#
Bamba

##
Overview

Bamba-9B is a decoder-only language model based on the Mamba-2 architecture and is designed to handle a wide range of text generation tasks. It is trained from scratch using a two-stage training approach. In the first stage, the model is trained on 2 trillion tokens from the Dolma v1.7 dataset. In the second stage, it undergoes additional training on 200 billion tokens, leveraging a carefully curated blend of high-quality data to further refine its performance and enhance output quality.

Checkout all Bamba-9B model checkpoints here.

##
BambaConfig

Model | Params | # Layers | Hidden Dim. | Attention Heads | GQA | KV Heads | Context Length | Tied Embeddings
--- | --- | --- | --- | --- | --- | --- | --- | ---
Bamba | 9B (9.78B) | 32 | 4096 | 32 | Yes | 8 | 4096 | True

###
class transformers.BambaConfig

< source >

( vocab_size = 128000 tie_word_embeddings = False hidden_size = 4096 intermediate_size = 14336 num_hidden_layers = 32 num_attention_heads = 32 num_key_value_heads = 8 hidden_act = 'silu' initializer_range = 0.02 rms_norm_eps = 1e-05 use_cache = True num_logits_to_keep = 1 pad_token_id = 0 bos_token_id = 1 eos_token_id = 2 max_position_embeddings = 262144 attention_dropout = 0.0 attn_layer_indices = None mamba_n_heads = 128 mamba_d_head = 'auto' mamba_n_groups = 1 mamba_d_state = 256 mamba_d_conv = 4 mamba_expand = 2 mamba_chunk_size = 256 mamba_conv_bias = True mamba_proj_bias = False **kwargs )

Parameters

- vocab_size (`int`, optional, defaults to 128000) — Vocabulary size of the Bamba model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling BambaModel
- tie_word_embeddings (`bool`, optional, defaults to `False`) — Whether the model’s input and output word embeddings should be tied. Note that this is only relevant if the model has an output word embedding layer.
- hidden_size (`int`, optional, defaults to 4096) — Dimension of the hidden representations.
- intermediate_size (`int`, optional, defaults to 14336) — Dimension of the MLP representations.
- num_hidden_layers (`int`, optional, defaults to 32) — Number of hidden layers in the Transformer encoder.
- num_attention_heads (`int`, optional, defaults to 32) — Number of attention heads for each attention layer in the Transformer encoder.
- num_key_value_heads (`int`, optional, defaults to 8) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to `8`.
- hidden_act (`str` or `function`, optional, defaults to `"silu"`) — The non-linear activation function (function or string) in the decoder.
- initializer_range (`float`, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
- rms_norm_eps (`float`, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers.
- use_cache (`bool`, optional, defaults to `True`) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`.
- num_logits_to_keep (`int` or `None`, optional, defaults to 1) — Number of prompt logits to calculate during generation. If `None`, all logits will be calculated. If an integer value, only last `num_logits_to_keep` logits will be calculated. Default is 1 because only the logits of the last prompt token are needed for generation. For long sequences, the logits for the entire sequence may use a lot of memory so, setting `num_logits_to_keep=1` will reduce memory footprint significantly.
- pad_token_id (`int`, optional, defaults to 0) — The id of the padding token.
- bos_token_id (`int`, optional, defaults to 1) — The id of the “beginning-of-sequence” token.
- eos_token_id (`int`, optional, defaults to 2) — The id of the “end-of-sequence” token.
- max_position_embeddings (`int`, optional, defaults to 262144) — Max cached sequence length for the model
- attention_dropout (`float`, optional, defaults to 0.0) — The dropout ratio for the attention probabilities.
- attn_layer_indices (`list`, optional) — Specifies the layer indices that will have full attention. Must contain values at most num_hidden_layers.
- mamba_n_heads (`int`, optional, defaults to 128) — The number of mamba heads used in the v2 implementation.
- mamba_d_head (`int`, optional, defaults to `"auto"`) — Head embedding dimension size
- mamba_n_groups (`int`, optional, defaults to 1) — The number of the mamba groups used in the v2 implementation.
- mamba_d_state (`int`, optional, defaults to 256) — The dimension the mamba state space latents
- mamba_d_conv (`int`, optional, defaults to 4) — The size of the mamba convolution kernel
- mamba_expand (`int`, optional, defaults to 2) — Expanding factor (relative to hidden_size) used to determine the mamba intermediate size
- mamba_chunk_size (`int`, optional, defaults to 256) — The chunks in which to break the sequence when doing prefill/training
- mamba_conv_bias (`bool`, optional, defaults to `True`) — Flag indicating whether or not to use bias in the convolution layer of the mamba mixer block.
- mamba_proj_bias (`bool`, optional, defaults to `False`) — Flag indicating whether or not to use bias in the input and output projections ([“in_proj”, “out_proj”]) of the mamba mixer block

This is the configuration class to store the configuration of a BambaModel. It is used to instantiate a BambaModel model according to the specified arguments, defining the model architecture. Instantiating a configuration with defaults taken from ibm-fms/Bamba-9.8b-2.2T-hf.

