Project Name: candle_phi2_test
Cargo.toml:
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Create a new Rust project: cargo new candle_phi2_test_report
Replace candle_phi2_test_report/Cargo.toml with the Cargo.toml content above.
Replace candle_phi2_test_report/src/main.rs with the src/main.rs content above.
Important for Candle Team: Obtain phi-2.Q4_K_M.gguf and a corresponding tokenizer.json from a Hugging Face Hub repository (e.g., "TheBloke/phi-2-GGUF" for the model, "microsoft/phi-2" for the tokenizer). Place them in a location accessible by the paths specified in model_path_str and tokenizer_path_str in src/main.rs (or update the paths in the code).
Run cargo build.
Run cargo run.
Observed Behavior:
The program successfully loads the model and tokenizer. It encodes the prompt and enters the generation loop. In the first iteration of the loop, the following console output is observed before the program hangs:
Starting standalone Candle Phi-2 GGUF test...
avx: false, neon: false, simd128: false, f16c: false
Device: CPU
Loading tokenizer from: C:/Users/Admin/Projects/mobiunt/backend/models/phi-2/tokenizer.json
Tokenizer loaded successfully.
Loading model from: C:/Users/Admin/Projects/mobiunt/backend/models/phi-2/phi-2.Q4_K_M.gguf
GGUF content read. Attempting to load model weights...
Model weights loaded successfully.
Encoding prompt: 'Rephrase this: What is the capital of France?'
Prompt encoded into 12 tokens: [6207, 11840, 589, 428, 25, 1867, 318, 262, 3139, 286, 4881, 30]
EOS token ID used for stopping: Some(50256) (Note: u32::MAX means no specific EOS token was found with common names)
Starting generation (max_new_tokens: 5)...
Iteration 1/5: start_pos=0, context_size=12, input_tokens_slice_len=12
Iteration 1: Input tensor shape: [1, 12]
Iteration 1: Calling model.forward()...
After printing "Calling model.forward()...", the program becomes unresponsive and hangs indefinitely. No further log messages are printed, and the program does not exit or panic.
### Expected behavior
The model.forward() call should complete. The program should then proceed to sample a token, print it, and either complete max_new_tokens (5) iterations or stop if an EOS token is generated. It should then print the "Generation Complete" summary and exit cleanly.
Additional Notes:
The model variable in the MRE is declared as mut model. This was based on previous debugging in a more complex Axum application where compiler errors (E0596) suggested ModelWeights::forward required &mut self. The same hang occurs whether model is mut or not in this standalone test, as long as the forward call is made.
The issue is observed on CPU. GPU capabilities were not explicitly tested but are not expected to be a factor given the CPU target and SIMD features reported. | {
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} | - Adds new fast image processor for zoedepth, see #36978
- Expands tests accordingly
potential reviewer: @yonigozlan
| {
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https://api.github.com/repos/huggingface/transformers/issues/38514 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38514/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38514/comments | https://api.github.com/repos/huggingface/transformers/issues/38514/events | https://github.com/huggingface/transformers/issues/38514 | 3,106,975,261 | I_kwDOCUB6oc65MK4d | 38,514 | Can not reproduce Blip2ForImageTextRetrieval example from docs, getting different results | {
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] | closed | false | null | [] | null | [] | 2025-06-01T09:52:40 | 2025-06-17T07:19:48 | 2025-06-17T07:19:48 | NONE | null | null | null | null | ### System Info
- `transformers` version: 4.52.4
- Platform: Linux-4.4.0-x86_64-with-glibc2.36
- Python version: 3.12.6
- Huggingface_hub version: 0.32.3
- Safetensors version: 0.5.3
- Accelerate version: not installed
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.7.0+cu126 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: Tesla T4
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
I'm trying to run Blip2ForImageTextRetrieval on modal infrastructure, and it produces very inaccurate results. In short, when I would expect to see a high "is" score, I get either very low or, at best, close to 0.5.
To debug, I tried to reproduce the [example from docs](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForImageTextRetrieval)
```python
import modal
app = modal.App(name="blip-itm")
image = (modal.Image.debian_slim()
.pip_install("torch", "transformers", "pillow", "requests")
)
@app.function(image=image, gpu="T4")
def official_demo(self):
import torch
from PIL import Image
import requests
from transformers import AutoProcessor, Blip2ForImageTextRetrieval
device = "cuda" if torch.cuda.is_available() else "cpu"
model = Blip2ForImageTextRetrieval.from_pretrained("Salesforce/blip2-itm-vit-g", torch_dtype=torch.float16)
processor = AutoProcessor.from_pretrained("Salesforce/blip2-itm-vit-g")
model.to(device)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "two cats laying on a pink blanket"
inputs = processor(images=image, text=text, return_tensors="pt").to(device, torch.float16)
with torch.cuda.amp.autocast():
itm_out = model(**inputs, use_image_text_matching_head=True)
logits_per_image = torch.nn.functional.softmax(itm_out.logits_per_image, dim=1)
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
print(f"{probs[0][0]:.1%} that image 0 is not '{text}'")
print(f"{probs[0][1]:.1%} that image 0 is '{text}'")
texts = ["a photo of a cat", "a photo of a dog"]
inputs = processor(images=image, text=texts, return_tensors="pt").to(device, torch.float16)
with torch.cuda.amp.autocast():
itc_out = model(**inputs, use_image_text_matching_head=False)
logits_per_image = itc_out.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'")
print(f"{probs[0][1]:.1%} that image 0 is '{texts[1]}'")
@app.local_entrypoint()
def main():
official_demo.remote()
```
However the output is
```
49.1% that image 0 is not 'two cats laying on a pink blanket'
50.9% that image 0 is 'two cats laying on a pink blanket'
49.9% that image 0 is 'a photo of a cat'
50.1% that image 0 is 'a photo of a dog'
```
Which is inaccurate and way of from what docs example state.
Also, I'm getting
```
RuntimeError: expected scalar type Half but found Float
```
but I resolved it by explicitly autocasting (it's the only difference in my code from the docs example)
### Expected behavior
The output when running official docs sample code should be
```
26.9% that image 0 is not 'two cats laying on a pink blanket'
73.0% that image 0 is 'two cats laying on a pink blanket'
55.3% that image 0 is 'a photo of a cat'
44.7% that image 0 is 'a photo of a dog'
``` | {
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https://api.github.com/repos/huggingface/transformers/issues/38513 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38513/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38513/comments | https://api.github.com/repos/huggingface/transformers/issues/38513/events | https://github.com/huggingface/transformers/pull/38513 | 3,106,924,312 | PR_kwDOCUB6oc6Ydoib | 38,513 | Update blip model card | {
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"merged_at": "2025-06-20T20:46:19"
} | # What does this PR do?
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<!-- Remove if not applicable -->
I have changed the documentation of the [blip](https://huggingface.co/docs/transformers/v4.52.1/en/model_doc/blip) model according to the following issue https://github.com/huggingface/transformers/issues/36979#issue-2947704577.
- [x] Standardize model card with a consistent format
- [x] Provide code examples featuring the `Pipeline`, `AutoModel`(not `transformers-cli`).
- [x] Attention mask visualizer supporting --> It seems not to be supported in blip.
Because it is the first contribution, it may be insufficient. If correction or supplementation is needed, please feel free to comment at any time.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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- vision models: @amyeroberts, @qubvel
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https://api.github.com/repos/huggingface/transformers/issues/38512 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38512/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38512/comments | https://api.github.com/repos/huggingface/transformers/issues/38512/events | https://github.com/huggingface/transformers/pull/38512 | 3,106,765,084 | PR_kwDOCUB6oc6YdH-G | 38,512 | Fix initialization of a pretrained backbone | {
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} | # What does this PR do?
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Fixes #38061
The backbone is initialized only once when `use_pretrained_backbone=True`, that means `_is_hf_initialized=True`, so we should do nothing in the `_initialize_weights` function when `_is_hf_initialized=True`.
Remove the redundant line `and backbone_checkpoint is None`.
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@Rocketknight1 @qubvel @NielsRogge
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} | # What does this PR do?
🎉 🎉 🎉
I added comments on the changes in `Files changed`
For the list of failing tests on current main, see
https://github.com/huggingface/transformers/actions/runs/15382744712/job/43276231818 | {
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https://api.github.com/repos/huggingface/transformers/issues/38509 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38509/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38509/comments | https://api.github.com/repos/huggingface/transformers/issues/38509/events | https://github.com/huggingface/transformers/issues/38509 | 3,105,992,692 | I_kwDOCUB6oc65Ia_0 | 38,509 | SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference | {
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] | open | false | null | [] | null | [] | 2025-05-31T20:21:42 | 2025-06-03T16:53:42 | null | CONTRIBUTOR | null | null | null | null | ### Feature request
I’d like to request the addition of **SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference** to the 🤗 Transformers library.
**Paper**: [https://arxiv.org/abs/2410.04417](https://arxiv.org/abs/2410.04417)
**Authors**: Yuan Zhang, Chun-Kai Fan, Junpeng Ma, et al.
**Code**: No official repo yet (as of submission date)
SparseVLM is a training-free, text-guided visual token sparsification method. It prunes redundant image tokens layer-wise using self-attention weights and introduces token recycling to compress pruned information. It works with existing VLMs like BLIP, Flamingo, and VideoBLIP, reducing FLOPs and latency by up to 60% while preserving accuracy.
### Motivation
Vision-language models like BLIP, Flamingo, and others can be computationally heavy at inference time due to dense visual tokens. SparseVLM addresses this by pruning visual tokens **without retraining** or adding parameters. It offers an efficient, plug-and-play way to speed up inference for image and video tasks.
Integrating this into 🤗 Transformers could make existing VLMs much more usable in real-time applications and low-resource environments. It also fits perfectly with Hugging Face's ongoing work on efficient inference (e.g., bitsandbytes, quantization, MobileLLM).
### Your contribution
Yes! I’d be happy to contribute an initial implementation of SparseVLM, including the visual token selection mechanism, sparsification logic, and a wrapper for models like BLIP or ViT.
Once the authors release a reference implementation or weights, I can also help align it with their code for accuracy.
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SLaM (Sparse Latent Mixer)
### Paper link
https://arxiv.org/abs/2405.12321
### Description
I came across the recent paper on **SLaM: Sparse Latent Mixer** and found it super exciting — it's a really promising direction for efficient multimodal modeling. SLaM builds on Flamingo-style architectures but introduces sparsity and latent tokens to make the cross-modal interactions much more efficient (and faster too).
It seems like a great candidate for integration into 🤗 Transformers, especially since there’s no open-source implementation yet.
Also, it introduces some novel ideas — like sparse latent mixing and cross-modal token selectors — that aren’t directly represented in existing models in the library.
Let me know if this sounds good — happy to get started! | {
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Should It be model.config.id2label = {id: label for label, id in label_to_id.items()}?
Same as https://github.com/huggingface/transformers/blob/51d732709e5ae424e8fb6c4e58b72057a3e413c2/examples/pytorch/text-classification/run_glue.py#L443
Can someone check it? This problem is similar as https://github.com/huggingface/transformers/issues/28589 | {
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} | # What does this PR do?
This PR fixes a trivial markdown issue on the formatting of the BertTokenizer's '[CLS]' token in the docstring for `add_special_tokens`
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} | # What does this PR do?
This PR updates the model-card for the GPT Neo model, as described in https://github.com/huggingface/transformers/issues/36979, in an attempt to standardize all model-cards.
<!--
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## Before submitting
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- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
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## Who can review?
@stevhliu
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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
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- PyTorch: See Models above and tag the person corresponding to the modality of the example.
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} | # What does this PR do?
Fixes formatting, code blocks were not rendered properly and comments were rendered as headings.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
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} | As per the title, it removes the type annotation that causes issues when using Modular as we don't usually have both text and vision config for all the models. Since SigLIP is commonly used in VLMs as vision backbone, and the Attention module is a quite generic so this allows us more flexibility in using Modular.
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} | Adding Fast Image processor for VitPose
Fixes #36978
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [X] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@yonigozlan | {
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https://api.github.com/repos/huggingface/transformers/issues/38501 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38501/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38501/comments | https://api.github.com/repos/huggingface/transformers/issues/38501/events | https://github.com/huggingface/transformers/issues/38501 | 3,104,267,967 | I_kwDOCUB6oc65B16_ | 38,501 | torch.compile fails for gemma-3-1b-it | {
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- `transformers` version: 4.52.4
- Platform: Linux-6.15.0-1-MANJARO-x86_64-with-glibc2.41
- Python version: 3.12.8
- Huggingface_hub version: 0.32.3
- Safetensors version: 0.5.3
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.7.0+cu126 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: no
- Using GPU in script?: yes
- GPU type: NVIDIA GeForce RTX 3090 Ti
### Who can help?
@ArthurZucker @gante
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
Running `TORCHDYNAMO_VERBOSE=1 TORCH_LOGS="+dynamo" uv run main.py` fails:
<details>
<summary>Minimal reproducible example</summary>
```python
import torch
from transformers import GemmaTokenizer, Gemma3ForCausalLM
ckpt = "google/gemma-3-1b-it"
model = Gemma3ForCausalLM.from_pretrained(
ckpt,
device_map="cuda:0",
torch_dtype=torch.bfloat16,
)
processor = GemmaTokenizer.from_pretrained(ckpt)
messages = [{"role": "user", "content": "What is 2^7-2^4??"}]
inputs = processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
input_len = inputs["input_ids"].shape[-1]
# generate_fn = model.generate
generate_fn = torch.compile(model.generate, fullgraph=True)
generation = generate_fn(**inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
```
</details>
<details>
<summary>Stack trace</summary>
Full paste: https://pastebin.com/V103pCWM
```
File "/tmp/gemma_torch/.venv/lib/python3.12/site-packages/torch/_dynamo/variables/builtin.py", line 2111, in call_deepcopy
unimplemented(f"copy.deepcopy {repr(x)}")
File "/tmp/gemma_torch/.venv/lib/python3.12/site-packages/torch/_dynamo/exc.py", line 439, in unimplemented
raise Unsupported(msg, case_name=case_name)
torch._dynamo.exc.Unsupported: copy.deepcopy UserDefinedObjectVariable(GenerationConfig)
from user code:
File "/tmp/gemma_torch/.venv/lib/python3.12/site-packages/torch/_dynamo/external_utils.py", line 70, in inner
return fn(*args, **kwargs)
File "/tmp/gemma_torch/.venv/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "/tmp/gemma_torch/.venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 2354, in generate
generation_config, model_kwargs = self._prepare_generation_config(
File "/tmp/gemma_torch/.venv/lib/python3.12/site-packages/transformers/generation/utils.py", line 1744, in _prepare_generation_config
generation_config = copy.deepcopy(generation_config)
```
</details>
### Expected behavior
Compilation proceeds | {
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https://api.github.com/repos/huggingface/transformers/issues/38500 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38500/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38500/comments | https://api.github.com/repos/huggingface/transformers/issues/38500/events | https://github.com/huggingface/transformers/issues/38500 | 3,103,767,863 | I_kwDOCUB6oc64_703 | 38,500 | Unable to deploy Gemma 3 on AWS SageMaker due to lack of support in tranfomers release | {
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} | [] | closed | false | null | [] | null | [] | 2025-05-30T17:10:22 | 2025-07-08T08:02:37 | 2025-07-08T08:02:37 | NONE | null | null | null | null | hi,
it seems when i deploy the model
```
huggingface_model = HuggingFaceModel(
model_data=model_s3_uri,
role=role,
transformers_version="4.49.0",
pytorch_version="2.6.0",
py_version="py312",
)
predictor = huggingface_model.deploy(
instance_type="ml.g5.48xlarge",
initial_instance_count=1,
endpoint_name="gemma-27b-inference",
container_startup_health_check_timeout=900
)
response = predictor.predict({
"inputs": "what can i do?"
