gemma

mindnlp.transformers.models.gemma.configuration_gemma

Gemma model configuration

mindnlp.transformers.models.gemma.configuration_gemma.GemmaConfig

Bases: PretrainedConfig

This is the configuration class to store the configuration of a [GemmaModel]. It is used to instantiate an Gemma model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Gemma-7B. e.g. google/gemma-7b Configuration objects inherit from [PretrainedConfig] and can be used to control the model outputs. Read the documentation from [PretrainedConfig] for more information. Args: vocab_size (int, optional, defaults to 256000): Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [GemmaModel] hidden_size (int, optional, defaults to 3072): Dimension of the hidden representations. intermediate_size (int, optional, defaults to 24576): Dimension of the MLP representations. num_hidden_layers (int, optional, defaults to 28): Number of hidden layers in the Transformer decoder. num_attention_heads (int, optional, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. num_key_value_heads (int, optional, defaults to 16): 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 checkout this paper. If it is not specified, will default to num_attention_heads. head_dim (int, optional, defaults to 256): The attention head dimension. hidden_act (str or function, optional, defaults to "gelu_pytorch_tanh"): The legacy activation function. It is overwritten by the hidden_activation. hidden_activation (str or function, optional): The non-linear activation function (function or string) in the decoder. Will default to "gelu_pytorch_tanh" if not specified. "gelu_pytorch_tanh" uses an approximation of the "gelu" activation function. max_position_embeddings (int, optional, defaults to 8192): The maximum sequence length that this model might ever be used with. 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-06): 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. pad_token_id (int, optional, defaults to 0): Padding token id. eos_token_id (int, optional, defaults to 1): End of stream token id. bos_token_id (int, optional, defaults to 2): Beginning of stream token id. tie_word_embeddings (bool, optional, defaults to True): Whether to tie weight embeddings rope_theta (float, optional, defaults to 10000.0): The base period of the RoPE embeddings. attention_bias (bool, defaults to False, optional, defaults to False): Whether to use a bias in the query, key, value and output projection layers during self-attention. attention_dropout (float, optional, defaults to 0.0): The dropout ratio for the attention probabilities.

>>> from transformers import GemmaModel, GemmaConfig
>>> # Initializing a Gemma gemma-7b style configuration
>>> configuration = GemmaConfig()
>>> # Initializing a model from the gemma-7b style configuration
>>> model = GemmaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config

Source code in mindnlp\transformers\models\gemma\configuration_gemma.py

class GemmaConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
    defaults will yield a similar configuration to that of the Gemma-7B.
    e.g. [google/gemma-7b](https://huggingface.co/google/gemma-7b)
    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.
    Args:
        vocab_size (`int`, *optional*, defaults to 256000):
            Vocabulary size of the Gemma model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`GemmaModel`]
        hidden_size (`int`, *optional*, defaults to 3072):
            Dimension of the hidden representations.
        intermediate_size (`int`, *optional*, defaults to 24576):
            Dimension of the MLP representations.
        num_hidden_layers (`int`, *optional*, defaults to 28):
            Number of hidden layers in the Transformer decoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer decoder.
        num_key_value_heads (`int`, *optional*, defaults to 16):
            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 checkout [this
            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
            `num_attention_heads`.
        head_dim (`int`, *optional*, defaults to 256):
            The attention head dimension.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
            The legacy activation function. It is overwritten by the `hidden_activation`.
        hidden_activation (`str` or `function`, *optional*):
            The non-linear activation function (function or string) in the decoder. Will default to `"gelu_pytorch_tanh"`
            if not specified. `"gelu_pytorch_tanh"` uses an approximation of the `"gelu"` activation function.
        max_position_embeddings (`int`, *optional*, defaults to 8192):
            The maximum sequence length that this model might ever be used with.
        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-06):
            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`.
        pad_token_id (`int`, *optional*, defaults to 0):
            Padding token id.
        eos_token_id (`int`, *optional*, defaults to 1):
            End of stream token id.
        bos_token_id (`int`, *optional*, defaults to 2):
            Beginning of stream token id.
        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
            Whether to tie weight embeddings
        rope_theta (`float`, *optional*, defaults to 10000.0):
            The base period of the RoPE embeddings.
        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
            Whether to use a bias in the query, key, value and output projection layers during self-attention.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
    ```python
    >>> from transformers import GemmaModel, GemmaConfig
    >>> # Initializing a Gemma gemma-7b style configuration
    >>> configuration = GemmaConfig()
    >>> # Initializing a model from the gemma-7b style configuration
    >>> model = GemmaModel(configuration)
    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    model_type = "gemma"
    keys_to_ignore_at_inference = ["past_key_values"]

    def __init__(
        self,
        vocab_size=256000,
        hidden_size=3072,
        intermediate_size=24576,
        num_hidden_layers=28,
        num_attention_heads=16,
        num_key_value_heads=16,
        head_dim=256,
        hidden_act="gelu_pytorch_tanh",
        hidden_activation=None,
        max_position_embeddings=8192,
        initializer_range=0.02,
        rms_norm_eps=1e-6,
        use_cache=True,
        pad_token_id=0,
        eos_token_id=1,
        bos_token_id=2,
        tie_word_embeddings=True,
        rope_theta=10000.0,
        attention_bias=False,
        attention_dropout=0.0,
        sliding_window = 4096,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.head_dim = head_dim
        self.num_key_value_heads = num_key_value_heads
        self.hidden_act = hidden_act
        self.hidden_activation = hidden_activation
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.attention_bias = attention_bias
        self.attention_dropout = attention_dropout
        self.sliding_window = sliding_window
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            tie_word_embeddings=tie_word_embeddings,
            **kwargs,
        )

mindnlp.transformers.models.gemma.modeling_gemma

MindSpore Gemma

mindnlp.transformers.models.gemma.modeling_gemma.GemmaAttention

Bases: Module

Multi-headed attention from 'Attention Is All You Need' paper

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: GemmaConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.attention_dropout = config.attention_dropout
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = config.head_dim
        self.num_key_value_heads = config.num_key_value_heads
        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True
        self.scaling = 1 / math.sqrt(config.head_dim)

        if self.hidden_size % self.num_heads != 0:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )

        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
        self.rotary_emb = GemmaRotaryEmbedding(
            self.head_dim,
            max_position_embeddings=self.max_position_embeddings,
            base=self.rope_theta,
        )

    def forward(
        self,
        hidden_states: mindspore.Tensor,
        attention_mask: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[mindspore.Tensor] = None,
    ) -> Tuple[mindspore.Tensor, Optional[mindspore.Tensor], Optional[Tuple[mindspore.Tensor]]]:
        bsz, q_len, _ = hidden_states.shape

