llama3

Llama3 standalone implementation inspired from https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/07_gpt_to_llama/standalone-llama32.ipynb Explanations on grouped query attention: https://www.ibm.com/think/topics/grouped-query-attention.

class mfai.pytorch.models.llms.llama3.GroupedQueryAttention(d_in, d_out, num_heads, num_kv_groups)

Bases: Module

Parameters:

• d_in (int)

• d_out (int)

• num_heads (int)

• num_kv_groups (int)

forward(x, mask, cos, sin)

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

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 registered hooks while the latter silently ignores them.

Parameters:

• x (Tensor)

• mask (Tensor)

• cos (Tensor)

• sin (Tensor)

Return type:

Tensor

class mfai.pytorch.models.llms.llama3.Llama3(settings, vocab_size=32000)

Bases: Module

Parameters:

• settings (Llama3Settings)

• vocab_size (int)

embed_tokens(tok_ids)

Return type:

Tensor

Parameters:

tok_ids (Tensor)

forward(tok_ids)

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

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 registered hooks while the latter silently ignores them.

Parameters:

tok_ids (Tensor)

Return type:

Tensor

forward_vectors(embeddings, first_embedding=None)

Process a batch of embeddings through the model. If first_embedding is supplied the first tokens of each blocks are replaced by the corresponding embeddings. Useful for multimodal models with injection of vision data embeddings at each stage.

Return type:

Tensor

Parameters:

• embeddings (Tensor)

• first_embedding (Tensor | None)

model_type = 4

settings_kls

alias of Llama3Settings

class mfai.pytorch.models.llms.llama3.Llama3Settings(emb_dim=256, context_length=512, n_heads=8, n_layers=8, hidden_dim=768, num_kv_groups=2, rope_base=500000.0)

Bases: object

Parameters:

• emb_dim (int)

• context_length (int)

• n_heads (int)

• n_layers (int)

• hidden_dim (int)

• num_kv_groups (int)

• rope_base (float)

context_length: int

emb_dim: int

classmethod from_dict(kvs, *, infer_missing=False)

Return type:

TypeVar(A, bound= DataClassJsonMixin)

Parameters:

kvs (dict | list | str | int | float | bool | None)

classmethod from_json(s, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw)

Return type:

TypeVar(A, bound= DataClassJsonMixin)

Parameters:

s (str | bytes | bytearray)

hidden_dim: int

n_heads: int

n_layers: int

num_kv_groups: int

rope_base: float

classmethod schema(*, infer_missing=False, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)

Return type:

SchemaF[TypeVar(A, bound= DataClassJsonMixin)]

Parameters:

• infer_missing (bool)

• many (bool)

• partial (bool)

to_dict(encode_json=False)

Return type:

Dict[str, Union[dict, list, str, int, float, bool, None]]

to_json(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, sort_keys=False, **kw)

Return type:

str

Parameters:

• skipkeys (bool)

• ensure_ascii (bool)

• check_circular (bool)

• allow_nan (bool)

• indent (int | str | None)

• separators (Tuple[str, str] | None)

• default (Callable | None)

• sort_keys (bool)

class mfai.pytorch.models.llms.llama3.TransformerBlock(emb_dim, hidden_dim, num_heads, num_kv_groups, dtype=None)

Bases: Module

Parameters:

• emb_dim (int)

• hidden_dim (int)

• num_heads (int)

• num_kv_groups (int)

• dtype (dtype | None)

forward(x, mask, cos, sin)

Define the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

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 registered hooks while the latter silently ignores them.

Parameters:

• x (Tensor)

• mask (Tensor)

• cos (Tensor)

• sin (Tensor)

Return type:

Tensor

mfai.pytorch.models.llms.llama3.apply_rope(x, cos, sin)

Return type:

Tensor

Parameters:

• x (Tensor)

• cos (Tensor)

• sin (Tensor)

mfai.pytorch.models.llms.llama3.compute_rope_params(head_dim, theta_base=10000.0, context_length=4096)

Return type:

tuple[Tensor, Tensor]

Parameters:

• head_dim (int)

• theta_base (float)

• context_length (int)