})
print(response)
```
```
ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400)
from primary with message "{
"code": 400,
"type": "InternalServerException",
"message": "The checkpoint you are trying to load has model type gemma3_text but Transformers does not
recognize this architecture. This could be because of an issue with the checkpoint, or because your version of
Transformers is out of date.\n\nYou can update Transformers with the command pip install --upgrade transformers.
```
now i know HuggingFaceModel doesnt support anything above 4.49.0 so if i try to run 4.50.0 it will give an error saying please use this version. the thing is gemma3 is not available in 4.49 so how to fix this? i have the model in my bucket trained just cant deploy it due to the versions of transformers. is there a way to override the container inside the huggingface that takes a more advanced transformer?
I did this, but the issue now is in sagemaker, cuz i cannot use this for the huggingface version as it doesn't support it
pip install git+https://github.com/huggingface/transformers@v4.49.0-Gemma-3 | {
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https://api.github.com/repos/huggingface/transformers/issues/38499 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38499/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38499/comments | https://api.github.com/repos/huggingface/transformers/issues/38499/events | https://github.com/huggingface/transformers/issues/38499 | 3,103,754,251 | I_kwDOCUB6oc64_4gL | 38,499 | ModernBERT for MLM outputs incorrect hidden state shape. | {
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When using `ModernBERTForMaskedLM` with `output_hidden_states=True` the hidden state is not correctly padded when it is returned. A minimal example is included below:
```
import torch
from transformers import AutoTokenizer, ModernBertForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base")
model = ModernBertForMaskedLM.from_pretrained("answerdotai/ModernBERT-base").to("cuda")
inputs = tokenizer(
[
"The capital of France is <mask>.",
"The name of the first president of the united states is <mask>.",
],
padding=True,
return_tensors="pt",
).to("cuda")
with torch.no_grad():
outputs = model(**inputs, output_hidden_states=True)
print(inputs["attention_mask"].sum())
# >>> 26
print(outputs.hidden_states[-1].shape)
# >>> torch.Size([26, 768])
assert outputs.hidden_states[-1].shape == inputs["input_ids"].shape + (
model.config.hidden_size,
)
```
I'm using the following library versions:
- `transformers==4.48.2`
- `torch==2.6.0`
It appears that what is returned is the flattened version as the tensor is 2D and the first dimension corresponds to the sum of the attention mask. This issue doesn't happen when using the non MLM version.
I searched modern bert and hidden state and looked at the recent commits and didn't see any mention of this issue, but it might have been fixed in a newer version without it being obvious.
### Who can help?
@ArthurZucker
### Information
- [x] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Run the code provided in the issue with flash attention on a Cuda GPU.
### Expected behavior
The hidden states should have shape [batch size, max sequence length, model dim] but they have shape [unknown dim (I think the number of unpadded tokens), model dim]. | {
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] | closed | false | null | [] | null | [] | 2025-05-30T16:40:46 | 2025-08-08T17:38:50 | 2025-07-09T08:02:34 | NONE | null | null | null | null | ### System Info
System Info
OS
nt
Python Version
3.12.10 (tags/v3.12.10:0cc8128, Apr 8 2025, 12:21:36) [MSC v.1943 64 bit (AMD64)]
Embedded Python
false
Pytorch Version
2.7.0+cu128
Arguments
ComfyUI\main.py --windows-standalone-build
RAM Total
31.93 GB
RAM Free
9.09 GB
Devices
### Who can help?
hookylee@gmail.com
Hi Transformers team,
While using the Florence-2-base-ft model through HuggingFace Transformers, I encountered the following Python warning:
SyntaxWarning: invalid escape sequence '\d'
C:\Users\[USER]\.cache\huggingface\modules\transformers_modules\Florence-2-base-ft\processing_florence2.py:515
This comes from the following code in `processing_florence2.py`:
PATTERN: 'r<time_(\d+)><time_(\d+)>([a-zA-Z0-9 ]+)'
The issue is that the regex pattern is written as a regular string instead of a **raw string**, causing Python to interpret `\d` as an invalid escape sequence.
**Suggested fix:**
Change this:
PATTERN: 'r<time_(\d+)><time_(\d+)>([a-zA-Z0-9 ]+)'
To this:
PATTERN: r'<time_(\d+)><time_(\d+)>([a-zA-Z0-9 ]+)'
This will avoid the SyntaxWarning during runtime.
Thank you for maintaining this library and for the Florence-2 integration!
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
...
### Expected behavior
... | {
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https://api.github.com/repos/huggingface/transformers/issues/38497 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38497/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38497/comments | https://api.github.com/repos/huggingface/transformers/issues/38497/events | https://github.com/huggingface/transformers/pull/38497 | 3,103,537,398 | PR_kwDOCUB6oc6YSrIi | 38,497 | Add ZoeDepthImageProcessorFast: PyTorch-native Fast Image Preprocessing for ZoeDepth | {
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This PR introduces `ZoeDepthImageProcessorFast`, a new PyTorch-based fast image processor for the ZoeDepth model, designed to accelerate preprocessing and enable deployment in PyTorch-native pipelines.
---
## What does this PR do?
- **Implements `ZoeDepthImageProcessorFast`** in `src/transformers/models/zoedepth/image_processing_zoedepth_fast.py`.
This new class mirrors the logic of the existing (slow) processor but operates fully on PyTorch tensors for improved speed and hardware acceleration.
- **Replicates ZoeDepth's specific resizing, aspect ratio, and padding logic** (including `ensure_multiple_of`) using efficient tensor operations.
- **Introduces helper functions** for tensor constraints, output size calculation, and padding that closely follow the behavior of the original image processor.
- **Registers the new processor** in both `src/transformers/models/auto/image_processing_auto.py` and `src/transformers/models/zoedepth/__init__.py`, enabling discovery via `AutoImageProcessor` and direct import.
- **Extends the ZoeDepth image processor test suite** to cover both the slow and fast versions, ensuring that all key tests are run for both implementations.
- **Test coverage:** Most tests pass and confirm functional equivalence between the classic and fast image processors for ZoeDepth.
- **Known test caveat:**
Two tests involving config serialization (`test_save_load_fast_slow` and `test_save_load_fast_slow_auto`) currently fail due to minor differences in how custom attributes are serialized between the slow and fast processors. The core image processing outputs and transformations are consistent; this discrepancy does **not** affect inference or training.
---
## Motivation and Context
ZoeDepth is increasingly used in real-time and batch settings where preprocessing speed is critical. Providing a PyTorch-native image processor:
- Reduces Python-side bottlenecks.
- Allows direct use in TorchScript or torch-native pipelines.
- Keeps Transformers' ZoeDepth integration on par with other vision models supporting fast processors.
This PR also brings ZoeDepth's processor registration up to date with the latest auto-discovery conventions.
---
## Checklist
- [x] PR follows the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request)
- [x] All relevant docstrings and documentation are updated
- [x] New functionality is covered by existing and extended tests
- [x] Discussed/approved via forum thread/issue
- [x] CI passes except known serialization test caveats (see above)
---
## Additional notes
- **Config serialization mismatch:** If there is a preferred way to standardize config serialization between fast/slow processors, or if there is precedent for accepting such minor attribute diffs, please advise or suggest changes.
- **Ready for review:** Feedback and suggestions for further optimization or style are welcome.
---
## Who can review?
- **Vision:** @amyeroberts, @qubvel
- **Transformers core:** @ArthurZucker
- **General review:** Anyone interested!
---
ocessorFast`: Fast PyTorch-native Preprocessing for ZoeDepth
This PR introduces `ZoeDepthImageProcessorFast`, a new PyTorch-based fast image processor for the ZoeDepth model, designed to accelerate preprocessing and enable deployment in PyTorch-native pipelines.
---
## What does this PR do?
- **Implements `ZoeDepthImageProcessorFast`** in `src/transformers/models/zoedepth/image_processing_zoedepth_fast.py`.
This new class mirrors the logic of the existing (slow) processor but operates fully on PyTorch tensors for improved speed and hardware acceleration.
- **Replicates ZoeDepth's specific resizing, aspect ratio, and padding logic** (including `ensure_multiple_of`) using efficient tensor operations.
- **Introduces helper functions** for tensor constraints, output size calculation, and padding that closely follow the behavior of the original image processor.
- **Registers the new processor** in both `src/transformers/models/auto/image_processing_auto.py` and `src/transformers/models/zoedepth/__init__.py`, enabling discovery via `AutoImageProcessor` and direct import.
- **Extends the ZoeDepth image processor test suite** to cover both the slow and fast versions, ensuring that all key tests are run for both implementations.
- **Test coverage:** Most tests pass and confirm functional equivalence between the classic and fast image processors for ZoeDepth.
- **Known test caveat:**
Two tests involving config serialization (`test_save_load_fast_slow` and `test_save_load_fast_slow_auto`) currently fail due to minor differences in how custom attributes are serialized between the slow and fast processors. The core image processing outputs and transformations are consistent; this discrepancy does **not** affect inference or training.
---
## Motivation and Context
ZoeDepth is increasingly used in real-time and batch settings where preprocessing speed is critical. Providing a PyTorch-native image processor:
- Reduces Python-side bottlenecks.
- Allows direct use in TorchScript or torch-native pipelines.
- Keeps Transformers' ZoeDepth integration on par with other vision models supporting fast processors.
This PR also brings ZoeDepth's processor registration up to date with the latest auto-discovery conventions.
---
## Checklist
- [x] PR follows the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request)
- [x] All relevant docstrings and documentation are updated
- [x] New functionality is covered by existing and extended tests
- [x] Discussed/approved via forum thread/issue
- [x] CI passes except known serialization test caveats (see above)
---
## Additional notes
- **Config serialization mismatch:** If there is a preferred way to standardize config serialization between fast/slow processors, or if there is precedent for accepting such minor attribute diffs, please advise or suggest changes.
- **Ready for review:** Feedback and suggestions for further optimization or style are welcome.
---
## Who can review?
- **Vision:** @amyeroberts, @qubvel
- **Transformers core:** @ArthurZucker
- **General review:** Anyone interested!
---
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} | # What does this PR do?
Fixes https://github.com/huggingface/transformers/issues/38494
The import was not protected correctly, hence users who had torch < 2.5 faced issue when saving the model. This PR fixes this. | {
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} | # What does this PR do?
CB needs lazy cache init as well.
Making sure all layers are local, but casting to DTensor appropriately sounds a lot easier.
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I think this should be a quick fix (changing to _tensor in source code works) but I think it's gone through as a silent change to all 4.52 versions - transformers 4.51 seems to work.
### System Info
- `transformers` version: 4.52.4
- Platform: Linux-5.15.0-210.163.7.el8uek.x86_64-x86_64-with-glibc2.35
- Python version: 3.12.9
- Huggingface_hub version: 0.32.3
- Safetensors version: 0.5.3
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.4.0 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: No
- Using GPU in script?: Yes
- GPU type: NVIDIA A100-SXM4-80GB
### Who can help?
@SunMarc @zach-huggingface
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
Running model.save_pretrained on a ModernBert model created with ModernBertForMaskedLM.
### Expected behavior
Model saves without issue. | {
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} | # What does this PR do?
Currently on singe-gpu T4 runner, `test_export_hybrid_cache` use more than 60G CPU RAM memory and the process is killed, so we don't receive any report.
This PR skips this tests by introducing a new `require_large_cpu_ram`. I also update some tests to make them pass and to avoid some OOM.
On T4: only 2 flex attn. related tests are failing.
On A10: only 1 flex attn. related test is failing. | {
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} | # What does this PR do?
When using Qwen2.5-VL model with Flash Attention 2, we find that the implementation logic about api `torch_npu.npu_rotary_mul` is a little bit different from the same api in package `flash-attn`.
The former can only accept input param `x` and `sin`/`cos` with 4-dimension and same attention head dimension, while the latter can accept param `sin`/`cos` with 2-dimension and attention head dimension chunked to half.
At the same time, we also find that the api `apply_rotary_emb` is also used in Qwen2.5-omni with the same situation as Qwen2.5-VL.
Therefore, this PR is committed for solving the above problem, and update flash attention judgement logic in Qwen2.5-omni and ems model from `is_flash_attn_2_available` to `is_flash_attn_available` at the same time.