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = ops.transpose(query_states.view(bsz, q_len, self.num_heads, self.head_dim), 1, 2)
        key_states = ops.transpose(key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim), 1, 2)
        value_states = ops.transpose(value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim), 1, 2)

        cos, sin = self.rotary_emb(value_states, position_ids)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, self.num_key_value_groups)
        value_states = repeat_kv(value_states, self.num_key_value_groups)

        attn_weights = ops.matmul(query_states, ops.transpose(key_states, 2, 3)) * self.scaling

        if attention_mask is not None:  # no matter the length, we just slice it
            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
            attn_weights = attn_weights + causal_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=mindspore.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        attn_output = ops.matmul(attn_weights, value_states)

        if attn_output.shape != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.shape}"
            )

        attn_output = ops.transpose(attn_output, 1, 2)

        attn_output = attn_output.view(bsz, q_len, -1)
        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

mindnlp.transformers.models.gemma.modeling_gemma.GemmaDecoderLayer

Bases: Module

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaDecoderLayer(nn.Module):
    def __init__(self, config: GemmaConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = GEMMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)

        self.mlp = GemmaMLP(config)
        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: mindspore.Tensor,
        attention_mask: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[mindspore.Tensor] = None,
        **kwargs,
    ) -> Tuple[mindspore.Tensor, Optional[Tuple[mindspore.Tensor, mindspore.Tensor]]]:
        """
        Args:
            hidden_states (`mindspore.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`mindspore.Tensor`, *optional*):
                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
                query_sequence_length, key_sequence_length)` if default attention is used.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            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`).
            past_key_value (`Tuple(mindspore.Tensor)`, *optional*): cached past key and value projection states
            cache_position (`mindspore.Tensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence
            kwargs (`dict`, *optional*):
                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
                into the model
        """
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        return outputs

mindnlp.transformers.models.gemma.modeling_gemma.GemmaDecoderLayer.forward(hidden_states, attention_mask=None, position_ids=None, past_key_value=None, output_attentions=False, use_cache=False, cache_position=None, **kwargs)

PARAMETER	DESCRIPTION
hidden_states	input to the layer of shape (batch, seq_len, embed_dim) TYPE: `mindspore.Tensor`
attention_mask	attention mask of size (batch_size, sequence_length) if flash attention is used or (batch_size, 1, query_sequence_length, key_sequence_length) if default attention is used. TYPE: `mindspore.Tensor`, *optional* DEFAULT: None
output_attentions	Whether or not to return the attentions tensors of all attention layers. See attentions under returned tensors for more detail. TYPE: `bool`, *optional* DEFAULT: False
use_cache	If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values). TYPE: `bool`, *optional* DEFAULT: False
past_key_value	cached past key and value projection states TYPE: `Tuple(mindspore.Tensor)`, *optional* DEFAULT: None
cache_position	Indices depicting the position of the input sequence tokens in the sequence TYPE: `mindspore.Tensor` of shape `(sequence_length)`, *optional* DEFAULT: None
kwargs	Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code into the model TYPE: `dict`, *optional* DEFAULT: {}

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

def forward(
    self,
    hidden_states: mindspore.Tensor,
    attention_mask: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    past_key_value: Optional[Cache] = None,
    output_attentions: Optional[bool] = False,
    use_cache: Optional[bool] = False,
    cache_position: Optional[mindspore.Tensor] = None,
    **kwargs,
) -> Tuple[mindspore.Tensor, Optional[Tuple[mindspore.Tensor, mindspore.Tensor]]]:
    """
    Args:
        hidden_states (`mindspore.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
        attention_mask (`mindspore.Tensor`, *optional*):
            attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
            query_sequence_length, key_sequence_length)` if default attention is used.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under
            returned tensors for more detail.
        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`).
        past_key_value (`Tuple(mindspore.Tensor)`, *optional*): cached past key and value projection states
        cache_position (`mindspore.Tensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence
        kwargs (`dict`, *optional*):
            Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
            into the model
    """
    residual = hidden_states

    hidden_states = self.input_layernorm(hidden_states)

    # Self Attention
    hidden_states, self_attn_weights, present_key_value = self.self_attn(
        hidden_states=hidden_states,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_value=past_key_value,
        output_attentions=output_attentions,
        use_cache=use_cache,
        cache_position=cache_position,
        **kwargs,
    )
    hidden_states = residual + hidden_states

    # Fully Connected
    residual = hidden_states
    hidden_states = self.post_attention_layernorm(hidden_states)
    hidden_states = self.mlp(hidden_states)
    hidden_states = residual + hidden_states

    outputs = (hidden_states,)

    if output_attentions:
        outputs += (self_attn_weights,)

    if use_cache:
        outputs += (present_key_value,)

    return outputs

mindnlp.transformers.models.gemma.modeling_gemma.GemmaDynamicNTKScalingRotaryEmbedding

Bases: GemmaRotaryEmbedding

GemmaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaDynamicNTKScalingRotaryEmbedding(GemmaRotaryEmbedding):
    """GemmaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""

    def forward(self, x, position_ids):
        # difference to the original RoPE: inv_freq is recomputed when the sequence length > original length
        seq_len = ops.max(position_ids) + 1
        if seq_len > self.max_position_embeddings:
            base = self.base * (
                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
            ) ** (self.dim / (self.dim - 2))
            inv_freq = 1.0 / (
                base ** (ops.arange(0, self.dim, 2, dtype=mindspore.int64).float() / self.dim)
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: this may break with compilation

        cos, sin = super().forward(x, position_ids)
        return cos, sin

mindnlp.transformers.models.gemma.modeling_gemma.GemmaForCausalLM

Bases: GemmaPreTrainedModel

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaForCausalLM(GemmaPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = GemmaModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[Union[Cache, List[mindspore.Tensor]]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[mindspore.Tensor] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            labels (`mindspore.Tensor` 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]`.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, GemmaForCausalLM

        >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
        >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")

        >>> prompt = "What is your favorite condiment?"
        >>> inputs = tokenizer(prompt, return_tensors="ms")

        >>> # 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]
        "What is your favorite condiment?"
        ```"""
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            cache_position=cache_position,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()
        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :]
            shift_labels = labels[..., 1:]
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        cache_position=None,
        position_ids=None,
        use_cache=True,
        **kwargs,
    ):
        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
        # Exception 1: when passing input_embeds, input_ids may be missing entries
        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
        if past_key_values is not None:
            if inputs_embeds is not None:  # Exception 1
                if 0 not in input_ids.shape:
                    input_ids = input_ids[:, -cache_position.shape[0] :]
            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
                input_ids = input_ids[:, cache_position]

        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.int().cumsum(-1) - 1
            position_ids = position_ids.masked_fill(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and cache_position[0] == 0:
            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
        else:
            # The clone here is for the same reason as for `position_ids`.
            model_inputs = {"input_ids": input_ids, "inputs_embeds": None}

        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
            if model_inputs["inputs_embeds"] is not None:
                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
            else:
                batch_size, sequence_length = model_inputs["input_ids"].shape

            dtype = self.lm_head.weight.dtype
            min_dtype = float(ops.finfo(dtype).min)

            attention_mask = _prepare_4d_causal_attention_mask_with_cache_position(
                attention_mask,
                sequence_length=sequence_length,
                target_length=past_key_values.get_max_length(),
                dtype=dtype,
                min_dtype=min_dtype,
                cache_position=cache_position,
                batch_size=batch_size,
            )

        model_inputs.update(
            {
                "position_ids": position_ids,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": use_cache,
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

mindnlp.transformers.models.gemma.modeling_gemma.GemmaForCausalLM.forward(input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, cache_position=None)

PARAMETER	DESCRIPTION
labels	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]. TYPE: `mindspore.Tensor` of shape `(batch_size, sequence_length)`, *optional* DEFAULT: None

Example:

>>> from transformers import AutoTokenizer, GemmaForCausalLM

>>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
>>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")

>>> prompt = "What is your favorite condiment?"
>>> inputs = tokenizer(prompt, return_tensors="ms")

>>> # 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]
"What is your favorite condiment?"

