sgiwdZddlmZddlmZmZddlmZddl Z ddl m Z ddl Z ddlmZmZmZddlmZmZddlmZddlmZmZdd l mZd d lmZmZd d lmZmZm Z d d l!m"Z"m#Z#m$Z$ddl%m&Z&e$jNe(Z)dZ*dZ+dZ,dZ-dZ.dZ/dZ0dZ1GddejdZ3GddejdZ4GddejdZ5GddejdZ6Gd d!ejdZ7Gd"d#eZ8Gd$d%ejdZ9Gd&d'ejdZ:e"d(e-Gd)d*e8Z;e e;e+ee*e,+Gd,d-ejdZ<e"d.e-Gd/d0e8Z=e e=e+ee*e,+y)1zFlax LLaMA model.)partial)OptionalTupleN) FrozenDictfreezeunfreeze) combine_masksmake_causal_mask)dot_product_attention_weights) flatten_dictunflatten_dict)lax)FlaxBaseModelOutputFlaxCausalLMOutput)ACT2FNFlaxPreTrainedModelappend_call_sample_docstring)add_start_docstrings%add_start_docstrings_to_model_forwardlogging) LlamaConfigrzafmck/testing-llama-tinyz openlm-research/open_llama_3b_v2a This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`LlamaConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16`, or `jax.numpy.bfloat16`. This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. a Args: input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cddtjd|d|z zz }tjdtj||jd}tj||fd}tjtj |dddddftj |dddddffd}tj|ddddd|fS) Ng?i'rz i , j -> i jfloat32axis) nparangeeinsumastype concatenatesincosjnparray)num_posdiminv_freqfreqsembouts `/var/www/html/venv/lib/python3.12/site-packages/transformers/models/llama/modeling_flax_llama.pycreate_sinusoidal_positionsr0se !S! 4s :;