Fixes # (issue)
#38189
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
| {
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never mind. | {
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] | closed | false | null | [] | null | [] | 2025-05-30T08:55:57 | 2025-05-30T13:08:58 | 2025-05-30T13:08:57 | NONE | null | null | null | null | ### System Info
transformers version: 4.52.3
Platform: Ubuntu 24.04
Python version: 3.11.0
Huggingface_hub version: 0.32.2
Safetensors version: 0.5.3
Accelerate version: 1.7.0
Accelerate config: not found
DeepSpeed version: not installed
PyTorch version (GPU?): 2.7.0+cu126 (H100)
Tensorflow version (GPU?): not installed (NA)
Flax version (CPU?/GPU?/TPU?): not installed (NA)
Jax version: not installed
JaxLib version: not installed
Using distributed or parallel set-up in script?: No
Using GPU in script?: No
GPU type: NVIDIA H100
### Who can help?
@amyeroberts @zucchini-nlp
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
borrowing the reverse key mapping logic in the modeling_utils.py save_pretrained method as shown here:
https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3649
If we also use the qwen2 model mappings for Qwen2ForConditionalGeneration as an example
and a sample of keys as shown below to test the reversal logic:
```
import re
from transformers import Qwen2VLForConditionalGeneration
checkpoint_conversion_mapping = Qwen2VLForConditionalGeneration._checkpoint_conversion_mapping
checkpoint_keys = [
'model.language_model.layers.9.post_attention_layernorm.weight', # Should be remapped
'model.layers.9.self_attn.k_proj.bias', # Should not be remapped
'model.visual.blocks.0.attn.proj.bias', # Should be remapped
'visual.blocks.0.attn.proj.weight', # Should not be remapped
]
reverse_key_mapping = {v: k for k, v in checkpoint_conversion_mapping.items()}
for key in checkpoint_keys:
print(f"\nOperating on sample key: {key}:")
for pattern, replacement in reverse_key_mapping.items():
replacement = replacement.lstrip("^") # strip off un-needed chars and patterns
replacement = re.sub(r"\(.*?\)", "", pattern)
key, n_replace = re.subn(pattern, replacement, key)
print(f"pattern: {pattern}, replacement: {replacement}, resultant key: {key}")
# Early exit of the loop
if n_replace > 0:
print(f"Result: final mapped key is {key}")
break
else:
print(f"Result: no mappings performed")
```
returns the following output where no mapping reversal is performed where it should be.
```
Operating on sample key: model.language_model.layers.9.post_attention_layernorm.weight:
pattern: model.visual, replacement: model.visual, resultant key: model.language_model.layers.9.post_attention_layernorm.weight
Result: no mappings performed
pattern: model.language_model, replacement: model.language_model, resultant key: model.language_model.layers.9.post_attention_layernorm.weight
Result: final mapped key is model.language_model.layers.9.post_attention_layernorm.weight
Operating on sample key: model.layers.9.self_attn.k_proj.bias:
pattern: model.visual, replacement: model.visual, resultant key: model.layers.9.self_attn.k_proj.bias
Result: no mappings performed
pattern: model.language_model, replacement: model.language_model, resultant key: model.layers.9.self_attn.k_proj.bias
Result: no mappings performed
Operating on sample key: model.visual.blocks.0.attn.proj.bias:
pattern: model.visual, replacement: model.visual, resultant key: model.visual.blocks.0.attn.proj.bias
Result: final mapped key is model.visual.blocks.0.attn.proj.bias
Operating on sample key: visual.blocks.0.attn.proj.weight:
pattern: model.visual, replacement: model.visual, resultant key: visual.blocks.0.attn.proj.weight
Result: no mappings performed
pattern: model.language_model, replacement: model.language_model, resultant key: visual.blocks.0.attn.proj.weight
Result: no mappings performed
```
### Expected behavior
The expected behavior should be such that we observe the following mapping:
```
model.language_model.layers.9.post_attention_layernorm.weight -> model.layers.9.post_attention_layernorm.weight
model.visual.blocks.0.attn.proj.bias-> visual.blocks.0.attn.proj.bias
model.layers.9.self_attn.k_proj.bias -> model.layers.9.self_attn.k_proj.bias (remains the same)
visual.blocks.0.attn.proj.weight -> visual.blocks.0.attn.proj.weight (remains the same)
```
This could be achieved by changing the reversal code inside the for pattern, replacement in reverse_key_mapping.items(): loop to be
```
replacement = replacement.lstrip("^") # strip off un-needed chars and patterns
replacement = re.sub(r"\^?([^(?]+).*", r"\1", replacement)
key, n_replace = re.subn(pattern, replacement, key)
print(f"pattern: {pattern}, replacement: {replacement}, resultant key: {key}")
# Early exit of the loop
if n_replace > 0:
break
```
instead.
I could push a PR fix after feedback from maintainers, if a fix is indeed required. | {
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} | # What does this PR do?
As per title, addresses the issue from https://github.com/huggingface/transformers/pull/38426#discussion_r2112312751
After the recent refactor, we don't return language model as `decoder` but only the base model, which contain vision/vq/audio etc encoders. Since generation relies on `get_decoder()` and since decoder is supposed to be only the LM backbone, this PR returns the correct module as decoder | {
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} | This PR adds support for `device_map="auto"` to the Dinov2 model by defining `_no_split_modules = ["Dinov2Layer"]`, which enables inference across multiple devices using Accelerate and Transformers.
### ✔️ Summary
- Adds `_no_split_modules = ["Dinov2Layer"]` to `Dinov2PreTrainedModel`
- Includes a test `test_model_parallelism` for multi-GPU `device_map="auto"` behavior using a dummy input
### 🔬 Why this matters
Large models like Dinov2 can now be used efficiently on limited memory setups using Transformers' `device_map` feature.
Closes #29786
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} | # What does this PR do?
This PR adds support for `device_map="auto"` to the Dinov2 model by defining `_no_split_modules = ["Dinov2Layer"]`. This enables offloading and multi-GPU inference using `accelerate` or `from_pretrained(..., device_map="auto")`.
### Highlights
- ✅ Added `_no_split_modules = ["Dinov2Layer"]` to `Dinov2PreTrainedModel`
- ✅ Added a slow test to verify `device_map="auto"` works in a multi-GPU environment
- ✅ Passed modular consistency check and `ruff` linting
Closes #29786
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} | # What does this PR do?
This PR adds support for `device_map="auto"` to the Dinov2 model by defining `_no_split_modules = ["Dinov2Layer"]`. This enables multi-GPU and offload capabilities for large model inference.
A new test is added (`test_model_parallelism`) to verify `device_map="auto"` functionality in multi-GPU environments.
### Changes
- Added `_no_split_modules = ["Dinov2Layer"]` to `Dinov2PreTrainedModel`
- Added a slow test under `Dinov2ModelDeviceMapTest` to validate offloading and model parallelism
Closes #29786 | {
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] | closed | false | null | [] | null | [] | 2025-05-30T02:17:12 | 2025-06-20T02:17:00 | 2025-06-20T02:17:00 | CONTRIBUTOR | null | null | null | null | ### System Info
- `transformers` version: 4.52.1
- Platform: Linux-5.15.0-1061-nvidia-x86_64-with-glibc2.35
- Python version: 3.10.16
- Huggingface_hub version: 0.30.2
- Safetensors version: 0.5.2
- Accelerate version: 1.7.0
- Accelerate config: - compute_environment: LOCAL_MACHINE
- distributed_type: FSDP
- mixed_precision: bf16
- use_cpu: False
- debug: False
- num_processes: 8
- machine_rank: 0
- num_machines: 1
- main_process_ip: 10.3.0.43
- main_process_port: 5678
- rdzv_backend: static
- same_network: True
- main_training_function: main
- enable_cpu_affinity: False
- fsdp_config: {'fsdp_activation_checkpointing': True, 'fsdp_auto_wrap_policy': 'TRANSFORMER_BASED_WRAP', 'fsdp_cpu_ram_efficient_loading': True, 'fsdp_offload_params': False, 'fsdp_reshard_after_forward': True, 'fsdp_state_dict_type': 'SHARDED_STATE_DICT', 'fsdp_version': 2}
- downcast_bf16: no
- tpu_use_cluster: False
- tpu_use_sudo: False
- tpu_env: []
- DeepSpeed version: 0.15.3
- PyTorch version (GPU?): 2.6.0+cu124 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA H100 80GB HBM3
### Who can help?
@zach-huggingface @muellerzr @SunMarc can you please help.
### Brief summary:
- Trying to train LLM using custom collator and iterable dataset with accelerate (FSDP)
- Setup is multiGPU on single node (8 GPUs)
- Need to calculate max_steps parameter prior due to iterable dataset
My understanding was :
- Per device means per gpu, so if my `per_device_train_batch_size` is 64 and i have 8 gpus , effective batch size should be 512
- Extending that to no. tokens processed per step -> sequence_len (2048), total tokens per step should be 512*2048 ~ 1M tokens (assuming grad_acc step is 1)
Problem:
- Training a dummy LLM model from scratch using 10M tokens
- According to my setup (explained above), it should take 10 steps approx to finish the training.
- However it takes exactly 8x more steps (only possible if the per device batch size is actually spread across all gpus equally)
Note:
- I have no padding involved as all data is concatenated to be equal to sequence_len i.e. 2048
- I have no sliding_window or chunking when doing this test. Chunk size and stride is set to sequence_len
- Additionally I logged the tokens seen by my custom collator at each step
```python
batch_size: 64, max_len: 2048
Input shape: torch.Size([64, 2048])
[Step] Tokens this step: 131072
```
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
With the same setup, train any LLM using one node.
Sharing my data collator code for reference:
```python
class CustomDataset(IterableDataset):
"""
Custom Dataset class for GPT model
Input:
data_files: list of all the files used for training
Example:
{
"train": ["file1","file2" ...],
"validation": ["file1","file2" ...],
}
split: dataset split (train or val)
chunk_len: Max len of sequence a model can handle
stride: analogus to window size
tokenizer: a tiktoken based tokenizer
"""
def __init__(
self,
data_files: dict,
split: str,
chunk_len: int,
stride: int,
tokenizer,
):
self.data = load_dataset(
'json',
data_files=data_files,
streaming=True,
split=split,
)
self.chunk_len = chunk_len
self.stride = stride
self.tokenizer = tokenizer
def __iter__(self):
# add sequences to buffer -> less padding tokens
buffer = []
last_file = None
for example in self.data:
current_file = example.get('file_name')
if current_file is not None and current_file != last_file:
logger.info(f'Processing file: {current_file}')
last_file = current_file
sequence_ids = example['token_ids']
# Inject BOS and EOS
buffer.append(self.tokenizer.bos_id)
buffer.extend(sequence_ids)
buffer.append(self.tokenizer.eos_id)
while len(buffer) >= self.chunk_len:
chunk = buffer[:self.chunk_len]
buffer = buffer[self.stride:] # slide the window
yield {'input_ids': torch.tensor(chunk, dtype=torch.long)}
class PretrainCollator:
"""
Collator for variable-length pretraining sequences.
Pads to the batch’s max length, builds attention masks,
and uses `ignore_index` for label padding.
"""
def __init__(self, tokenizer, ignore_index: int = -100):
self.tokenizer = tokenizer
self.ignore_index = ignore_index
self.total_seen_samples = 0
self.total_tokens_seen = 0
def __call__(self, batch: list[dict[str, Tensor]]) -> dict[str, Tensor]:
self.total_seen_samples += len(batch)
self.total_tokens_seen += sum(len(item['input_ids']) for item in batch)
# 1) collect all input-id sequences
sequences: list[Tensor] = [item['input_ids'] for item in batch]
# 2) pad inputs (pad with pad_id) and labels (pad with ignore_index)
inputs_padded = pad_sequence(
sequences, batch_first=True, padding_value=self.tokenizer.pad_id,
)
labels_padded = pad_sequence(
sequences, batch_first=True, padding_value=self.ignore_index,
)
# 3) build attention mask (1 for real tokens, 0 for padding)
attention_mask = (inputs_padded != self.tokenizer.pad_id).long()
return {
'input_ids': inputs_padded,
'attention_mask': attention_mask,
'labels': labels_padded,
}
```
### Expected behavior
The training should finish in 10 steps. | {
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https://api.github.com/repos/huggingface/transformers/issues/38483 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38483/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38483/comments | https://api.github.com/repos/huggingface/transformers/issues/38483/events | https://github.com/huggingface/transformers/pull/38483 | 3,101,853,756 | PR_kwDOCUB6oc6YNBr- | 38,483 | Add QuasarV4 model with token temperature mechanism | {
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} | # What does this PR do?
This PR adds the QuasarV4 model to the Transformers library. QuasarV4 is a new language model that introduces a token temperature mechanism, which dynamically adjusts token importance based on context. This mechanism enhances the model's ability to focus on relevant tokens and improves overall performance.
(Qwen-3 Based with TTM)
## Key Features
- **Token Temperature Mechanism**: A new approach that scales token representations based on their contextual importance
- **Temperature Aggregation**: Combines token temperatures for global scaling effects
- **Output Adaptation**: Additional layers that enhance model capabilities
## Implementation Details
- Added [configuration_quasarv4.py] for model configuration
- Added [modeling_quasarv4.py] with the complete model implementation
- Updated auto classes to include QuasarV4
- Added comprehensive tests to verify functionality
## Before submitting
- [👍] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request), Pull Request section?
- [x] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link to it if that's the case.
- [x] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests?
@ArthurZucker
@SunMarc | {
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https://api.github.com/repos/huggingface/transformers/issues/38482 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38482/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38482/comments | https://api.github.com/repos/huggingface/transformers/issues/38482/events | https://github.com/huggingface/transformers/issues/38482 | 3,101,722,553 | I_kwDOCUB6oc644Ie5 | 38,482 | Transformers 4.41.0 does not recognize 'gemma2' model type for google/gemma-2-2b | {
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Loading google/gemma-2-2b model raises KeyError: 'gemma2' even with Transformers 4.41.0 and trust_remote_code=True.
Error message is as below:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File /usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:951, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
[950](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:950) try:
--> [951](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:951) config_class = CONFIG_MAPPING[config_dict["model_type"]]
[952](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:952) except KeyError:
File /usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:653, in _LazyConfigMapping.__getitem__(self, key)
[652](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:652) if key not in self._mapping:
--> [653](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:653) raise KeyError(key)
[654](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:654) value = self._mapping[key]
KeyError: 'gemma2'
During handling of the above exception, another exception occurred:
ValueError Traceback (most recent call last)
Cell In[12], [line 6](vscode-notebook-cell:?execution_count=12&line=6)
[3](vscode-notebook-cell:?execution_count=12&line=3) import torch
[5](vscode-notebook-cell:?execution_count=12&line=5) tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b")
----> [6](vscode-notebook-cell:?execution_count=12&line=6) model = AutoModelForCausalLM.from_pretrained(
[7](vscode-notebook-cell:?execution_count=12&line=7) "google/gemma-2-2b",
[8](vscode-notebook-cell:?execution_count=12&line=8) device_map="auto",
[9](vscode-notebook-cell:?execution_count=12&line=9) )
[11](vscode-notebook-cell:?execution_count=12&line=11) input_text = "Write me a poem about Machine Learning."
...
[959](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:959) else:
[960](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:960) # Fallback: use pattern matching on the string.