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

def forward(
    self,
    input_ids: mindspore.Tensor = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[Union[Cache, List[mindspore.Tensor]]] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    cache_position: Optional[mindspore.Tensor] = None,
) -> Union[Tuple, CausalLMOutputWithPast]:
    r"""
    Args:
        labels (`mindspore.Tensor` 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]`.

    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, GemmaForCausalLM

    >>> model = GemmaForCausalLM.from_pretrained("google/gemma-7b")
    >>> tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")

    >>> prompt = "What is your favorite condiment?"
    >>> inputs = tokenizer(prompt, return_tensors="ms")

    >>> # 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]
    "What is your favorite condiment?"
    ```"""
    output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
    output_hidden_states = (
        output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
    )
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
    outputs = self.model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_values=past_key_values,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
        cache_position=cache_position,
    )

    hidden_states = outputs[0]
    logits = self.lm_head(hidden_states)
    logits = logits.float()
    loss = None
    if labels is not None:
        # Shift so that tokens < n predict n
        shift_logits = logits[..., :-1, :]
        shift_labels = labels[..., 1:]
        # Flatten the tokens
        loss_fct = CrossEntropyLoss()
        shift_logits = shift_logits.view(-1, self.config.vocab_size)
        shift_labels = shift_labels.view(-1)
        # Enable model parallelism
        loss = loss_fct(shift_logits, shift_labels)

    if not return_dict:
        output = (logits,) + outputs[1:]
        return (loss,) + output if loss is not None else output

    return CausalLMOutputWithPast(
        loss=loss,
        logits=logits,
        past_key_values=outputs.past_key_values,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.gemma.modeling_gemma.GemmaForSequenceClassification

Bases: GemmaPreTrainedModel

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaForSequenceClassification(GemmaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = GemmaModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[Union[Cache, List[mindspore.Tensor]]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
        r"""
        labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = transformer_outputs[0]
        logits = self.score(hidden_states)

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        else:
            batch_size = inputs_embeds.shape[0]

        if self.config.pad_token_id is None and batch_size != 1:
            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
        if self.config.pad_token_id is None:
            sequence_lengths = -1
        else:
            if input_ids is not None:
                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
                sequence_lengths = ops.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
            else:
                sequence_lengths = -1

        pooled_logits = logits[ops.arange(batch_size), sequence_lengths]

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and labels.dtype in (mindspore.int64, mindspore.int32):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(pooled_logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(pooled_logits, labels)
        if not return_dict:
            output = (pooled_logits,) + transformer_outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
        )

mindnlp.transformers.models.gemma.modeling_gemma.GemmaForSequenceClassification.forward(input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

labels (mindspore.Tensor of shape (batch_size,), optional): Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

def forward(
    self,
    input_ids: mindspore.Tensor = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[Union[Cache, List[mindspore.Tensor]]] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
    r"""
    labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
        config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
        `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    transformer_outputs = self.model(
        input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_values=past_key_values,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    hidden_states = transformer_outputs[0]
    logits = self.score(hidden_states)

    if input_ids is not None:
        batch_size = input_ids.shape[0]
    else:
        batch_size = inputs_embeds.shape[0]

    if self.config.pad_token_id is None and batch_size != 1:
        raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
    if self.config.pad_token_id is None:
        sequence_lengths = -1
    else:
        if input_ids is not None:
            # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
            sequence_lengths = ops.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
            sequence_lengths = sequence_lengths % input_ids.shape[-1]
        else:
            sequence_lengths = -1

    pooled_logits = logits[ops.arange(batch_size), sequence_lengths]

    loss = None
    if labels is not None:
        if self.config.problem_type is None:
            if self.num_labels == 1:
                self.config.problem_type = "regression"
            elif self.num_labels > 1 and labels.dtype in (mindspore.int64, mindspore.int32):
                self.config.problem_type = "single_label_classification"
            else:
                self.config.problem_type = "multi_label_classification"

        if self.config.problem_type == "regression":
            loss_fct = MSELoss()
            if self.num_labels == 1:
                loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
            else:
                loss = loss_fct(pooled_logits, labels)
        elif self.config.problem_type == "single_label_classification":
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
        elif self.config.problem_type == "multi_label_classification":
            loss_fct = BCEWithLogitsLoss()
            loss = loss_fct(pooled_logits, labels)
    if not return_dict:
        output = (pooled_logits,) + transformer_outputs[1:]
        return ((loss,) + output) if loss is not None else output

    return SequenceClassifierOutputWithPast(
        loss=loss,
        logits=pooled_logits,
        past_key_values=transformer_outputs.past_key_values,
        hidden_states=transformer_outputs.hidden_states,
        attentions=transformer_outputs.attentions,
    )

mindnlp.transformers.models.gemma.modeling_gemma.GemmaForTokenClassification

Bases: GemmaPreTrainedModel

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaForTokenClassification(GemmaPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = GemmaModel(config)
        if getattr(config, "classifier_dropout", None) is not None:
            classifier_dropout = config.classifier_dropout
        elif getattr(config, "hidden_dropout", None) is not None:
            classifier_dropout = config.hidden_dropout
        else:
            classifier_dropout = 0.1
        self.dropout = nn.Dropout(classifier_dropout)
        self.score = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def forward(
        self,
        input_ids: Optional[mindspore.Tensor] = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[List[mindspore.Tensor]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        labels: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, TokenClassifierOutput]:
        r"""
        labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.score(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

mindnlp.transformers.models.gemma.modeling_gemma.GemmaForTokenClassification.forward(input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, inputs_embeds=None, labels=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None)

labels (mindspore.Tensor of shape (batch_size,), optional): Labels for computing the sequence classification/regression loss. Indices should be in [0, ..., config.num_labels - 1]. If config.num_labels == 1 a regression loss is computed (Mean-Square loss), If config.num_labels > 1 a classification loss is computed (Cross-Entropy).

