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3f20877da9
* Add jamba arch * apply "make fix-copies" changes * fix link to model in JambaConfig docstring * Add n_ctx in modeling file because repo-consistency wants that * Add jamba to flash attention and sdpa documentation * mamba dt_proj quant fix now works for LoRA as well * override test_left_padding_compatibility and use a more permissive tolerance. left padding numerical difference are accentuated by mamba layers * add jamba to tokenization auto * fix comments of shape (PR #24 in the model page: https://huggingface.co/ai21labs/Jamba-v0.1/discussions/24) * simple PR fixes * remove unnecessary kwargs from JambaAttentionDecoderLayer and JambaMambaDecoderLayer * remove the LoRA hack for the mamba dt_proj bias. It was solved in huggingface/peft#1530 (https://github.com/huggingface/peft/pull/1530) * Add copied comment on JambaMLP (it's the same as MixtralMLP) * remove padding_mask warnings. It's not supported anymore * fix docstring. Float instead of int * A few more minor PR fixes * (1) lowercase names for mamba layernorms (2) remove _apply_inner_layernorms and do it directly in the forward pass * Return None attention weights from mamba layers. Append to all attentions only if not None. * remove some leftover jamba archive lists * Better separation between expert vs non-expert layers. non-expert layers return None as router_logits, and it is not concatenated to all_router_logits returned from JambaModel * no need to take router_logits at config.expert_layer_offset anymore. result.router_logits now holds results only for expert layers * Add Jamba paper on READMEs * (1) rename n_ctx -> max_position_embeddings (2) don't use it in the modeling file since it's not needed (set it as an exception to check_config_attributes) * Add copied from comment * remove the code path for apply_inner_layernorms=False. Jamba always has the inner mamba layernorms * clearer docstring for _convert_to_standard_cache * style fixes * Change calc_logits_for_entire_prompt (bool) to num_logits_to_keep (int). Adapt assisted decoding code tp use it. Also small change in low memory beam search decoding path to support this new int value in model_inputs * rename test so it still overrides what its meant to override * draft * oups * nit * remove more complexe logic * fix names used in config * fix fix fix * style * fix some more failing tests * generate did not init the cache 🙃 * more small nits * typo * config.mamba_expand * config.hidden_size for the intermediate size of the mamba shapes * fix init of pkv with torch.tensor() * empty tensor * fix some init issues * stupid changes required by generate because it does not even support it's own DynamicCache class * more fixes * fix general assisted gen cache_position bug * tests passing * Add offsets and periods as SPECIAL_CASES_TO_ALLOW in check_config_attributes.py * fix reorder_cache to reorder mamba states and override some more functions in HybridMambaAttentionDynamicCache * no need to override test_past_key_values_format() and _check_past_key_values_for_generate() in tests anymore * fix docstrings and typehints for past_key_values * style fixes * fix docs * change typehint due to copy from Mixtral * forgot import * import order * Add configuration_jamba and modeling_jamba to not_doctested because the model is too big to download (in docstring of JambaForCausalLM.forward) * Add integration test with tiny tandom Jamba model on hub * fix flash attention cache shapes * bring back forgotten hidden states * rename HybridMambaAttentionDynamicCache.seqlen_offset to has_previous_state (and make bool) and bugfix - it should be set to True after a finished forward pass of the entire model * align integration test after modeling fixes * bugfix - mamba can use precomputed states only of forward pass is on a single token * bugfix - mamba can use precomputed states only if they match the batch size * typo * remove making _prepare_4d_causal_attention_mask a leaf function * stop using past_seq_len.get_seq_length(). Use cache positions instead. Adjust test (test_decoder_model_past_with_large_inputs) accordingly --------- Co-authored-by: Arthur Zucker <arthur.zucker@gmail.com> Co-authored-by: Joao Gante <joao@huggingface.co> |
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| .. | ||
| internal | ||
| main_classes | ||
| model_doc | ||
| tasks | ||
| _config.py | ||
| _redirects.yml | ||
| _toctree.yml | ||
| accelerate.md | ||
| add_new_model.md | ||
| add_new_pipeline.md | ||
| add_tensorflow_model.md | ||
| attention.md | ||
| autoclass_tutorial.md | ||
| benchmarks.md | ||
| bertology.md | ||
| big_models.md | ||
| chat_templating.md | ||
| community.md | ||
| contributing.md | ||
| create_a_model.md | ||
| custom_models.md | ||
| custom_tools.md | ||
| debugging.md | ||
| deepspeed.md | ||
| fast_tokenizers.md | ||
| fsdp.md | ||
| generation_strategies.md | ||
| glossary.md | ||
| hf_quantizer.md | ||
| hpo_train.md | ||
| index.md | ||
| installation.md | ||
| llm_tutorial_optimization.md | ||
| llm_tutorial.md | ||
| model_memory_anatomy.md | ||
| model_sharing.md | ||
| model_summary.md | ||
| multilingual.md | ||
| notebooks.md | ||
| pad_truncation.md | ||
| peft.md | ||
| perf_hardware.md | ||
| perf_infer_cpu.md | ||
| perf_infer_gpu_one.md | ||
| perf_torch_compile.md | ||
| perf_train_cpu_many.md | ||
| perf_train_cpu.md | ||
| perf_train_gpu_many.md | ||
| perf_train_gpu_one.md | ||
| perf_train_special.md | ||
| perf_train_tpu_tf.md | ||
| performance.md | ||
| perplexity.md | ||
| philosophy.md | ||
| pipeline_tutorial.md | ||
| pipeline_webserver.md | ||
| pr_checks.md | ||
| preprocessing.md | ||
| quantization.md | ||
| quicktour.md | ||
| run_scripts.md | ||
| sagemaker.md | ||
| serialization.md | ||
| task_summary.md | ||
| tasks_explained.md | ||
| testing.md | ||
| tf_xla.md | ||
| tflite.md | ||
| tokenizer_summary.md | ||
| torchscript.md | ||
| trainer.md | ||
| training.md | ||
| transformers_agents.md | ||
| troubleshooting.md | ||