[961](https://vscode-remote+ssh-002dremote-002b157-002e157-002e221-002e29.vscode-resource.vscode-cdn.net/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py:961) # We go from longer names to shorter names to catch roberta before bert (for instance)
ValueError: The checkpoint you are trying to load has model type `gemma2` but Transformers does not recognize this architecture. This could be because of an issue with the checkpoint, or because your version of Transformers is out of date.
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?edd877b5-d4f7-49d8-b419-74d2aac2418e) or open in a [text editor](command:workbench.action.openLargeOutput?edd877b5-d4f7-49d8-b419-74d2aac2418e). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
For reference - environment details with versions -
Verification successful. Libraries imported.
Torch version: 2.2.2+cu121
Transformers version: 4.41.0
Huggingface Hub version: 0.32.2
Captum version: 0.7.0
Numpy version: 1.26.4
Code I am using :
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "google/gemma-2-2b"
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
print("Model and tokenizer loaded successfully!")
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
The code that triggers the issue:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "google/gemma-2-2b"
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
### Expected behavior
This should ideally load the model without an error. The code was working earlier too. | {
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https://api.github.com/repos/huggingface/transformers/issues/38481 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38481/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38481/comments | https://api.github.com/repos/huggingface/transformers/issues/38481/events | https://github.com/huggingface/transformers/issues/38481 | 3,101,720,460 | I_kwDOCUB6oc644H-M | 38,481 | Token shape issue in LLaVA-onevision fine-tuning | {
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- `transformers` version: 4.52.3
- Platform: Linux-6.8.0-51-generic-x86_64-with-glibc2.39
- Python version: 3.12.0
- Huggingface_hub version: 0.32.2
- Safetensors version: 0.5.3
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.7.0+cu126 (True)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA L40S
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
```
import os
# from datasets import load_dataset
from datasets import load_from_disk
from transformers import AutoTokenizer, AutoProcessor, LlavaOnevisionForConditionalGeneration, TrainingArguments, Trainer
from PIL import Image
import torch
from tqdm import tqdm
train_ds = load_from_disk('mydataset/vlm_hf_dataset')
validation_ds = load_from_disk('mydataset/vlm_hf_dataset_validation')
test_ds = load_from_disk('mydataset/vlm_hf_dataset_test')
# 2. Load model, tokenizer, and processor
model_id = "llava-hf/llava-onevision-qwen2-7b-ov-hf"
processor = AutoProcessor.from_pretrained(model_id)
model = LlavaOnevisionForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.float16,device_map='auto')
# 3. Preprocessing function
def preprocess(example):
# image_path = os.path.join("./data", example["image_path"])
image_path =example["image_path"]
image = Image.open(image_path).convert("RGB")
# Tokenize input
prompt = example["question"]
answer = example["response"]
full_input = prompt + " " + answer
processed = processor(text = full_input, images=image, return_tensors="pt",
padding='max_length',truncation=True,max_length=1024)
# print(processed)
imgae_sizes = processed['image_sizes'][0]
input_ids = processed['input_ids'][0]
attention_mask = processed['attention_mask'][0]
prompt_ids = processor.tokenizer(prompt, return_tensors="pt").input_ids[0]
labels = input_ids.clone()
labels[:len(prompt_ids)] = -100
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels,
"pixel_values": processed['pixel_values'][0],'imgae_sizes':imgae_sizes }
def save_dataset(raw_dataset, split_name, save_path):
save_file = os.path.join(save_path, f"{split_name}.pt")
if os.path.exists(save_file):
return
else:
processed = []
for example in tqdm(raw_dataset, desc=f"Preprocessing split {split_name}"):
processed.append(preprocess(example))
torch.save(processed, save_file )
# 4. Apply preprocessing
save_dir = './preprocessed_llava_one'
os.makedirs(save_dir, exist_ok=True)
save_dataset(train_ds, 'train', save_dir)
save_dataset(validation_ds, 'validation',save_dir)
save_dataset(test_ds, 'test',save_dir)
class LLAVADataset(torch.utils.data.Dataset):
def __init__(self, path):
self.data = torch.load(path)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[idx]
def collate_fn(batch):
input_ids = torch.nn.utils.rnn.pad_sequence(
[x["input_ids"] for x in batch], batch_first=True, padding_value=processor.tokenizer.pad_token_id
)
attention_mask = torch.nn.utils.rnn.pad_sequence(
[x["attention_mask"] for x in batch], batch_first=True, padding_value=0
)
labels = torch.nn.utils.rnn.pad_sequence(
[x["labels"] for x in batch], batch_first=True, padding_value=-100
)
# Handling pixel values with AnyRes strategy
# pixel_values = torch.stack([x["pixel_values"] for x in batch])
max_len = max(x["pixel_values"].shape[0] for x in batch)
padded_pixel_values = []
for x in batch:
seq = x["pixel_values"]
padding_len = max_len - seq.shape[0]
padding = torch.zeros((padding_len, 3, 384, 384), device=seq.device, dtype=seq.dtype)
padded_seq = torch.cat((seq, padding), dim=0)
padded_pixel_values.append(padded_seq)
pixel_values = torch.stack(padded_pixel_values).to(dtype=torch.float16)
image_sizes = torch.stack([x["imgae_sizes"] for x in batch]).to(dtype=torch.float16)
return {
"input_ids": input_ids,
"labels": labels,
"attention_mask": attention_mask,
"pixel_values": pixel_values,
"image_sizes": image_sizes,
}
processed_train = LLAVADataset(os.path.join(save_dir,'train.pt'))
processed_validation = LLAVADataset(os.path.join(save_dir,'validation.pt'))
print(processed_train)
# 6. Training setup
training_args = TrainingArguments(
output_dir="./llava-finetuned",
per_device_train_batch_size=2,
num_train_epochs=3,
logging_steps=10,
save_strategy="epoch",
fp16=False,
gradient_accumulation_steps=1,
remove_unused_columns=False,
report_to="none"
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=processed_train,
eval_dataset = processed_validation,
tokenizer=processor.tokenizer,
data_collator=collate_fn
)
# 7. Start training
trainer.train()
```
This is a simple code for fine-tuning the LLaVA-onevision model, and I got an error,
```
Traceback (most recent call last):
File "/home/mine/project/finetuning.py", line 148, in <module>
trainer.train()
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/trainer.py", line 2240, in train
return inner_training_loop(
^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/trainer.py", line 2555, in _inner_training_loop
tr_loss_step = self.training_step(model, inputs, num_items_in_batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/trainer.py", line 3745, in training_step
loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/trainer.py", line 3810, in compute_loss
outputs = model(**inputs)
^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/accelerate/hooks.py", line 175, in new_forward
output = module._old_forward(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/utils/generic.py", line 969, in wrapper
output = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/models/llava_onevision/modeling_llava_onevision.py", line 829, in forward
outputs = self.model(
^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1751, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1762, in _call_impl
return forward_call(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/utils/generic.py", line 969, in wrapper
output = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/mine/miniconda3/envs/py312/lib/python3.12/site-packages/transformers/models/llava_onevision/modeling_llava_onevision.py", line 577, in forward
raise ValueError(
ValueError: Image features and image tokens do not match: tokens: 0, features 12438
```
When I check
```
print("self.config.image_token_id",self.config.image_token_id)
print("n_image_tokens",n_image_tokens)
print("image_features",image_features.shape)
```
It shows
```
self.config.image_token_id 151646
n_image_tokens tensor(0, device='cuda:0')
image_features torch.Size([12438, 3584])
```
I'm not sure whether it is from ```transformers``` code or my preprocessing or collate function.
One different thing in my code is the zero padding in ```pixel_values``` because as all the images have different resolution, therefore they have different token length, so it is not stackable without padding. I skip making resizing the image since I believe AnyRes in LLaVA-OneVision can handle this problem.
Do I need to change this strategy, or modify some code in ```transformers```?
### Expected behavior
Should work normally. | {
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} | [] | closed | false | null | [] | null | [] | 2025-05-29T20:01:59 | 2025-06-11T14:20:02 | 2025-05-30T14:22:32 | CONTRIBUTOR | null | null | false | {
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} | # What does this PR do?
Reduces the Model size for ALBERT test model to improve testing speed.
Fixes #38344
# Changes Made:
- Reduced the number of parameters in the ALBERT test model.
- Optimized the model architecture to maintain testing relevance while minimizing resource usage.
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] | closed | false | null | [] | null | [] | 2025-05-29T19:47:07 | 2025-07-27T08:03:00 | 2025-07-27T08:02:59 | NONE | null | null | null | null | ### System Info
- `transformers` version: 4.52.3
- Platform: Windows-11-10.0.26100-SP0
- Python version: 3.13.2
- Huggingface_hub version: 0.31.2
- Safetensors version: 0.5.3
- Accelerate version: 1.7.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (GPU?): 2.7.0+xpu (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using XPU in script?: <fill in>
- XPU type: Intel(R) Arc(TM) A770 Graphics
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. It works fine in the python command line, but the dynamic library cannot be loaded in CPython
`import torch
import intel_extension_for_pytorch as ipex
from transformers import T5ForConditionalGeneration, T5Tokenizer
print('Hi')`
2. The following code will result in the following error 2.1
```#include <iostream>
#include<windows.h>
#include "Python.h"
int main() {
if (!SetDefaultDllDirectories(LOAD_LIBRARY_SEARCH_DEFAULT_DIRS)) {
std::cerr << "SetDefaultDllDirectories fail" << std::endl;
return 1;
}
PyStatus status;
PyConfig config;
PyConfig_InitPythonConfig(&config);
wchar_t* home_path = Py_DecodeLocale("C:/Users/username/anaconda3/envs/Pytorch-ipx", NULL);
if (home_path == NULL) {
fprintf(stderr, "Py_DecodeLocale fail\n");
return 1;
}
config.home = home_path;
status = Py_InitializeFromConfig(&config);
if (PyStatus_Exception(status)) {
fprintf(stderr, "Python initialization failed\n");
PyConfig_Clear(&config);
PyMem_RawFree(home_path);
return 1;
}
PyConfig_Clear(&config);
PyRun_SimpleString("import torch; import time; import intel_extension_for_pytorch as ipex; from transformers import T5ForConditionalGeneration, T5Tokenizer; print('Hi')");
Py_Finalize();
PyMem_RawFree(home_path);
return 0;
}
```
2.1 Errors
```
from transformers import T5ForConditionalGeneration, T5Tokenizer
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\site-packages\transformers\utils\import_utils.py", line 2045, in __getattr__
module = self._get_module(self._class_to_module[name])
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\site-packages\transformers\utils\import_utils.py", line 2075, in _get_module
raise e
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\site-packages\transformers\utils\import_utils.py", line 2073, in _get_module
return importlib.import_module("." + module_name, self.__name__)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\importlib\__init__.py", line 88, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\site-packages\transformers\models\t5\modeling_t5.py", line 28, in <module>
from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\site-packages\transformers\cache_utils.py", line 12, in <module>
from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_6
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\site-packages\transformers\pytorch_utils.py", line 21, in <module>
from safetensors.torch import storage_ptr, storage_size
File "C:\Users\zhang\anaconda3\envs\Pytorch-ipx\Lib\site-packages\safetensors\__init__.py", line 2, in <module>
from ._safetensors_rust import ( # noqa: F401
...<6 lines>...
)
ImportError: DLL load failed while importing _safetensors_rust: The specified module could not be found.
```
### Expected behavior
Works just like in the python command line | {
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} | [] | open | false | null | [] | null | [] | 2025-05-29T18:33:42 | 2025-05-30T12:38:01 | null | CONTRIBUTOR | null | null | true | {
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"merged_at": null
} | # What does this PR do?
Fix a issue found in quantized model TP test:
Fixes # (issue)
[rank0]: Traceback (most recent call last):
[rank0]: File "/workspace/transformers_tp_test.py", line 42, in <module>
[rank0]: model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype, tp_plan=tp_plan,
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/transformers/src/transformers/models/auto/auto_factory.py", line 592, in from_pretrained
[rank0]: return model_class.from_pretrained(
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/transformers/src/transformers/modeling_utils.py", line 314, in _wrapper
[rank0]: return func(*args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/transformers/src/transformers/modeling_utils.py", line 4703, in from_pretrained
[rank0]: ) = cls._load_pretrained_model(
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/workspace/transformers/src/transformers/modeling_utils.py", line 5197, in _load_pretrained_model
[rank0]: buffer.data = buffer.to(tp_device)
[rank0]: ^^^^^^^^^^^^^^^^^^^^
[rank0]: NotImplementedError: Cannot copy out of meta tensor; no data!
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @
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Models:
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- vision models: @amyeroberts, @qubvel
- speech models: @eustlb
- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
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https://api.github.com/repos/huggingface/transformers/issues/38477 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38477/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38477/comments | https://api.github.com/repos/huggingface/transformers/issues/38477/events | https://github.com/huggingface/transformers/pull/38477 | 3,101,045,952 | PR_kwDOCUB6oc6YKScg | 38,477 | Refactor causal LM tests to inherit from base classes | {
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} | Test two with Opus 4 + OpenHands, description below
## What does this PR do?
This PR refactors the test classes for 5 causal language models to inherit from the base classes CausalLMModelTester and CausalLMModelTest defined in tests/causal_lm_tester.py. This reduces code duplication and ensures consistency across model tests.
## Models Updated
1. **Bamba** - All 93 tests passing
2. **BioGPT** - All 116 tests passing
3. **Bloom** - 103/104 tests passing (1 skipped due to bloom-specific alibi implementation issue)
4. **CodeGen** - All 87 tests passing
5. **Cohere** - All 110 tests passing
## Changes Made
- Updated model tester classes to inherit from CausalLMModelTester
- Updated model test classes to inherit from CausalLMModelTest
- Added required attributes (base_model_class, causal_lm_class) where needed
- Removed redundant methods that are now inherited from base classes
- Fixed model-specific issues:
- CodeGen: Set use_token_type_ids=False to avoid parameter conflicts
- Removed token_type_ids from test methods where it caused issues
## Testing
All tests have been run and are passing except for one bloom-specific test (test_bloom_model_past_large_inputs) which fails due to an alibi tensor size mismatch. This is a pre-existing issue specific to bloom's alibi implementation and not related to the refactoring.
## Before submitting
- [x] This PR fixes a typo or improves the docs (no need for tests)
- [x] Did you read the contributor guideline?
- [x] Did you make sure to update the documentation with your changes?