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

def forward(
    self,
    input_ids: Optional[mindspore.Tensor] = None,
    attention_mask: Optional[mindspore.Tensor] = None,
    position_ids: Optional[mindspore.Tensor] = None,
    past_key_values: Optional[List[mindspore.Tensor]] = None,
    inputs_embeds: Optional[mindspore.Tensor] = None,
    labels: Optional[mindspore.Tensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
) -> Union[Tuple, TokenClassifierOutput]:
    r"""
    labels (`mindspore.Tensor` of shape `(batch_size,)`, *optional*):
        Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
        config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
        `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
    """
    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    outputs = self.model(
        input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        past_key_values=past_key_values,
        inputs_embeds=inputs_embeds,
        use_cache=use_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict,
    )
    sequence_output = outputs[0]
    sequence_output = self.dropout(sequence_output)
    logits = self.score(sequence_output)

    loss = None
    if labels is not None:
        loss_fct = CrossEntropyLoss()
        loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

    if not return_dict:
        output = (logits,) + outputs[2:]
        return ((loss,) + output) if loss is not None else output

    return TokenClassifierOutput(
        loss=loss,
        logits=logits,
        hidden_states=outputs.hidden_states,
        attentions=outputs.attentions,
    )

mindnlp.transformers.models.gemma.modeling_gemma.GemmaLinearScalingRotaryEmbedding

Bases: GemmaRotaryEmbedding

GemmaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaLinearScalingRotaryEmbedding(GemmaRotaryEmbedding):
    """GemmaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    def forward(self, x, position_ids):
        # difference to the original RoPE: a scaling factor is aplied to the position ids
        position_ids = position_ids.float() / self.scaling_factor
        cos, sin = super().forward(x, position_ids)
        return cos, sin

mindnlp.transformers.models.gemma.modeling_gemma.GemmaModel

Bases: GemmaPreTrainedModel

Transformer decoder consisting of config.num_hidden_layers layers. Each layer is a [GemmaDecoderLayer]

PARAMETER	DESCRIPTION
config	GemmaConfig TYPE: GemmaConfig

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

class GemmaModel(GemmaPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmaDecoderLayer`]

    Args:
        config: GemmaConfig
    """

    def __init__(self, config: GemmaConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [GemmaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    def forward(
        self,
        input_ids: mindspore.Tensor = None,
        attention_mask: Optional[mindspore.Tensor] = None,
        position_ids: Optional[mindspore.Tensor] = None,
        past_key_values: Optional[Union[Cache, List[mindspore.Tensor]]] = None,
        inputs_embeds: Optional[mindspore.Tensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[mindspore.Tensor] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
            )
            use_cache = False

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        return_legacy_cache = False  # noqa: F841
        if (
            use_cache and not isinstance(past_key_values, Cache) and not self.training
        ):  # kept for BC (non `Cache` `past_key_values` inputs)
            return_legacy_cache = True  # noqa: F841
            past_key_values = DynamicCache.from_legacy_cache(past_key_values)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = ops.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1]
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        # embed positions
        hidden_states = inputs_embeds

        # normalized
        # Gemma downcasts the below to float16, causing sqrt(3072)=55.4256 to become 55.5
        # See https://github.com/huggingface/transformers/pull/29402
        normalizer = mindspore.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
        hidden_states = hidden_states * normalizer
        if (
            use_cache and not isinstance(past_key_values, Cache) and not self.training
        ):  # kept for BC (non `Cache` `past_key_values` inputs)
            return_legacy_cache = True
            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            logger.warning_once(
                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
            )

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if return_legacy_cache:
            next_cache = next_cache.to_legacy_cache()

        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )

    def _update_causal_mask(
        self,
        attention_mask: mindspore.Tensor,
        input_tensor: mindspore.Tensor,
        cache_position: mindspore.Tensor,
        past_key_values: Cache,
        output_attentions: bool,
    ):
        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                is_training=self.training,
            ):
                return None

        dtype = input_tensor.dtype
        min_dtype = float(ops.finfo(dtype).min)
        sequence_length = input_tensor.shape[1]
        if using_static_cache:
            target_length = past_key_values.get_max_length()
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, mindspore.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = _prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            min_dtype=min_dtype,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
        )

        return causal_mask

mindnlp.transformers.models.gemma.modeling_gemma.apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1)

Applies Rotary Position Embedding to the query and key tensors.

PARAMETER	DESCRIPTION
q	The query tensor. TYPE: `mindspore.Tensor`
k	The key tensor. TYPE: `mindspore.Tensor`
cos	The cosine part of the rotary embedding. TYPE: `mindspore.Tensor`
sin	The sine part of the rotary embedding. TYPE: `mindspore.Tensor`
position_ids	Deprecated and unused. TYPE: `mindspore.Tensor`, *optional* DEFAULT: None
unsqueeze_dim	The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. TYPE: `int`, *optional*, defaults to 1 DEFAULT: 1

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`mindspore.Tensor`): The query tensor.
        k (`mindspore.Tensor`): The key tensor.
        cos (`mindspore.Tensor`): The cosine part of the rotary embedding.
        sin (`mindspore.Tensor`): The sine part of the rotary embedding.
        position_ids (`mindspore.Tensor`, *optional*):
            Deprecated and unused.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(mindspore.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed

mindnlp.transformers.models.gemma.modeling_gemma.repeat_kv(hidden_states, n_rep)

This is the equivalent of ops.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

def repeat_kv(hidden_states: mindspore.Tensor, n_rep: int) -> mindspore.Tensor:
    """
    This is the equivalent of ops.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].broadcast_to((batch, num_key_value_heads, n_rep, slen, head_dim))
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)

mindnlp.transformers.models.gemma.modeling_gemma.rotate_half(x)

Rotates half the hidden dims of the input.

Source code in mindnlp\transformers\models\gemma\modeling_gemma.py

def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return ops.cat((-x2, x1), dim=-1)

mindnlp.transformers.models.gemma.tokenization_gemma

Tokenization classes for Gemma.

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer

Bases: PreTrainedTokenizer

Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is no padding token in the original model.

PARAMETER	DESCRIPTION
vocab_file	Path to the vocabulary file. TYPE: `str`
unk_token	The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. TYPE: `str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"` DEFAULT: '<unk>'
bos_token	The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. TYPE: `str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"` DEFAULT: '<bos>'
eos_token	The end of sequence token. TYPE: `str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"` DEFAULT: '<eos>'
pad_token	A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by attention mechanisms or loss computation. TYPE: `str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"` DEFAULT: '<pad>'
sp_model_kwargs	Will be passed to the SentencePieceProcessor.__init__() method. The Python wrapper for SentencePiece can be used, among other things, to set: enable_sampling: Enable subword regularization. nbest_size: Sampling parameters for unigram. Invalid for BPE-Dropout. nbest_size = {0,1}: No sampling is performed. nbest_size > 1: samples from the nbest_size results. nbest_size < 0: assuming that nbest_size is infinite and samples from the all hypothesis (lattice) using forward-filtering-and-backward-sampling algorithm. alpha: Smoothing parameter for unigram sampling, and dropout probability of merge operations for BPE-dropout. TYPE: `Dict[str, Any]`, `Optional`, *optional* DEFAULT: None
add_bos_token	Whether or not to add an bos_token at the start of sequences. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
add_eos_token	Whether or not to add an eos_token at the end of sequences. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
clean_up_tokenization_spaces	Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
use_default_system_prompt	Whether or not the default system prompt for Gemma should be used. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
spaces_between_special_tokens	Whether or not to add spaces between special tokens. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

class GemmaTokenizer(PreTrainedTokenizer):
    """
    Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
    no padding token in the original model.