The BambaModel is a hybrid mamba2 architecture with SwiGLU. The checkpoints are jointly trained by IBM, Princeton, and UIUC.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

##
BambaForCausalLM

    from transformers import AutoModelForCausalLM, AutoTokenizer

    model = AutoModelForCausalLM.from_pretrained("ibm-fms/Bamba-9B")
    tokenizer = AutoTokenizer.from_pretrained("ibm-fms/Bamba-9B")

    message = ["Mamba is a snake with following properties  "]
    inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
    response = model.generate(**inputs, max_new_tokens=64)
    print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])

##
Padding-Free Training

Bamba supports padding-free training in which distinct training examples can be concatenated together while nevertheless processing the inputs as though they belonged to separate batches. When the examples are of varying lengths, padding-free training can provide significant speed ups and memory savings compared to batching the examples together and using padding, as the unnecessary compute and memory due to padding is avoided entirely. The performance gains depend on factors such as the model and the data distribution, but throughput gains up to ~2x are commonly seen.

Using padding-free training with Bamba requires the `flash-attn`, `mamba-ssm`, and `causal-conv1d` packages, and the following arguments must be passed to the model in addition to `input_ids` and `labels`:

- `position_ids: torch.LongTensor`: the position index of each token in each sequence.
- `seq_idx: torch.IntTensor`: the index of each sequence in the batch.
- Each of the `FlashAttentionKwargs`
  - `cu_seq_lens_q: torch.LongTensor`: The cumulative sequence lengths of all queries.
  - `cu_seq_lens_k: torch.LongTensor`: The cumulative sequence lengths of all keys.
  - `max_length_q: int`: the longest query length in the batch.
  - `max_length_k: int`: the longest key length in the batch.

The `attention_mask` inputs should not be provided. The DataCollatorWithFlattening can be used to programmatically generate the above set of additional arguments using `return_seq_idx=True` and `return_flash_attn_kwargs=True`. See this blog post for additional information.

###
class transformers.BambaForCausalLM

< source >

( config )

Parameters

- config (BambaForCausalLM) — 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 from_pretrained() method to load the model weights.

The Bamba Model for causal language modeling.

This model inherits from PreTrainedModel. 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 PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

####
forward

< source >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None past_key_values: typing.Optional[transformers.models.bamba.modeling_bamba.HybridMambaAttentionDynamicCache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_attentions: typing.Optional[bool] = None output_hidden_states: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 **kwargs ) →
transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

Parameters

- input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

  Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

  What are input IDs?

- attention_mask (`torch.Tensor` 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?

- position_ids (`torch.LongTensor` 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?

- past_key_values (`~models.bamba.modeling_bamba.HybridMambaAttentionDynamicCache`, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

  Two formats are allowed:

  - a Cache instance, see our kv cache guide;
  - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format.

  The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned.

  If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don’t have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`.

- inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, optional) — Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model’s internal embedding lookup matrix.
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, optional) — Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
- use_cache (`bool`, optional) — If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`).
- 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.
- cache_position (`torch.LongTensor` of shape `(sequence_length)`, optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.
- logits_to_keep (`Union[int, torch.Tensor]`, defaults to `0`) — If an `int`, compute logits for the last `logits_to_keep` tokens. If `0`, calculate logits for all `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that token can save memory, which becomes pretty significant for long sequences or large vocabulary size. If a `torch.Tensor`, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns

transformers.modeling_outputs.CausalLMOutputWithPast or `tuple(torch.FloatTensor)`

A transformers.modeling_outputs.CausalLMOutputWithPast or a tuple of `torch.FloatTensor` (if `return_dict=False` is passed or when `config.return_dict=False`) comprising various elements depending on the configuration (BambaConfig) and inputs.

- loss (`torch.FloatTensor` of shape `(1,)`, optional, returned when `labels` is provided) — Language modeling loss (for next-token prediction).

- logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

- past_key_values (`Cache`, optional, returned when `use_cache=True` is passed or when `config.use_cache=True`) — It is a Cache instance. For more details, see our kv cache guide.

  Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.

- hidden_states (`tuple(torch.FloatTensor)`, optional, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`) — Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

  Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

- attentions (`tuple(torch.FloatTensor)`, optional, returned when `output_attentions=True` is passed or when `config.output_attentions=True`) — Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`.

  Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The BambaForCausalLM forward method, overrides the `__call__` special method.

Although the recipe for forward pass needs to be defined within this function, one should call the `Module` instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Example:

    >>> from transformers import AutoTokenizer, BambaForCausalLM

    >>> model = BambaForCausalLM.from_pretrained("...")
    >>> tokenizer = AutoTokenizer.from_pretrained("...")

    >>> prompt = "Hey, are you conscious? Can you talk to me?"
    >>> inputs = tokenizer(prompt, return_tensors="pt")

    >>> # Generate
    >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
    >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
    "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."

This HF implementation is contributed by ani300 and fabianlim.

< > Update on GitHub