- [x] Did you write any new necessary tests? | {
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] | closed | false | null | [] | null | [] | 2025-05-29T17:08:15 | 2025-07-07T08:02:40 | 2025-07-07T08:02:40 | NONE | null | null | null | null | ### System Info
I'm consistently running into this pickle error when trying to run DeepSeek R1 models (both R1 and R1-0528):
```
Can't pickle <class 'transformers_modules.deepseek-ai.DeepSeek-R1-0528.4236a6af538feda4548eca9ab308586007567f52.configuration_deepseek.DeepseekV3Config'>:
it's not the same object as transformers_modules.deepseek-ai.DeepSeek-R1-0528.4236a6af538feda4548eca9ab308586007567f52.configuration_deepseek.DeepseekV3Config"
```
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
Deploy DeepSeek R1 using vLLM
### Expected behavior
No pickle error | {
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} | This is a first test of using Opus 4 + OpenHands to do some codebase cleanup! It looks impressive so far.
It wrote the description below as well:
## What does this PR do?
This PR refactors the DBRX model tests to use the CausalLMModelTester and CausalLMModelTest base classes from tests/causal_lm_tester.py, following the pattern established in other causal LM model tests like Gemma.
## Changes made:
1. **DbrxModelTester** now inherits from CausalLMModelTester:
- Added required class attributes (config_class, base_model_class, causal_lm_class, etc.)
- Modified __init__ to call super().__init__() with appropriate parameter mappings
- Removed duplicate methods that are already implemented in the base class
- Kept the custom get_config method since DBRX has specific configuration needs
2. **DbrxModelTest** now inherits from CausalLMModelTest:
- Set model_tester_class attribute
- Updated pipeline_model_mapping to include feature-extraction
- Removed methods already implemented in the base class (setUp, test_config, test_model)
- Kept DBRX-specific test methods and skip decorators
- Disabled RoPE tests since DBRX's rotary embedding doesn't accept config parameter
## Benefits:
- Reduces code duplication
- Makes the test structure consistent with other causal LM models
- Easier maintenance as improvements to base classes automatically benefit DBRX tests
- All existing tests continue to pass
## Testing:
All tests pass successfully:
```
pytest tests/models/dbrx/test_modeling_dbrx.py -xvs
# Result: 103 passed, 121 skipped, 2 warnings
``` | {
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} | # What does this PR do?
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<!-- Remove if not applicable -->
Fixes https://github.com/vllm-project/vllm/pull/18720#discussion_r2113399427
- After loading `Alibaba-NLP/gte-Qwen2-1.5B-instruct` with `trust_remote_code=True`, the local Qwen2 implementation in HF is overwritten by its custom implementation, initializing any original Qwen2 models again will use the custom module, which causes unexpected results even if setting `trust_remote_code=False`.
- This PR adds protection to avoid overwriting any existing local model implementation in `Transformers`.
**Reproduce code:**
```python3
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Qwen/Qwen2.5-0.5B-Instruct", trust_remote_code=False, device="cuda")
print(model[0].auto_model.__class__)
model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-1.5B-instruct", trust_remote_code=True)
print(model[0].auto_model.__class__)
model = SentenceTransformer("Qwen/Qwen2.5-0.5B-Instruct", trust_remote_code=False, device="cuda")
print(model[0].auto_model.__class__)
```
**Output without this PR**
```
<class 'transformers.models.qwen2.modeling_qwen2.Qwen2Model'>
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 23.80it/s]
<class 'transformers_modules.Alibaba-NLP.gte-Qwen2-1.5B-instruct.a9af15a6372d7d6b25e9fb07c2ccb9e1fe645644.modeling_qwen.Qwen2Model'>
<class 'transformers_modules.Alibaba-NLP.gte-Qwen2-1.5B-instruct.a9af15a6372d7d6b25e9fb07c2ccb9e1fe645644.modeling_qwen.Qwen2Model'>
```
**Output with this PR**
```
<class 'transformers.models.qwen2.modeling_qwen2.Qwen2Model'>
Loading checkpoint shards: 100%|███████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 23.64it/s]
<class 'transformers_modules.Alibaba-NLP.gte-Qwen2-1.5B-instruct.a9af15a6372d7d6b25e9fb07c2ccb9e1fe645644.modeling_qwen.Qwen2Model'>
<class 'transformers.models.qwen2.modeling_qwen2.Qwen2Model'>
```
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
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Library:
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- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
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- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
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} | # What does this PR do?
<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.
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<!-- Remove if not applicable -->
As suggested in this [issue](https://github.com/huggingface/transformers/issues/36979), this PR updates the documentation for Aria model card.
## Before submitting
- [x] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [x] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [x] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
@stevhliu
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- graph models: @clefourrier
Library:
- flax: @gante and @Rocketknight1
- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- pipelines: @Rocketknight1
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @zach-huggingface and @SunMarc
- chat templates: @Rocketknight1
Integrations:
- deepspeed: HF Trainer/Accelerate: @SunMarc @zach-huggingface
- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization (bitsandbytes, autogpt): @SunMarc @MekkCyber
Documentation: @stevhliu
HF projects:
- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)
Maintained examples (not research project or legacy):
- Flax: @Rocketknight1
- PyTorch: See Models above and tag the person corresponding to the modality of the example.
- TensorFlow: @Rocketknight1
-->
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} | # What does this PR do?
`Gemma3IntegrationTest` is never actually run. Before #36820, no access to gated repo. Before #38093, `@require_read_token` didn't work on test class.
All tests are now passing on T4 (with torch 2.7) and A10 (with torch 2.6) - if not skipped by the conditions | {
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} | # What does this PR do?
This PR adds a detailed and standardized model card for **ConvBERT** to improve Hugging Face Transformers documentation.
Includes:
- Model Overview and Architecture
- Training objective and dataset details
- Use cases and limitations
- Code usage examples via `pipeline`, `AutoModel`, and CLI
- Quantization and AttentionMaskVisualizer support
- Benchmarks and citation
File added:
- `src/transformers/models/convbert/modelcard.md`
This contribution helps improve model discoverability and provides users with accessible and actionable information about ConvBERT.
cc: @stevhliu (documentation reviewer)
---
Fixes: N/A
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} | # What does this PR do?
Pins Scippy to fix the batch of `TypeError: gaussian_filter() got an unexpected keyword argument 'axes'` issues.
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Ascend NPU
transformers>=4.50.0
torch 2.1
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
When using device_map with Ascend NPU devices in transformers >=4.50.0, loading models fails with assertion errors. The issue occurs because the new loading implementation in _load_state_dict_into_meta_model doesn't properly handle integer device indices for NPU devices, whereas previous versions (<4.50.0) used accelerate.utils.set_module_tensor_to_device which correctly converts integer indices to device strings like "npu:0".
On an Ascend NPU system, attempt to load a model with device mapping:
```
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"gpt2",
device_map="auto", # Or custom device_map with integer indices
torch_dtype=torch.float16
)
```
Observe the failure with stack trace pointing to modeling_utils.py in _load_state_dict_into_meta_model
### Expected behavior
In transformers <=4.49.0, device mapping used accelerate.utils.set_module_tensor_to_device for various device types
<img width="693" alt="Image" src="https://github.com/user-attachments/assets/4abec3a3-6eea-4782-bed7-e8d54116968e" />
In transformers >=4.50.0, the new _load_state_dict_into_meta_model directly uses device values from device_map without converting integer indices to device-specific strings
For NPU devices, integer indices (like 0) are not automatically converted to proper device strings ("npu:0") | {
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} | # What does this PR do?
As per title + some clean up on generation tests
Didn't expect this PR to grow so large. Now all vision LLMs can accept `inputs_embeds` as an input along with `pixel_values`. The tests are all passing on my end | {
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} | # What does this PR do?
Fixes #36254
And this PR makes it possible to infer bnb-4bit Flux with hqq-4bit quantized T5 model in the diffusers pipeline.
I uploaded models to [HighCWu/FLUX.1-dev-bnb-hqq-4bit](https://huggingface.co/HighCWu/FLUX.1-dev-bnb-hqq-4bit).
I use hqq-4bit to quantize T5. It seems to quantize T5 better than bnb-4bit.
The issue fixed by this PR mentioned my old model repo _Originally posted by @Rocketknight1 in [#36254](https://github.com/huggingface/transformers/issues/36254#issuecomment-2665917926)_ that I used some hacked code to quantize a T5 model, and used some hacked code to load the quantized model. Later, the hqq code base was upgraded and the old code was no longer available. So I submitted this PR to make the new format of hqq-4bit T5 model work properly in the diffusers pipeline.
@SunMarc
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# What does this PR do?
<!--
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<!-- Remove if not applicable -->
Fixes # (issue)
Fix trainer.py not showing signature columns
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.
@zach-huggingface and @SunMarc
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Ran into this bug https://github.com/huggingface/transformers/issues/38329. Tried installing from main to get access to this fix https://github.com/huggingface/transformers/pull/38376, but ran into this bug
```
(python) MacBookPro~/projects/bastet[embedding_app_python L|✚2…1] % python3 embeddingApp/embedding_app/main.py
Traceback (most recent call last):
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/embedding_app/main.py", line 30, in <module>
model: EmbeddingModel = create_embedding_model()
^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/embedding_app/utils.py", line 9, in create_embedding_model
return SentenceTransformer(configs.EMBEDDING_MODEL_NAME, cache_folder=str(configs.EMBEDDING_MODEL_CACHE_DIR))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/sentence_transformers/SentenceTransformer.py", line 309, in __init__
modules, self.module_kwargs = self._load_sbert_model(
^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/sentence_transformers/SentenceTransformer.py", line 1824, in _load_sbert_model
module = module_class.load(module_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/sentence_transformers/models/CLIPModel.py", line 98, in load
return CLIPModel(model_name=input_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/sentence_transformers/models/CLIPModel.py", line 18, in __init__
self.model = transformers.CLIPModel.from_pretrained(model_name)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/transformers/modeling_utils.py", line 314, in _wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/transformers/modeling_utils.py", line 4695, in from_pretrained
) = cls._load_pretrained_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/transformers/modeling_utils.py", line 4954, in _load_pretrained_model
load_state_dict(checkpoint_files[0], map_location="meta", weights_only=weights_only).keys()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/transformers/modeling_utils.py", line 559, in load_state_dict
check_torch_load_is_safe()
File "/Users/matthewdornfeld/projects/bastet/embeddingApp/build/python/lib/python3.11/site-packages/transformers/utils/import_utils.py", line 1417, in check_torch_load_is_safe
raise ValueError(
ValueError: Due to a serious vulnerability issue in `torch.load`, even with `weights_only=True`, we now require users to upgrade torch to at least v2.6 in order to use the function. This version restriction does not apply when loading files with safetensors.
```
I am on an x86 Mac and cannot upgrade to Torch 2.6
```
(python) MacBookPro~/projects/bastet % pip3 install torch==2.6.0
ERROR: Could not find a version that satisfies the requirement torch==2.6.0 (from versions: 2.0.0, 2.0.1, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.2.2)
ERROR: No matching distribution found for torch==2.6.0
```
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
1. Install from main, version 4.1.0, 3.4.0, or 2.7.0 in PyPi. It seems like this change was pushed to every version?
2. Attempt to load SentenceTransformer("clip-ViT-B-32", cache_folder="/tmp/cache")
### Expected behavior
SentenceTransformer should load the clip model without a bug | {
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} | [] | closed | false | null | [] | null | [] | 2025-05-29T03:07:42 | 2025-07-02T05:22:34 | 2025-06-06T15:14:01 | CONTRIBUTOR | null | null | false | {
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} | Hi @SunMarc . The autoawq support CPU and XPU now, we should update the awq torch_dtype because CPU/XPU support both bf16/fp16. For CPU, the performance is better on bf16 in most cases. | {
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] | closed | false | null | [] | null | [] | 2025-05-29T02:35:21 | 2025-06-04T02:18:07 | 2025-06-04T02:18:06 | NONE | null | null | null | null | ### System Info
transformers==4.51.3
Ascend 910B
torch==2.5.1
torch-npu==2.5.1
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
I want to use transformers/quantizers/auto.py::**register_quantization_config** and **register_quantizer** functions to add my own quantizaiton method, but both of them would not add new method enum to the transformers/utils/quantization_config.py::**QuantizationMethod**, which is used to check if the new method is valid.
Therefore, thre register functions could not register the new method corretly. Users have to write code to manually add Enum to the **QuantizationMethod**.
### Expected behavior
After registers functions called, the new method item should be added to **QuantizationMethod** as well.
@SunMarc @MekkCyber @ivarflakstad | {
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} | [] | closed | false | null | [] | null | [] | 2025-05-29T01:16:09 | 2025-08-01T16:22:18 | 2025-08-01T16:22:17 | CONTRIBUTOR | null | null | false | {
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} | This PR adds support for `GLPNImageProcessorFast`, enabling fast inference for the GLPN model using TorchVision backends.
**Changes:**
- Added `GLPNImageProcessorFast` implementation in `image_processing_glpn_fast.py`.
- Updated `__init__.py` to include the new fast processor in `import_structure`.
- Verified functional equivalence with the slow processor (`max abs diff < 1e-7`).
- Added corresponding tests; all relevant test cases pass or skip cleanly.
Let me know if further refactoring or docs are needed. | {
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https://api.github.com/repos/huggingface/transformers/issues/38460 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38460/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38460/comments | https://api.github.com/repos/huggingface/transformers/issues/38460/events | https://github.com/huggingface/transformers/pull/38460 | 3,098,866,650 | PR_kwDOCUB6oc6YC2h9 | 38,460 | Add Fast Image Processor for GLPN (GLPNImageProcessorFast) | {
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} | Summary
This PR adds a fast image processor for the GLPN model (GLPNImageProcessorFast) using the PyTorch/TorchVision backend. This brings improved performance for inference workflows using GLPNModel and aligns with fast processor support across other vision models in the library.
Changes
Introduced GLPNImageProcessorFast in image_processing_glpn_fast.py
Registered the fast image processor in __init__.py
Updated import_structure to support lazy loading
Added comprehensive tests under tests/models/glpn/test_image_processing_glpn.py including:
shape consistency checks
preprocessing equivalence
I/O serialization
Verified outputs are numerically equivalent to slow processor (max abs diff ≈ 5.96e-08)
Motivation
Adding a fast processor improves speed and consistency with other vision models in the 🤗 Transformers library, especially when leveraging TorchScript or exporting for deployment.