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`):
            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`):
            The end of sequence token.
        pad_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<pad>"`):
            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
            attention mechanisms or loss computation.
        sp_model_kwargs (`Dict[str, Any]`, `Optional`, *optional*):
            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
            to set:

            - `enable_sampling`: Enable subword regularization.
            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.

                - `nbest_size = {0,1}`: No sampling is performed.
                - `nbest_size > 1`: samples from the nbest_size results.
                - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
                using forward-filtering-and-backward-sampling algorithm.
                - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
                BPE-dropout.

        add_bos_token (`bool`, *optional*, defaults to `True`):
            Whether or not to add an `bos_token` at the start of sequences.
        add_eos_token (`bool`, *optional*, defaults to `False`):
            Whether or not to add an `eos_token` at the end of sequences.
        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
            extra spaces.
        use_default_system_prompt (`bool`, *optional*, defaults to `False`):
            Whether or not the default system prompt for Gemma should be used.
        spaces_between_special_tokens (`bool`, *optional*, defaults to `False`):
            Whether or not to add spaces between special tokens.
    """
    vocab_files_names = VOCAB_FILES_NAMES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        unk_token="<unk>",
        bos_token="<bos>",
        eos_token="<eos>",
        pad_token="<pad>",
        sp_model_kwargs: Optional[Dict[str, Any]] = None,
        add_bos_token=True,
        add_eos_token=False,
        clean_up_tokenization_spaces=False,
        use_default_system_prompt=False,
        spaces_between_special_tokens=False,
        **kwargs,
    ):
        """
        This method initializes an instance of GemmaTokenizer.

        Args:
            self: The instance of the class.
            vocab_file (str): The path to the vocabulary file.
            unk_token (str): The unknown token. Default is '<unk>'.
            bos_token (str): The beginning of sequence token. Default is '<bos>'.
            eos_token (str): The end of sequence token. Default is '<eos>'.
            pad_token (str): The padding token. Default is '<pad>'.
            sp_model_kwargs (Optional[Dict[str, Any]]): Optional keyword arguments for SentencePiece model configuration.
                Default is None.
            add_bos_token (bool): Whether to add the beginning of sequence token. Default is True.
            add_eos_token (bool): Whether to add the end of sequence token. Default is False.
            clean_up_tokenization_spaces (bool): Whether to clean up tokenization spaces. Default is False.
            use_default_system_prompt (bool): Whether to use the default system prompt. Default is False.
            spaces_between_special_tokens (bool): Whether to add spaces between special tokens. Default is False.

        Returns:
            None.

        Raises:
            ValueError: If the provided vocab_file is invalid or does not exist.
            OSError: If an I/O or OS error occurs while loading the vocab_file.
            TypeError: If the provided sp_model_kwargs is not a dictionary.
            RuntimeError: If an error occurs during the initialization process.
        """
        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
        bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
        unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token

        self.vocab_file = vocab_file
        self.add_bos_token = add_bos_token
        self.add_eos_token = add_eos_token
        self.use_default_system_prompt = use_default_system_prompt

        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.Load(vocab_file)

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            pad_token=pad_token,
            add_bos_token=add_bos_token,
            add_eos_token=add_eos_token,
            sp_model_kwargs=self.sp_model_kwargs,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            use_default_system_prompt=use_default_system_prompt,
            spaces_between_special_tokens=spaces_between_special_tokens,
            **kwargs,
        )

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.__getstate__
    def __getstate__(self):
        """
        Get the state of the GemmaTokenizer object for serialization.

        Args:
            self (GemmaTokenizer): The current instance of the GemmaTokenizer class.

        Returns:
            None.

        Raises:
            None.
        """
        state = self.__dict__.copy()
        state["sp_model"] = None
        state["sp_model_proto"] = self.sp_model.serialized_model_proto()
        return state

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.__setstate__
    def __setstate__(self, d):
        """
        This method '__setstate__' in the class 'GemmaTokenizer' is used to set the internal state of the tokenizer
        object based on the provided dictionary 'd'.

        Args:
            self (GemmaTokenizer): The instance of the GemmaTokenizer class on which this method is called.
                It represents the tokenizer object itself.
            d (dict): A dictionary containing the state information to be set on the tokenizer object.
                This dictionary should include the necessary information for reforwarding the object's state.

        Returns:
            None: This method does not return any value explicitly.
                It updates the state of the GemmaTokenizer object in-place.

        Raises:
            None:
                However, potential exceptions could be raised during the execution of the code within the method, such as:

                - TypeError: If the provided 'd' parameter is not a valid dictionary.
                - ValueError: If the 'sp_model_kwargs' or 'sp_model_proto' keys are missing in the 'd' dictionary.
                - Other exceptions related to the initialization or loading of the SentencePieceProcessor object may occur.
        """
        self.__dict__ = d
        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

    @property
    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.vocab_size
    def vocab_size(self):
        """Returns vocab size"""
        return self.sp_model.get_piece_size()

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.get_vocab
    def get_vocab(self):
        """Returns vocab as a dict"""
        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
        vocab.update(self.added_tokens_encoder)
        return vocab

    def _tokenize(self, text, **kwargs):
        """
        Returns a tokenized string. The Gemma tokenizer never adds a prefix space.
        """
        return self.sp_model.encode(text, out_type=str)

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer._convert_token_to_id
    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.sp_model.piece_to_id(token)

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer._convert_id_to_token
    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        token = self.sp_model.IdToPiece(index)
        return token

    def _decode(
        self,
        token_ids: List[int],
        skip_special_tokens: bool = False,
        spaces_between_special_tokens: bool = False,
        **kwargs,
    ) -> str:
        """
        Decodes a list of token IDs into a string representation.

        Args:
            self (GemmaTokenizer): An instance of the GemmaTokenizer class.
            token_ids (List[int]): A list of token IDs to be decoded.
            skip_special_tokens (bool, optional): Whether to skip special tokens during decoding. Defaults to False.
            spaces_between_special_tokens (bool, optional):
                Whether to include spaces between special tokens in the decoded string. Defaults to False.
            **kwargs: Additional keyword arguments.

        Returns:
            str: The decoded string representation of the token IDs.

        Raises:
            None.

        Note:
            - The method decodes the token IDs by iterating through the list and converting each ID into its corresponding text.
            - If skip_special_tokens is set to True, special tokens are ignored and not included in the decoded string.
            - If spaces_between_special_tokens is set to True, spaces are added between special tokens in the decoded string.
            - The decoding process utilizes the GemmaTokenizer's sp_model and _added_tokens_decoder attributes.