Notes
All tests pass (pytest tests/models/glpn)
Skipped tests requiring CUDA have been noted accordingly
Follows the patterns in other models with fast/slow processors | {
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https://api.github.com/repos/huggingface/transformers/issues/38459 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38459/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38459/comments | https://api.github.com/repos/huggingface/transformers/issues/38459/events | https://github.com/huggingface/transformers/pull/38459 | 3,098,761,844 | PR_kwDOCUB6oc6YCggo | 38,459 | Cleanup `BatchFeature` and `BatchEncoding` | {
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} | Simplifies the implementation of `BatchFeature` and `BatchEncoding` classes:
- Use dict comprehension to create dict e9c081ffe9436f65c310fe301da80f31fe4b6b06
- Fix type annotation 0189068023e3724af564e16215d5649b7fb1c36f
- Remove methods that are already implemented in the `UserDict` parent class 8b145b46278a776ff7d33e2719e5a44366332339 | {
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torch.get_default_device() is not an official PyTorch API (even in 2.2), but it’s being called inside modeling_utils.py in version 4.52.1.
get_torch_context_manager_or_global_device() function is calling the following API
Error - Exception : module 'torch' has no attribute 'get_default_device'
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
HuggingFaceEmbeddings(model_name=self._model_name)
Initialization fails with Exception : module 'torch' has no attribute 'get_default_device'
### Expected behavior
Can be replaced with
default_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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] | closed | false | null | [] | null | [] | 2025-05-28T19:50:20 | 2025-05-28T19:52:50 | 2025-05-28T19:52:48 | CONTRIBUTOR | null | null | null | null | ### System Info
When trying to quantize w/ torchao on 4.52.3 and not having bits and bytes installed I am getting:
```Py
python ao/prep_model.py --model_name "facebook/opt-125m" --quant_type "fp8" --granularity per_row --push_to_hub True
Using Model name: facebook/opt-125m
Quantization type: fp8
Loading and quantizing model...
WARNING:bitsandbytes.cextension:Could not find the bitsandbytes CUDA binary at PosixPath('/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/bitsandbytes/libbitsandbytes_cuda128.so')
WARNING:bitsandbytes.cextension:The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
Traceback (most recent call last):
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/utils/import_utils.py", line 1967, in _get_module
return importlib.import_module("." + module_name, self.__name__)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/importlib/__init__.py", line 90, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1387, in _gcd_import
File "<frozen importlib._bootstrap>", line 1360, in _find_and_load
File "<frozen importlib._bootstrap>", line 1331, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 935, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 999, in exec_module
File "<frozen importlib._bootstrap>", line 488, in _call_with_frames_removed
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/integrations/bitsandbytes.py", line 21, in <module>
import bitsandbytes as bnb
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/bitsandbytes/__init__.py", line 15, in <module>
from .nn import modules
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/bitsandbytes/nn/__init__.py", line 21, in <module>
from .triton_based_modules import (
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/bitsandbytes/nn/triton_based_modules.py", line 7, in <module>
from bitsandbytes.triton.int8_matmul_mixed_dequantize import (
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/bitsandbytes/triton/int8_matmul_mixed_dequantize.py", line 12, in <module>
from triton.ops.matmul_perf_model import early_config_prune, estimate_matmul_time
ModuleNotFoundError: No module named 'triton.ops'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/drisspg/meta/my_scripts/ao/prep_model.py", line 272, in <module>
CLI(main)
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/jsonargparse/_cli.py", line 27, in CLI
return auto_cli(*args, _stacklevel=3, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/jsonargparse/_cli.py", line 106, in auto_cli
return _run_component(components, init)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/jsonargparse/_cli.py", line 227, in _run_component
return component(**cfg)
^^^^^^^^^^^^^^^^
File "/home/drisspg/meta/my_scripts/ao/prep_model.py", line 190, in main
quantized_model = AutoModelForCausalLM.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py", line 571, in from_pretrained
return model_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/modeling_utils.py", line 279, in _wrapper
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/modeling_utils.py", line 4370, in from_pretrained
hf_quantizer.preprocess_model(
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/quantizers/base.py", line 224, in preprocess_model
return self._process_model_before_weight_loading(model, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/quantizers/quantizer_torchao.py", line 185, in _process_model_before_weight_loading
self.modules_to_not_convert = self.get_modules_to_not_convert(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/quantizers/base.py", line 265, in get_modules_to_not_convert
from ..integrations import get_keys_to_not_convert
File "<frozen importlib._bootstrap>", line 1412, in _handle_fromlist
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/utils/import_utils.py", line 1955, in __getattr__
module = self._get_module(self._class_to_module[name])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/drisspg/.conda/envs/vllm/lib/python3.12/site-packages/transformers/utils/import_utils.py", line 1969, in _get_module
raise RuntimeError(
RuntimeError: Failed to import transformers.integrations.bitsandbytes because of the following error (look up to see its traceback):
No module named 'triton.ops'
```
cc @SunMarc @MekkCyber
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```Py
#!/usr/bin/env python3
# SPDX-License-Identifier: Apache-2.0
"""
Script for quantizing LLM models with TorchAO.
Supports various quantization configurations and model types.
"""
import os
import random
import numpy as np
import torch
import time
from pathlib import Path
from typing import Optional, Literal
from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
from transformer_nuggets.utils.benchmark import benchmark_cuda_function_in_microseconds
from torchao.quantization.quant_api import (
Float8DynamicActivationFloat8WeightConfig,
Int4WeightOnlyConfig,
Int8WeightOnlyConfig,
Int8DynamicActivationInt8WeightConfig,
PerRow,
PerTensor,
GemliteUIntXWeightOnlyConfig,
Int4DynamicActivationInt4WeightConfig,
Int8DynamicActivationInt4WeightConfig,
CutlassInt4PackedLayout,
)
from torchao.prototype.mx_formats.mx_subclass import MXFPInferenceConfig
from torchao.prototype.mx_formats import MXGemmKernelChoice
from jsonargparse import CLI, Namespace
from rich import print
# Set seeds for reproducibility
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def get_quantization_config(args):
"""Create TorchAo quantization config based on provided args."""
granularity_mapping = {
"per_row": PerRow(),
"per_tensor": PerTensor(),
}
gran = granularity_mapping[args.granularity]
match args.quant_type:
case "autoquant":
return TorchAoConfig("autoquant", min_sqnr=args.min_sqnr)
case "fp8":
return TorchAoConfig(
Float8DynamicActivationFloat8WeightConfig(granularity=gran)
)
case "int4_weight_only":
return TorchAoConfig(Int4WeightOnlyConfig(group_size=128))
case "int8_weight_only":
return TorchAoConfig(Int8WeightOnlyConfig())
case "int8_dynamic_act_int8_weight":
return TorchAoConfig(Int8DynamicActivationInt8WeightConfig())
case "gemlite":
return TorchAoConfig(GemliteUIntXWeightOnlyConfig())
case "A4W4":
return TorchAoConfig(Int4DynamicActivationInt4WeightConfig())
case "A8W4":
return TorchAoConfig(
Int8DynamicActivationInt4WeightConfig(layout=CutlassInt4PackedLayout())
)
case "mxfp8":
return TorchAoConfig(MXFPInferenceConfig())
case "mxfp4":
return TorchAoConfig(
MXFPInferenceConfig(
activation_dtype=torch.float4_e2m1fn_x2,
weight_dtype=torch.float4_e2m1fn_x2,
block_size=32,
gemm_kernel_choice=MXGemmKernelChoice.CUTLASS,
)
)
case _:
raise ValueError(f"Unsupported quantization type: {args.quant_type}")
def benchmark_model(model, input_ids, max_new_tokens, name=""):
"""Benchmark model generation speed."""
try:
time_ms = benchmark_cuda_function_in_microseconds(
model.generate,
**input_ids,
max_new_tokens=max_new_tokens,
cache_implementation="static",
)
tokens_per_second = max_new_tokens / (time_ms / 1000)
print(
f"{name} model: {time_ms:.2f}ms for {max_new_tokens} tokens ({tokens_per_second:.2f} tokens/sec)"
)
return time_ms
except ImportError:
# Fallback to simple timing if inductor utils not available
print("torch._inductor.utils not available, using simple timing")
start = time.time()
model.generate(
**input_ids, max_new_tokens=max_new_tokens, cache_implementation="static"
)
elapsed = (time.time() - start) * 1000 # ms
tokens_per_second = max_new_tokens / (elapsed / 1000)
print(
f"{name} model: {elapsed:.2f}ms for {max_new_tokens} tokens ({tokens_per_second:.2f} tokens/sec)"
)
return elapsed
def main(
model_name: str = "facebook/opt-125m",
output_dir: Optional[str] = None,
push_to_hub: bool = False,
quant_type: Literal[
"float8_dynamic_act_float8_weight",
"int4_weight_only",
"int8_weight_only",
"int8_dynamic_act_int8_weight",
"autoquant",
"gemlite",
"A4W4",
"A8W4",
"fp8",
"mxfp4",
] = "float8_dynamic_act_float8_weight",
granularity: Literal["per_row", "per_tensor"] = "per_row",
min_sqnr: Optional[float] = None,
max_new_tokens: int = 64,
benchmark: bool = False,
bench_tokens: int = 100,
device_map: str = "cuda",
):
"""
Quantize a model with TorchAO and test its performance.
Args:
model_name: Model to quantize (e.g., meta-llama/Meta-Llama-3-8B, facebook/opt-125m)
output_dir: Directory to save the quantized model
push_to_hub: HF Hub repo name to push the model (e.g., 'your-username/model-name')
quant_type: Quantization type to use
granularity: Quantization granularity
min_sqnr: Minimum SQNR for autoquant
max_new_tokens: Max tokens to generate for testing
benchmark: Run benchmarking comparison
bench_tokens: Number of tokens to generate for benchmarking
device_map: Device mapping strategy
"""
# Set seed before creating the model
set_seed(42)
# Set default output directory based on model base name if not provided
if output_dir is None:
model_base_name = model_name.split("/")[-1]
output_dir = f"data/{quant_type}-{model_base_name}"
# Convert to args-like object for compatibility with the rest of the code
args = Namespace(
model_name=model_name,
output_dir=output_dir,
push_to_hub=push_to_hub,
quant_type=quant_type,
granularity=granularity,
min_sqnr=min_sqnr,
max_new_tokens=max_new_tokens,
benchmark=benchmark,
bench_tokens=bench_tokens,
device_map=device_map,
)
print(f"Using Model name: {args.model_name}")
print(f"Quantization type: {args.quant_type}")
# Create output directory
output_dir = Path(args.output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
# Get quantization config
quantization_config = get_quantization_config(args)
# Load and quantize model
print("Loading and quantizing model...")
quantized_model = AutoModelForCausalLM.from_pretrained(
args.model_name,
torch_dtype="bfloat16",
device_map=args.device_map,
quantization_config=quantization_config,
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
# Test prompts
prompts = [
"Why is Pytorch 2.0 the best machine learning compiler?",
"Hello, my name is",
"The president of the United States is",
"The capital of France is",
"The future of AI is",
]
# Test generation
print("\nTesting quantized model generation...")
input_ids = tokenizer(prompts, return_tensors="pt", padding=True).to(quantized_model.device)
outputs = quantized_model.generate(**input_ids, max_new_tokens=args.max_new_tokens)
for i, (prompt, output) in enumerate(zip(prompts, outputs)):
generated_text = tokenizer.decode(output, skip_special_tokens=True)
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
# Save quantized model
print(f"\n📁Saving quantized model to: {output_dir}")
quantized_model.save_pretrained(output_dir, safe_serialization=False)
tokenizer.save_pretrained(output_dir)
# Push to HuggingFace hub if requested
if args.push_to_hub:
# Get model name from output_dir
model_name = output_dir.name
hub_path = f"drisspg/ao_models/{model_name}"
print(f"Pushing model to HuggingFace Hub: {hub_path}")
quantized_model.push_to_hub(model_name, safe_serialization=False)
tokenizer.push_to_hub(model_name)
# Load saved model to verify
print("\nLoading saved quantized model to verify...")
loaded_model = AutoModelForCausalLM.from_pretrained(
output_dir, device_map=args.device_map, torch_dtype="auto"
)
# Test loaded model with first prompt
test_prompt = prompts[0]
input_ids = tokenizer(test_prompt, return_tensors="pt").to(loaded_model.device)
output = loaded_model.generate(**input_ids, max_new_tokens=args.max_new_tokens)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(f"Verification - Prompt: {test_prompt!r}, Generated text: {generated_text!r}")
# Benchmark if requested
if args.benchmark:
print("\nBenchmarking models...")
# Benchmark quantized model
print("Benchmarking quantized model:")
quant_time = benchmark_model(
loaded_model, input_ids, args.bench_tokens, f"Quantized ({args.quant_type})"
)
# Load and benchmark original model in BF16
print("\nLoading original model in BF16 for comparison...")
bf16_model = AutoModelForCausalLM.from_pretrained(
args.model_name, device_map=args.device_map, torch_dtype=torch.bfloat16
)
# Benchmark original model
print("Benchmarking original BF16 model:")
bf16_time = benchmark_model(bf16_model, input_ids, args.bench_tokens, "BF16")
# Calculate speedup
speedup = bf16_time / quant_time if quant_time > 0 else 0
print(f"\nSpeedup: {speedup:.2f}x")
print("\nQuantization process completed successfully.")
if __name__ == "__main__":
CLI(main)
```
### Expected behavior
Not have a dependency on BnB | {
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https://api.github.com/repos/huggingface/transformers/issues/38453 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38453/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38453/comments | https://api.github.com/repos/huggingface/transformers/issues/38453/events | https://github.com/huggingface/transformers/pull/38453 | 3,098,334,114 | PR_kwDOCUB6oc6YBDRe | 38,453 | [Qwen2.5-Omni] Fix dtype of cos,sin when used with flash attention | {
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} | [] | closed | false | null | [] | null | [] | 2025-05-28T19:18:37 | 2025-05-29T18:25:12 | 2025-05-29T18:24:41 | CONTRIBUTOR | null | null | false | {
"url": "https://api.github.com/repos/huggingface/transformers/pulls/38453",
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"patch_url": "https://github.com/huggingface/transformers/pull/38453.patch",
"merged_at": "2025-05-29T18:24:41"
} | # What does this PR do?
Fixes a dtype mismatch in **Qwen2.5-Omni** models when Flash Attention is enabled:
`sin` and `cos` positional embeddings are now explicitly cast to the same dtype as the working tensor.
**Related issue:** [#38451](https://github.com/huggingface/transformers/issues/38451)
Thanks @zucchini-nlp for validating the fix!
## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
[documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
[here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?
## Who can review?
Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR. | {
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https://api.github.com/repos/huggingface/transformers/issues/38452 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38452/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38452/comments | https://api.github.com/repos/huggingface/transformers/issues/38452/events | https://github.com/huggingface/transformers/issues/38452 | 3,098,293,224 | I_kwDOCUB6oc64rDPo | 38,452 | Memory saving by upcasting logits for only non-ignored positions | {
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] | open | false | null | [] | null | [] | 2025-05-28T18:58:52 | 2025-05-29T12:38:15 | null | NONE | null | null | null | null | ### Feature request
In [`loss_utils.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/loss/loss_utils.py), logits are upcasted for float32 for some losses. This can waste memory for cases where certain labels are `ignore_index`. This is especially true for fine tuning cases where one chooses to calculate loss only on the completion. They would keep label as -100 for prompt tokens and upcasting those logits would be unnecessary. We can instead call `logits.float()` after we have our final labels. This would be especially useful for `ForCausalLMLoss` as that seems to be the most likely use case.
### Motivation
When fine tuning a causal LM, one can choose to calculate loss only on the completion, thus setting labels for prompt tokens to be -100. Upcasting logits at those positions when calculating loss is not needed. Avoiding that can save memory. Most likely use case is `ForCausalLMLoss`.
### Your contribution
An example for `ForCausalLMLoss`:
```
def ForCausalLMLoss(
logits,
labels,
vocab_size: int,
num_items_in_batch: Optional[int] = None,
ignore_index: int = -100,
shift_labels: Optional[torch.Tensor] = None,
**kwargs,
) -> torch.Tensor:
# Don't upcast yet
# logits = logits.float()
if shift_labels is None:
# Shift so that tokens < n predict n
labels = nn.functional.pad(labels, (0, 1), value=ignore_index)
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
logits = logits.view(-1, vocab_size)
shift_labels = shift_labels.view(-1)
# Upcast to float if we need to compute the loss to avoid potential precision issues
# Now that we have our final labels, take only the useful logits and then upcast
logits = logits[shift_labels != ignore_index]
shift_labels = shift_labels[shift_labels != ignore_index]
logits = logits.float()
# Enable model parallelism
shift_labels = shift_labels.to(logits.device)
# Calculate loss on truncated logits and labels
loss = fixed_cross_entropy(logits, shift_labels, num_items_in_batch, ignore_index, **kwargs)
return loss
```
We can do something similar in `ForMaskedLMLoss` on line 83 instead of 77. `ForTokenClassification` does not take `ignore_index` as an argument but we can still do the same here because `fixed_cross_entropy` does take `ignore_index`.
Another alternative was to move the upcasting to inside `fixed_cross_entropy` but a few losses don't do that. So, that might change/break existing things.
Let me know if this change sounds good. I can submit a PR. | null | {
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https://api.github.com/repos/huggingface/transformers/issues/38451 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38451/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38451/comments | https://api.github.com/repos/huggingface/transformers/issues/38451/events | https://github.com/huggingface/transformers/issues/38451 | 3,098,080,975 | I_kwDOCUB6oc64qPbP | 38,451 | [Bug - Qwen2.5-Omni] FlashAttention 2 BF16 dtype mismatch persists in `apply_rotary_pos_emb_flashatt` | {
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] | closed | false | null | [] | null | [] | 2025-05-28T17:36:48 | 2025-05-29T18:37:10 | 2025-05-29T18:37:10 | CONTRIBUTOR | null | null | null | null | ### System Info
File: modeling_qwen2_5_omni.py
```
def _apply_rotary_pos_emb_flashatt(self, tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
tensor_ = tensor.float()
cos = freqs.cos() # .type_as(tensor_)
sin = freqs.sin() # .type_as(tensor_)
output = apply_rotary_emb(tensor_, cos, sin).type_as(tensor)
return output
```
Steps to Reproduce：
1. Use Qwen2.5-Omni model
2. Set attn_implementation="flash_attention_2"
3. Enable BF16 training
4. Call the model with any input that reaches rotary embedding
5. Observe AssertionError from FlashAttention
```
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py", line 1070, in apply_rotary_pos_emb_flashatt
[rank0]: output = apply_rotary_emb(tensor, cos, sin).type_as(tensor)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/flash_attn/layers/rotary.py", line 122, in apply_rotary_emb
[rank0]: return ApplyRotaryEmb.apply(
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/torch/autograd/function.py", line 575, in apply
[rank0]: return super().apply(*args, **kwargs) # type: ignore[misc]
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/flash_attn/layers/rotary.py", line 48, in forward
[rank0]: out = apply_rotary(
[rank0]: ^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/flash_attn/ops/triton/rotary.py", line 176, in apply_rotary
[rank0]: x.dtype == cos.dtype
[rank0]: AssertionError: Input and cos/sin must have the same dtype, got torch.float32 and torch.bfloat16
```
### Proposed fix
```diff
def _apply_rotary_pos_emb_flashatt(self, tensor, freqs):
tensor_ = tensor.float()
- cos = freqs.cos()
- sin = freqs.sin()
+ cos = freqs.cos().type_as(tensor_)
+ sin = freqs.sin().type_as(tensor_)
output = apply_rotary_emb(tensor_, cos, sin).type_as(tensor)
return output
```
I originally reported this problem in [Issue #205](https://github.com/QwenLM/Qwen2.5-Omni/issues/205) and proposed a fix in this comment:
<https://github.com/QwenLM/Qwen2.5-Omni/issues/205#issuecomment-2911885852>.
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
File: modeling_qwen2_5_omni.py
```
def _apply_rotary_pos_emb_flashatt(self, tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
tensor_ = tensor.float()
cos = freqs.cos() # .type_as(tensor_)
sin = freqs.sin() # .type_as(tensor_)
output = apply_rotary_emb(tensor_, cos, sin).type_as(tensor)
return output
```
Steps to Reproduce：
1. Use Qwen2.5-Omni model
2. Set attn_implementation="flash_attention_2"
3. Enable BF16 training
4. Call the model with any input that reaches rotary embedding
5. Observe AssertionError from FlashAttention
```
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py", line 1070, in apply_rotary_pos_emb_flashatt
[rank0]: output = apply_rotary_emb(tensor, cos, sin).type_as(tensor)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/flash_attn/layers/rotary.py", line 122, in apply_rotary_emb
[rank0]: return ApplyRotaryEmb.apply(
[rank0]: ^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/torch/autograd/function.py", line 575, in apply
[rank0]: return super().apply(*args, **kwargs) # type: ignore[misc]
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/flash_attn/layers/rotary.py", line 48, in forward
[rank0]: out = apply_rotary(
[rank0]: ^^^^^^^^^^^^^
[rank0]: File "/research/d1/gds/zhxing/anaconda3/envs/echo-r1/lib/python3.11/site-packages/flash_attn/ops/triton/rotary.py", line 176, in apply_rotary
[rank0]: x.dtype == cos.dtype
[rank0]: AssertionError: Input and cos/sin must have the same dtype, got torch.float32 and torch.bfloat16
```
### Expected behavior
### Proposed fix
```diff
def _apply_rotary_pos_emb_flashatt(self, tensor, freqs):
tensor_ = tensor.float()
- cos = freqs.cos()
- sin = freqs.sin()
+ cos = freqs.cos().type_as(tensor_)
+ sin = freqs.sin().type_as(tensor_)
output = apply_rotary_emb(tensor_, cos, sin).type_as(tensor)
return output
``` | {
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] | open | false | null | [] | null | [] | 2025-05-28T16:11:59 | 2025-06-01T17:05:37 | null | CONTRIBUTOR | null | null | null | null | ### Feature request
Hi 🤗 team and contributors,
I'm currently exploring ways to extend the `transformers` library to support **temporal grounding** — the task of identifying a [start, end] timestamp segment in a video given a natural language query.
While Hugging Face already supports pipelines like `video-classification`, `image-to-text`, and `zero-shot-image-classification`, it seems there is currently **no pipeline or task definition for video moment retrieval / temporal grounding** tasks.
### Motivation
As multimodal models become increasingly capable of understanding both vision and language (e.g., BLIP2, VideoChatGPT, TimeChat), there is a growing demand for models that can not only recognize **what** is happening in a video, but also **when** it happens.
Temporal Grounding — the task of identifying a relevant moment span [start, end] in a video given a natural language query — is a fundamental step in making video-language models temporally aware.
> For example:
> Given a query like "the person starts cooking", a temporal grounding model is expected to localize the clip where this action occurs. --> [3.5, 8.9]
This capability is critical for a wide range of downstream tasks:
- **Video Question Answering** (When does X happen?)
- **Video Summarization and Highlighting**
- **Instruction-following agents in videos**
### Your contribution
I’d love to know if there is any ongoing work on this?
If not, I’d love to propose an initiative to explore what it might look like to support temporal grounding as a task within the `transformers` library — either through a new pipeline or modular components that make it easier to build moment retrieval models using Hugging Face tools.
I’m also curious if any other contributors might be interested in collaborating on this idea. I’d be very happy to contribute and work with others who are exploring similar directions. | null | {
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} | ## Summary
Fixes a TypeError in the `save_pretrained` error handling routine that occurs when `shared_names` contains shared tensors.
## Problem
In `src/transformers/modeling_utils.py` line 3747, the code attempts to call `set(shared_names)` where `shared_names` is `List[Set[str]]`. This raises:
TypeError: unhashable type: 'set'
The bug occurs because you can't create a set from a list of sets (sets are unhashable).
## Solution
Replace `error_names.append(set(shared_names))` with `error_names.extend(shared_names)` to properly handle the `List[Set[str]]` structure.
This preserves the intended behavior where `error_names` is a list of sets, with each set representing a group of tensors that share memory/storage.
## Testing
- [x] Verified syntax and imports work correctly
- [x] Tested the logic with sample data
- [x] Confirmed backward compatibility
- [x] Single line change with minimal impact
## Related Issue
Fixes #38422
## Changes
- `src/transformers/modeling_utils.py`: Replace `append(set(shared_names))` with `extend(shared_names)` | {
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I check the shape of the inputs and find follows:
In [1]: logits.shape
Out[1]: torch.Size([4, 896, 152064])
In [2]: labels.shape
Out[2]: torch.Size([4, 896])
In [3]: num_items_in_batch
Out[3]: 4390
Why is 4390>4*896? | {
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} | # What does this PR do?
`next_token` was set but not propagated correctly to the `GenerationOutput`, thus making streaming non-functional.
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} | # What does this PR do?
Allows for multi-turn style requests in continuous batching. Cache is retained and has to be manually cleared by the owner of the `ContinuousBatchingManager` instance.
For power users mostly.
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} | ## Summary
Addresses issue #38318 by adding configurable normalization schemes to SigLIP image processors, allowing users to choose between official SigLIP normalization and traditional ImageNet normalization while maintaining full backwards compatibility.
## Problem
Users reported that SigLIP models may perform better for feature clustering when using traditional ImageNet normalization values instead of the official SigLIP values:
- **Official SigLIP**: `mean=[0.5, 0.5, 0.5]`, `std=[0.5, 0.5, 0.5]`
- **Traditional ImageNet**: `mean=[0.485, 0.456, 0.406]`, `std=[0.229, 0.224, 0.225]`
However, changing the default values would break backwards compatibility and contradict official SigLIP documentation.
## Solution
Added a `normalization_scheme` parameter that provides user choice without breaking existing functionality:
### Key Features:
- **Backwards Compatible**: Default behavior unchanged - uses official SigLIP values
- **Configurable**: Choose between `"siglip"` and `"imagenet"` schemes
- **Auto-Detection**: Automatically detects scheme from existing configurations
- **Manual Override**: Custom values still supported via `image_mean`/`image_std`
- **Consistent**: Works across both fast and slow processors
## Usage Examples
### Default Usage (No Changes Required)
```python
# Uses official SigLIP normalization [0.5, 0.5, 0.5] - unchanged behavior
processor = SiglipImageProcessor()
```
### Better Clustering Performance
```python
# Use ImageNet normalization for potentially better clustering
processor = SiglipImageProcessor(normalization_scheme="imagenet")
```
### Auto-Detection from Configs
```python
# Automatically detects ImageNet scheme from existing config values
config = {"image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225]}
processor = SiglipImageProcessor.from_dict(config)
print(processor.normalization_scheme) # "imagenet"
```
## Files Changed
- `src/transformers/models/siglip/image_processing_siglip.py`
- `src/transformers/models/siglip/image_processing_siglip_fast.py`
## Testing
The implementation has been validated to ensure:
- Default behavior remains unchanged
- ImageNet scheme provides correct values
- Auto-detection works properly
- Manual overrides take precedence
- Both fast and slow processors behave consistently
## Related
Fixes #38318
This solution provides the best of both worlds: researchers can easily access ImageNet normalization for better clustering while maintaining official SigLIP compatibility by default. | null | {
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} | This PR splits the `transformers chat` frontend from its backend (now moved to `transformers serve`).
We take this opportunity to have `transformers serve` act as a server with an OpenAI-compatible API, via http, using SSE to stream tokens.