        Example:
            ```python
            >>> tokenizer = GemmaTokenizer()
            >>> token_ids = [101, 2054, 2003, 1037, 2154, 2008, 1037, 2307, 1012, 102]
            >>> tokenizer._decode(token_ids)
            '[CLS] This is a sample text. [SEP]'
            ```
        """
        sub_texts = []
        current_sub_text = []
        for ids in token_ids:
            if skip_special_tokens and ids in self.all_special_ids:
                continue
            if ids in self._added_tokens_decoder:
                if current_sub_text:
                    sub_texts.append(self.sp_model.decode(current_sub_text))
                sub_texts.append(self._added_tokens_decoder[ids].content)
                current_sub_text = []
            else:
                current_sub_text.append(ids)
        if current_sub_text:
            sub_texts.append(self.sp_model.decode(current_sub_text))

        if spaces_between_special_tokens:
            sub_texts = " ".join(sub_texts)
        else:
            sub_texts = "".join(sub_texts)

        return sub_texts

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        current_sub_tokens = []
        out_string = ""
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self._added_tokens_encoder:
                out_string += self.sp_model.decode(current_sub_tokens) + token
                current_sub_tokens = []
            else:
                current_sub_tokens.append(token)
        out_string += self.sp_model.decode(current_sub_tokens)
        return out_string

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.save_vocabulary
    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary and special tokens file to a directory.

        Args:
            save_directory (`str`):
                The directory in which to save the vocabulary.

        Returns:
            `Tuple(str)`: Paths to the files saved.
        """
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
            copyfile(self.vocab_file, out_vocab_file)
        elif not os.path.isfile(self.vocab_file):
            with open(out_vocab_file, "wb") as fi:
                content_spiece_model = self.sp_model.serialized_model_proto()
                fi.write(content_spiece_model)

        return (out_vocab_file,)

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.build_inputs_with_special_tokens
    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        ''' 
        build_inputs_with_special_tokens method in GemmaTokenizer class.

        This method takes three parameters:

        Args:
            self: GemmaTokenizer object.
            token_ids_0: list of integers. The token IDs for the first sequence.
            token_ids_1: (optional) list of integers. The token IDs for the second sequence.

        Returns:
            list of integers:
                The concatenated token IDs with special tokens added at the beginning and end of each sequence.

        Raises:
            None.
        '''
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = bos_token_id + token_ids_0 + eos_token_id

        if token_ids_1 is not None:
            output = output + bos_token_id + token_ids_1 + eos_token_id

        return output

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.get_special_tokens_mask
    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        """
        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer `prepare_for_model` method.

        Args:
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

        bos_token_id = [1] if self.add_bos_token else []
        eos_token_id = [1] if self.add_eos_token else []

        if token_ids_1 is None:
            return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(token_ids_0))
            + eos_token_id
            + bos_token_id
            + ([0] * len(token_ids_1))
            + eos_token_id
        )

    # Copied from transformers.models.llama.tokenization_llama.LlamaTokenizer.create_token_type_ids_from_sequences
    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
        sequence pair mask has the following format:

        ```
        0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
        | first sequence    | second sequence |
        ```

        if token_ids_1 is None, only returns the first portion of the mask (0s).

        Args:
            token_ids_0 (`List[int]`):
                List of ids.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.

        Returns:
            `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
        """
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)

        if token_ids_1 is not None:
            output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)

        return output

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.vocab_size property

Returns vocab size

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.__getstate__()

Get the state of the GemmaTokenizer object for serialization.

PARAMETER	DESCRIPTION
self	The current instance of the GemmaTokenizer class. TYPE: GemmaTokenizer

RETURNS	DESCRIPTION
	None.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def __getstate__(self):
    """
    Get the state of the GemmaTokenizer object for serialization.

    Args:
        self (GemmaTokenizer): The current instance of the GemmaTokenizer class.

    Returns:
        None.

    Raises:
        None.
    """
    state = self.__dict__.copy()
    state["sp_model"] = None
    state["sp_model_proto"] = self.sp_model.serialized_model_proto()
    return state

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.__init__(vocab_file, unk_token='<unk>', bos_token='<bos>', eos_token='<eos>', pad_token='<pad>', sp_model_kwargs=None, add_bos_token=True, add_eos_token=False, clean_up_tokenization_spaces=False, use_default_system_prompt=False, spaces_between_special_tokens=False, **kwargs)

This method initializes an instance of GemmaTokenizer.

PARAMETER	DESCRIPTION
self	The instance of the class.
vocab_file	The path to the vocabulary file. TYPE: str
unk_token	The unknown token. Default is ''. TYPE: str DEFAULT: '<unk>'
bos_token	The beginning of sequence token. Default is ''. TYPE: str DEFAULT: '<bos>'
eos_token	The end of sequence token. Default is ''. TYPE: str DEFAULT: '<eos>'
pad_token	The padding token. Default is ''. TYPE: str DEFAULT: '<pad>'
sp_model_kwargs	Optional keyword arguments for SentencePiece model configuration. Default is None. TYPE: Optional[Dict[str, Any]] DEFAULT: None
add_bos_token	Whether to add the beginning of sequence token. Default is True. TYPE: bool DEFAULT: True
add_eos_token	Whether to add the end of sequence token. Default is False. TYPE: bool DEFAULT: False
clean_up_tokenization_spaces	Whether to clean up tokenization spaces. Default is False. TYPE: bool DEFAULT: False
use_default_system_prompt	Whether to use the default system prompt. Default is False. TYPE: bool DEFAULT: False
spaces_between_special_tokens	Whether to add spaces between special tokens. Default is False. TYPE: bool DEFAULT: False

RETURNS	DESCRIPTION
	None.

RAISES	DESCRIPTION
ValueError	If the provided vocab_file is invalid or does not exist.
OSError	If an I/O or OS error occurs while loading the vocab_file.
TypeError	If the provided sp_model_kwargs is not a dictionary.
RuntimeError	If an error occurs during the initialization process.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def __init__(
    self,
    vocab_file,
    unk_token="<unk>",
    bos_token="<bos>",
    eos_token="<eos>",
    pad_token="<pad>",
    sp_model_kwargs: Optional[Dict[str, Any]] = None,
    add_bos_token=True,
    add_eos_token=False,
    clean_up_tokenization_spaces=False,
    use_default_system_prompt=False,
    spaces_between_special_tokens=False,
    **kwargs,
):
    """
    This method initializes an instance of GemmaTokenizer.

    Args:
        self: The instance of the class.
        vocab_file (str): The path to the vocabulary file.
        unk_token (str): The unknown token. Default is '<unk>'.
        bos_token (str): The beginning of sequence token. Default is '<bos>'.
        eos_token (str): The end of sequence token. Default is '<eos>'.
        pad_token (str): The padding token. Default is '<pad>'.
        sp_model_kwargs (Optional[Dict[str, Any]]): Optional keyword arguments for SentencePiece model configuration.
            Default is None.
        add_bos_token (bool): Whether to add the beginning of sequence token. Default is True.
        add_eos_token (bool): Whether to add the end of sequence token. Default is False.
        clean_up_tokenization_spaces (bool): Whether to clean up tokenization spaces. Default is False.
        use_default_system_prompt (bool): Whether to use the default system prompt. Default is False.
        spaces_between_special_tokens (bool): Whether to add spaces between special tokens. Default is False.

    Returns:
        None.

    Raises:
        ValueError: If the provided vocab_file is invalid or does not exist.
        OSError: If an I/O or OS error occurs while loading the vocab_file.
        TypeError: If the provided sp_model_kwargs is not a dictionary.
        RuntimeError: If an error occurs during the initialization process.
    """
    self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
    bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token
    eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token
    unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token
    pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token

    self.vocab_file = vocab_file
    self.add_bos_token = add_bos_token
    self.add_eos_token = add_eos_token
    self.use_default_system_prompt = use_default_system_prompt

    self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
    self.sp_model.Load(vocab_file)

    super().__init__(
        bos_token=bos_token,
        eos_token=eos_token,
        unk_token=unk_token,
        pad_token=pad_token,
        add_bos_token=add_bos_token,
        add_eos_token=add_eos_token,
        sp_model_kwargs=self.sp_model_kwargs,
        clean_up_tokenization_spaces=clean_up_tokenization_spaces,
        use_default_system_prompt=use_default_system_prompt,
        spaces_between_special_tokens=spaces_between_special_tokens,
        **kwargs,
    )

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.__setstate__(d)

This method 'setstate' in the class 'GemmaTokenizer' is used to set the internal state of the tokenizer object based on the provided dictionary 'd'.

PARAMETER	DESCRIPTION
self	The instance of the GemmaTokenizer class on which this method is called. It represents the tokenizer object itself. TYPE: GemmaTokenizer
d	A dictionary containing the state information to be set on the tokenizer object. This dictionary should include the necessary information for reforwarding the object's state. TYPE: dict

RETURNS	DESCRIPTION
None	This method does not return any value explicitly. It updates the state of the GemmaTokenizer object in-place.

RAISES	DESCRIPTION
None	However, potential exceptions could be raised during the execution of the code within the method, such as: TypeError: If the provided 'd' parameter is not a valid dictionary. ValueError: If the 'sp_model_kwargs' or 'sp_model_proto' keys are missing in the 'd' dictionary. Other exceptions related to the initialization or loading of the SentencePieceProcessor object may occur.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def __setstate__(self, d):
    """
    This method '__setstate__' in the class 'GemmaTokenizer' is used to set the internal state of the tokenizer
    object based on the provided dictionary 'd'.

    Args:
        self (GemmaTokenizer): The instance of the GemmaTokenizer class on which this method is called.
            It represents the tokenizer object itself.
        d (dict): A dictionary containing the state information to be set on the tokenizer object.
            This dictionary should include the necessary information for reforwarding the object's state.

    Returns:
        None: This method does not return any value explicitly.
            It updates the state of the GemmaTokenizer object in-place.

    Raises:
        None:
            However, potential exceptions could be raised during the execution of the code within the method, such as:

            - TypeError: If the provided 'd' parameter is not a valid dictionary.
            - ValueError: If the 'sp_model_kwargs' or 'sp_model_proto' keys are missing in the 'd' dictionary.
            - Other exceptions related to the initialization or loading of the SentencePieceProcessor object may occur.
    """
    self.__dict__ = d
    self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto)

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)

build_inputs_with_special_tokens method in GemmaTokenizer class.

This method takes three parameters:

PARAMETER	DESCRIPTION
self	GemmaTokenizer object.
token_ids_0	list of integers. The token IDs for the first sequence.
token_ids_1	(optional) list of integers. The token IDs for the second sequence. DEFAULT: None

RETURNS	DESCRIPTION
	list of integers: The concatenated token IDs with special tokens added at the beginning and end of each sequence.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
    ''' 
    build_inputs_with_special_tokens method in GemmaTokenizer class.

    This method takes three parameters:

    Args:
        self: GemmaTokenizer object.
        token_ids_0: list of integers. The token IDs for the first sequence.
        token_ids_1: (optional) list of integers. The token IDs for the second sequence.

    Returns:
        list of integers:
            The concatenated token IDs with special tokens added at the beginning and end of each sequence.

    Raises:
        None.
    '''
    bos_token_id = [self.bos_token_id] if self.add_bos_token else []
    eos_token_id = [self.eos_token_id] if self.add_eos_token else []

    output = bos_token_id + token_ids_0 + eos_token_id

    if token_ids_1 is not None:
        output = output + bos_token_id + token_ids_1 + eos_token_id

    return output

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.convert_tokens_to_string(tokens)

Converts a sequence of tokens (string) in a single string.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def convert_tokens_to_string(self, tokens):
    """Converts a sequence of tokens (string) in a single string."""
    current_sub_tokens = []
    out_string = ""
    for token in tokens:
        # make sure that special tokens are not decoded using sentencepiece model
        if token in self._added_tokens_encoder:
            out_string += self.sp_model.decode(current_sub_tokens) + token
            current_sub_tokens = []
        else:
            current_sub_tokens.append(token)
    out_string += self.sp_model.decode(current_sub_tokens)
    return out_string

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.create_token_type_ids_from_sequences(token_ids_0, token_ids_1=None)

Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format:

0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence    | second sequence |

if token_ids_1 is None, only returns the first portion of the mask (0s).

PARAMETER	DESCRIPTION
token_ids_0	List of ids. TYPE: `List[int]`
token_ids_1	Optional second list of IDs for sequence pairs. TYPE: `List[int]`, *optional* DEFAULT: None

RETURNS	DESCRIPTION
List[int]	List[int]: List of token type IDs according to the given sequence(s).

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def create_token_type_ids_from_sequences(
    self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
    """
    Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
    sequence pair mask has the following format:

    ```
    0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
    | first sequence    | second sequence |
    ```

    if token_ids_1 is None, only returns the first portion of the mask (0s).

    Args:
        token_ids_0 (`List[int]`):
            List of ids.
        token_ids_1 (`List[int]`, *optional*):
            Optional second list of IDs for sequence pairs.

    Returns:
        `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
    """
    bos_token_id = [self.bos_token_id] if self.add_bos_token else []
    eos_token_id = [self.eos_token_id] if self.add_eos_token else []

    output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)

    if token_ids_1 is not None:
        output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)

    return output

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.get_special_tokens_mask(token_ids_0, token_ids_1=None, already_has_special_tokens=False)

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer prepare_for_model method.

PARAMETER	DESCRIPTION
token_ids_0	List of IDs. TYPE: `List[int]`
token_ids_1	Optional second list of IDs for sequence pairs. TYPE: `List[int]`, *optional* DEFAULT: None
already_has_special_tokens	Whether or not the token list is already formatted with special tokens for the model. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

RETURNS	DESCRIPTION
List[int]	List[int]: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def get_special_tokens_mask(
    self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
    """
    Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
    special tokens using the tokenizer `prepare_for_model` method.

    Args:
        token_ids_0 (`List[int]`):
            List of IDs.
        token_ids_1 (`List[int]`, *optional*):
            Optional second list of IDs for sequence pairs.
        already_has_special_tokens (`bool`, *optional*, defaults to `False`):
            Whether or not the token list is already formatted with special tokens for the model.