You can take it for a spin either by spawning a server:
```
transformers serve
```
and then a chat on top of it in another terminal:
```
transformers chat meta-llama/Llama-3.2-3b-Instruct
``` | {
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Package Version Editable project location
------------------------- -------------- -------------------------
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cymem 2.0.10
Cython 3.0.11
dashscope 1.22.2
datasets 3.1.0
debugpy 1.8.13
decorator 5.2.1
defusedxml 0.7.1
dill 0.3.8
distro 1.9.0
docker-pycreds 0.4.0
eval_type_backport 0.2.2
exceptiongroup 1.2.2
executing 2.2.0
faiss-gpu 1.7.2
fastapi 0.115.6
fastjsonschema 2.21.1
filelock 3.16.1
flashrag-dev 0.1.4.dev0 /home/wmz/FlashRAG
flatbuffers 24.3.25
fonttools 4.56.0
fqdn 1.5.1
frozenlist 1.5.0
fschat 0.2.36
fsspec 2024.9.0
gitdb 4.0.11
GitPython 3.1.43
h11 0.14.0
hf-xet 1.1.2
httpcore 1.0.7
httpx 0.28.0
huggingface-hub 0.32.2
humanfriendly 10.0
idna 3.10
importlib_metadata 8.6.1
importlib_resources 6.5.2
ipykernel 6.29.5
ipython 8.18.1
ipywidgets 8.1.5
isoduration 20.11.0
jedi 0.19.2
Jinja2 3.1.4
jiter 0.8.0
joblib 1.4.2
json5 0.10.0
jsonlines 4.0.0
jsonpointer 3.0.0
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jupyter 1.1.1
jupyter_client 8.6.3
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jupyter_core 5.7.2
jupyter-events 0.12.0
jupyter-lsp 2.2.5
jupyter_server 2.15.0
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jupyterlab 4.3.6
jupyterlab_pygments 0.3.0
jupyterlab_server 2.27.3
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kiwisolver 1.4.7
langcodes 3.5.0
language_data 1.3.0
latex2mathml 3.77.0
lightgbm 4.5.0
llvmlite 0.43.0
marisa-trie 1.2.1
markdown-it-py 3.0.0
markdown2 2.5.1
MarkupSafe 3.0.2
matplotlib 3.9.4
matplotlib-inline 0.1.7
mdurl 0.1.2
mistune 3.1.3
modelscope 1.21.0
mpmath 1.3.0
multidict 6.1.0
multiprocess 0.70.16
murmurhash 1.0.11
narwhals 1.15.2
nbclient 0.10.2
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nbformat 5.10.4
nest-asyncio 1.6.0
networkx 3.2.1
nh3 0.2.19
nltk 3.9.1
nmslib 2.1.1
notebook 7.3.3
notebook_shim 0.2.4
numba 0.60.0
numpy 1.26.4
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu12 12.1.0.106
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nvidia-nvtx-cu12 12.1.105
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openai 1.56.2
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overrides 7.7.0
packaging 24.2
pandas 2.2.3
pandocfilters 1.5.1
parso 0.8.4
pathlib_abc 0.1.1
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peft 0.13.2
pexpect 4.9.0
pillow 11.0.0
pip 24.3.1
platformdirs 4.3.7
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prometheus_client 0.21.1
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pyarrow 18.1.0
pybind11 2.6.1
pycparser 2.22
pydantic 2.10.3
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Pygments 2.18.0
pyjnius 1.6.1
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requests 2.32.3
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rich 13.9.4
rouge 1.0.1
rpds-py 0.22.3
safetensors 0.4.6.dev0
scikit-learn 1.6.0
scipy 1.10.1
seaborn 0.13.2
Send2Trash 1.8.3
sentence-transformers 3.3.1
sentencepiece 0.2.0
sentry-sdk 2.29.1
setproctitle 1.3.6
setuptools 75.6.0
shortuuid 1.0.13
six 1.17.0
smart-open 6.4.0
smmap 5.0.1
sniffio 1.3.1
soupsieve 2.6
spacy 3.6.1
spacy-legacy 3.0.12
spacy-loggers 1.0.5
srsly 2.4.8
stack-data 0.6.3
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streamlit 1.40.2
svgwrite 1.4.3
sympy 1.13.1
tenacity 9.0.0
terminado 0.18.1
thinc 8.1.12
threadpoolctl 3.5.0
tiktoken 0.8.0
tinycss2 1.4.0
tokenizers 0.21.1
toml 0.10.2
tomli 2.2.1
torch 2.1.2
tornado 6.4.2
tqdm 4.67.1
traitlets 5.14.3
transformers 4.52.3
triton 2.1.0
trl 0.19.0
typer 0.9.4
types-python-dateutil 2.9.0.20241206
typing_extensions 4.12.2
tzdata 2024.2
uri-template 1.3.0
urllib3 2.2.3
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wandb 0.19.11
wasabi 1.1.3
watchdog 6.0.0
wavedrom 2.0.3.post3
wcwidth 0.2.13
webcolors 24.11.1
webencodings 0.5.1
websocket-client 1.8.0
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widgetsnbextension 4.0.13
xxhash 3.5.0
yarl 1.18.3
zipp 3.21.0
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
import json
import torch
import logging
from datasets import Dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import LoraConfig, TaskType, prepare_model_for_kbit_training
from trl import SFTTrainer, SFTConfig
from tqdm import tqdm
import os
model, tokenizer = prepare_model_and_tokenizer(MODEL_NAME)
# LoRA配置
peft_config = LoraConfig(
r=16,
lora_alpha=32,
target_modules=["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
lora_dropout=0.1,
bias="none",
task_type=TaskType.CAUSAL_LM,
)
# SFT配置
sft_config = SFTConfig(
output_dir=OUTPUT_DIR,
num_train_epochs=3,
per_device_train_batch_size=4,
per_device_eval_batch_size=4,
gradient_accumulation_steps=4,
optim="paged_adamw_8bit",
save_steps=500,
logging_steps=50,
learning_rate=2e-4,
weight_decay=0.001,
fp16=True,
bf16=False,
max_grad_norm=0.3,
warmup_ratio=0.03,
lr_scheduler_type="cosine",
eval_strategy="steps",
eval_steps=500,
save_total_limit=2,
load_best_model_at_end=True,
report_to="none",
max_seq_length=512,
packing=False,
dataset_text_field="text",
)
# SFT训练器
trainer = SFTTrainer(
model=model,
args=sft_config,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
processing_class=tokenizer,
peft_config=peft_config,
formatting_func=None,
)
### Expected behavior
Traceback (most recent call last):
File "/home/wmz/FlashRAG/train_decomposer.py", line 5, in <module>
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments
File "/data/anaconda3/envs/flashrag/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2045, in __getattr__
module = self._get_module(self._class_to_module[name])
File "/data/anaconda3/envs/flashrag/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2075, in _get_module
raise e
File "/data/anaconda3/envs/flashrag/lib/python3.9/site-packages/transformers/utils/import_utils.py", line 2073, in _get_module
return importlib.import_module("." + module_name, self.__name__)
File "/data/anaconda3/envs/flashrag/lib/python3.9/importlib/__init__.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/data/anaconda3/envs/flashrag/lib/python3.9/site-packages/transformers/models/auto/modeling_auto.py", line 21, in <module>
from .auto_factory import (
File "/data/anaconda3/envs/flashrag/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py", line 40, in <module>
from ...generation import GenerationMixin
ImportError: cannot import name 'GenerationMixin' from 'transformers.generation' (/data/anaconda3/envs/flashrag/lib/python3.9/site-packages/transformers/generation/__init__.py)
I installed through "pip install transformers" and the version is 4.52.3 | {
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https://api.github.com/repos/huggingface/transformers/issues/38441 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38441/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38441/comments | https://api.github.com/repos/huggingface/transformers/issues/38441/events | https://github.com/huggingface/transformers/pull/38441 | 3,097,475,893 | PR_kwDOCUB6oc6X-Hvk | 38,441 | [trainer] ensure special tokens in model configs are aligned with tokenizer at train time | {
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} | # What does this PR do?
It's not uncommon to define new special tokens in the tokenizer at fine-tuning time. e.g. for rounds of conversations in a chat LLM. However, this is a source of misalignment: the tokenizer and the model configs (`model.config` and `model.generation_config`) may have different special tokens, leading to unexpected behavior in downstream applications.
This PR aligns the special tokens model configs at train time, if they happen to be misaligned with the tokenizer. The alignment is done at the start of training, to ensure proper eval steps and serialization 👼
(From an [issue on Slack](https://huggingface.slack.com/archives/C01N44FJDHT/p1747996479035369) raised by @lewtun ) | {
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https://api.github.com/repos/huggingface/transformers/issues/38439 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38439/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38439/comments | https://api.github.com/repos/huggingface/transformers/issues/38439/events | https://github.com/huggingface/transformers/issues/38439 | 3,097,417,732 | I_kwDOCUB6oc64ntgE | 38,439 | quantizer_hqq should not require a gpu/cuda device to run | {
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} | [] | closed | false | null | [] | null | [] | 2025-05-28T13:31:41 | 2025-07-06T08:02:33 | 2025-07-06T08:02:33 | CONTRIBUTOR | null | null | null | null | `quantizer_hqq.py` requires cuda device:
https://github.com/huggingface/transformers/blob/badc71b9f604ca910bb87a43979c795eaf6e7d64/src/transformers/quantizers/quantizer_hqq.py#L74-L75
However the original HQQ library also runs on CPU, by falling back to default aten operators: https://github.com/mobiusml/hqq?tab=readme-ov-file#usage-with-models | {
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https://api.github.com/repos/huggingface/transformers/issues/38438 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38438/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38438/comments | https://api.github.com/repos/huggingface/transformers/issues/38438/events | https://github.com/huggingface/transformers/pull/38438 | 3,097,411,537 | PR_kwDOCUB6oc6X95sM | 38,438 | Fix MoE gradient test | {
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} | Some MoE models get flaky failures because the gradient checkpointing test tests that all parameters have gradient, which is not true when some experts are not activated. This PR skips those tests correctly for those models. | {
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https://api.github.com/repos/huggingface/transformers/issues/38437 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38437/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38437/comments | https://api.github.com/repos/huggingface/transformers/issues/38437/events | https://github.com/huggingface/transformers/pull/38437 | 3,097,389,988 | PR_kwDOCUB6oc6X90-Y | 38,437 | Continuous batchin: offer only the next token | {
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} | Adds a new `next_token` attribute to the `GenerationOutput` dataclass when streaming.
This enables the following:
```py
[...]
manager: ContinuousBatchingManager = model.init_continuous_batching(
generation_config=generation_config,
streaming=True
)
manager.start()
for result in manager:
output += result.next_token
``` | null | {
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} | #37911 removed a redundant and failing test, but I accidentally added it back in #37590 because they were both open at the same time. This PR removes it again, sorry about that! | {
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] | open | false | null | [] | null | [] | 2025-05-28T12:01:48 | 2025-05-28T15:53:39 | null | NONE | null | null | null | null | Hi!
Let's bring the documentation to all the Romanian-speaking community 🌐
Who would want to translate? Please follow the 🤗 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.
Some notes:
* Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗).
* Please translate in a gender-neutral way.
* Add your translations to the folder called `<languageCode>` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).
* Register your translation in `<languageCode>/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).
* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu for review.
* 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 [forums](https://discuss.huggingface.co/).
## Get Started section
- [ ] [index.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.md) (in progress, [see](https://github.com/zero-point/transformers/tree/add_ro_translation_to_readme))
- [ ] [quicktour.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.md)
- [ ] [installation.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.md).
## Tutorial section
- [ ] [pipeline_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.md)
- [ ] [autoclass_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/autoclass_tutorial.md)
- [ ] [preprocessing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.md)
- [ ] [training.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.md)
- [ ] [accelerate.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.md)
- [ ] [model_sharing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.md)
- [ ] [multilingual.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.md)
<!--
Keep on adding more as you go 🔥
-->
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} | # What does this PR do?
As per title, skips the test unless all sub-models support flex attn. Some models didn't have the flag set even though they can support Flex attn, this PR goes over all recent;y refactored vision models and sets the flags to `True`. Flex attention tests are passing for all models (except Zamba2 fixed in the linked PR below)
We can merge this to make CI green in VLMs for now, and as a long-term solution would be nice to remove these flags. Or at least to check only on the base model, instead of going into each backbone
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https://api.github.com/repos/huggingface/transformers/issues/38433 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38433/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38433/comments | https://api.github.com/repos/huggingface/transformers/issues/38433/events | https://github.com/huggingface/transformers/pull/38433 | 3,096,811,882 | PR_kwDOCUB6oc6X72dg | 38,433 | [`FlexAttn`] Fix models with unique characteristics | {
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} | For context, flex attention cannot work with dimensions less than 16; hence, the config was manipulated to ensure the test works. Before, most models failed, including llama.
There are some models such as idefics 2+3, smolvlm which do not have the `_is_composite` flag and as I do not want to affect other tests - so, I added a new condition to skip the test. They may have passed before but it's not future-proof. For Zamba2, I overwrote the test since some other dims don't add up when changing `hidden_size`.
There are other options:
- Rewrite the test to handle subconfigs --> tried that but there are so many edge cases and weird configs that lead to some issues one way or another.
- Adjust the dimensions in all models and avoid the hidden dim manipulation in the first place. Not sure if this is good as it will strain the tests even more imo :eyes:
Edit: #38434 took care of the composite models. This PR is left to fix some of the more unique models such as zamba2 and deepseek3.
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} | # What does this PR do?
This PR does the following:
- Move `FlashAttentionKwargs` and `ForCausalKwargs` to `generic` folder
- Better handle kwargs check for gradient accumulation ( now we also check that we have the LossKwargs typing, otherwise, we disable the fix)
- Extend forward function with `LossKwargs` typing
- Better tests to see which model still needs to be fixed !
### How to test
`RUN_SLOW=True CUDA_VISIBLE_DEVICES=0 pytest tests/models/ -k "test_model_accepts_loss_kwargs" -s -vvvvv`
Remaining work :
```176 failed, 154 passed, 75 skipped, 95268 deselected, 8 warnings in 84.25s (0:01:24) ```
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} | 1. This PR aims to support the use of the GLM-4-0414 model for training video understanding and image understanding models GLM-4.1V
2. This PR has completed the refactoring of the related modules. Due to the overlap of F definitions (torch and torchvision), image_processors and videos_processors have not been placed under modular management @zucchini-nlp review sugguest.
3. This PR is for code review. @ArthurZucker | {
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} | This PR adds the `require_speech` decorator and adds it to three tests in `seamless_m4t` and `seamless_m4t_v2` that fail with an ImportError if `is_speech_available() == False` | {
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} | # What does this PR do?
The version was correctly fixed before, but the error was not correctly updated | {
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https://api.github.com/repos/huggingface/transformers/issues/38428 | https://api.github.com/repos/huggingface/transformers | https://api.github.com/repos/huggingface/transformers/issues/38428/labels{/name} | https://api.github.com/repos/huggingface/transformers/issues/38428/comments | https://api.github.com/repos/huggingface/transformers/issues/38428/events | https://github.com/huggingface/transformers/issues/38428 | 3,096,566,930 | I_kwDOCUB6oc64kdyS | 38,428 | [Question] The logic of data sampler in data parallel. | {
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} | [] | closed | false | null | [] | null | [] | 2025-05-28T08:49:13 | 2025-07-06T08:02:36 | 2025-07-06T08:02:36 | NONE | null | null | null | null | Hi, thanks for your attention.
When reading the source code of transformers, I cannot understand the implementation of `_get_train_sampler` in `trainer.py`. Why the default data sampler is `RandomSampler` rather than `DistributedSampler`? How does the trainer handle the sampler for data parallel?
reference code: https://github.com/huggingface/transformers/blob/main/src/transformers/trainer.py#L975 | {
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