    Returns:
        `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
    """
    if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
        )

    bos_token_id = [1] if self.add_bos_token else []
    eos_token_id = [1] if self.add_eos_token else []

    if token_ids_1 is None:
        return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
    return (
        bos_token_id
        + ([0] * len(token_ids_0))
        + eos_token_id
        + bos_token_id
        + ([0] * len(token_ids_1))
        + eos_token_id
    )

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.get_vocab()

Returns vocab as a dict

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def get_vocab(self):
    """Returns vocab as a dict"""
    vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
    vocab.update(self.added_tokens_encoder)
    return vocab

mindnlp.transformers.models.gemma.tokenization_gemma.GemmaTokenizer.save_vocabulary(save_directory, filename_prefix=None)

Save the vocabulary and special tokens file to a directory.

PARAMETER	DESCRIPTION
save_directory	The directory in which to save the vocabulary. TYPE: `str`

RETURNS	DESCRIPTION
Tuple[str]	Tuple(str): Paths to the files saved.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma.py

def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
    """
    Save the vocabulary and special tokens file to a directory.

    Args:
        save_directory (`str`):
            The directory in which to save the vocabulary.

    Returns:
        `Tuple(str)`: Paths to the files saved.
    """
    if not os.path.isdir(save_directory):
        logger.error(f"Vocabulary path ({save_directory}) should be a directory")
        return
    out_vocab_file = os.path.join(
        save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
    )

    if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
        copyfile(self.vocab_file, out_vocab_file)
    elif not os.path.isfile(self.vocab_file):
        with open(out_vocab_file, "wb") as fi:
            content_spiece_model = self.sp_model.serialized_model_proto()
            fi.write(content_spiece_model)

    return (out_vocab_file,)

mindnlp.transformers.models.gemma.tokenization_gemma_fast

Gemma Tokenizer

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast

Bases: PreTrainedTokenizerFast

Construct a Gemma tokenizer fast. Based on byte-level Byte-Pair-Encoding.

This uses notably ByteFallback and no prefix space. Normalization is applied to replace " " with "▁"

Example

>>> from transformers import GemmaTokenizerFast
...
>>> tokenizer = GemmaTokenizerFast.from_pretrained("hf-internal-testing/dummy-gemma")
>>> tokenizer.encode("Hello this is a test")
[2, 4521, 736, 603, 476, 2121]

If you want to change the bos_token or the eos_token, make sure to specify them when initializing the model, or call tokenizer.update_post_processor() to make sure that the post-processing is correctly done (otherwise the values of the first token and final token of an encoded sequence will not be correct). For more details, checkout [post-processors] (https://hf-mirror.com/docs/tokenizers/api/post-processors) documentation.

This tokenizer inherits from [PreTrainedTokenizerFast] which contains most of the main methods. Users should refer to this superclass for more information regarding those methods.

PARAMETER	DESCRIPTION
vocab_file	SentencePiece file (generally has a .model extension) that contains the vocabulary necessary to instantiate a tokenizer. TYPE: `str`, *optional* DEFAULT: None
tokenizer_file	tokenizers file (generally has a .json extension) that contains everything needed to load the tokenizer. TYPE: `str`, *optional* DEFAULT: None
clean_up_tokenization_spaces	Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False
unk_token	The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead. TYPE: `str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"` DEFAULT: '<unk>'
bos_token	The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token. TYPE: `str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"` DEFAULT: '<bos>'
eos_token	The end of sequence token. TYPE: `str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"` DEFAULT: '<eos>'
pad_token	The padding token TYPE: `str`, *optional*, defaults to `"<pad>"` DEFAULT: '<pad>'
add_bos_token	Whether or not to add an bos_token at the start of sequences. TYPE: `bool`, *optional*, defaults to `True` DEFAULT: True
add_eos_token	Whether or not to add an eos_token at the end of sequences. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

Source code in mindnlp\transformers\models\gemma\tokenization_gemma_fast.py

class GemmaTokenizerFast(PreTrainedTokenizerFast):
    """
    Construct a Gemma tokenizer fast. Based on byte-level Byte-Pair-Encoding.

    This uses notably ByteFallback and no prefix space. Normalization is applied to replace  `" "` with `"▁"`

    Example:
        ```python
        >>> from transformers import GemmaTokenizerFast
        ...
        >>> tokenizer = GemmaTokenizerFast.from_pretrained("hf-internal-testing/dummy-gemma")
        >>> tokenizer.encode("Hello this is a test")
        [2, 4521, 736, 603, 476, 2121]
        ```

    If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
    call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
    values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
    [post-processors] (https://hf-mirror.com/docs/tokenizers/api/post-processors) documentation.

    This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
    refer to this superclass for more information regarding those methods.

    Args:
        vocab_file (`str`, *optional*):
            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
            contains the vocabulary necessary to instantiate a tokenizer.
        tokenizer_file (`str`, *optional*):
            [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
            contains everything needed to load the tokenizer.
        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
            extra spaces.
        unk_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        bos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<bos>"`):
            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
        eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<eos>"`):
            The end of sequence token.
        pad_token (`str`, *optional*, defaults to `"<pad>"`):
            The padding token
        add_bos_token (`bool`, *optional*, defaults to `True`):
            Whether or not to add an `bos_token` at the start of sequences.
        add_eos_token (`bool`, *optional*, defaults to `False`):
            Whether or not to add an `eos_token` at the end of sequences.
    """
    vocab_files_names = VOCAB_FILES_NAMES
    slow_tokenizer_class = GemmaTokenizer
    padding_side = "left"
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file=None,
        tokenizer_file=None,
        clean_up_tokenization_spaces=False,
        unk_token="<unk>",
        bos_token="<bos>",
        eos_token="<eos>",
        pad_token="<pad>",
        add_bos_token=True,
        add_eos_token=False,
        **kwargs,
    ):
        """
        Initialize GemmaTokenizerFast object.

        Args:
            self (object): The GemmaTokenizerFast object itself.
            vocab_file (str, optional): Path to the vocabulary file. Default is None.
            tokenizer_file (str, optional): Path to the tokenizer file. Default is None.
            clean_up_tokenization_spaces (bool, optional): Whether to clean up tokenization spaces. Default is False.
            unk_token (str, optional): Unknown token to be used. Default is '<unk>'.
            bos_token (str, optional): Beginning of sentence token. Default is '<bos>'.
            eos_token (str, optional): End of sentence token. Default is '<eos>'.
            pad_token (str, optional): Padding token. Default is '<pad>'.
            add_bos_token (bool, optional): Whether to add the beginning of sentence token. Default is True.
            add_eos_token (bool, optional): Whether to add the end of sentence token. Default is False.

        Returns:
            None.

        Raises:
            None.
        """
        super().__init__(
            vocab_file=vocab_file,
            tokenizer_file=tokenizer_file,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            unk_token=unk_token,
            bos_token=bos_token,
            eos_token=eos_token,
            pad_token=pad_token,
            add_bos_token=add_bos_token,
            add_eos_token=add_eos_token,
            **kwargs,
        )
        self._add_bos_token = add_bos_token
        self._add_eos_token = add_eos_token
        self.update_post_processor()
        self.vocab_file = vocab_file

    @property
    def can_save_slow_tokenizer(self) -> bool:
        """
        Checks if the slow tokenizer can be saved.

        Args:
            self: An instance of the GemmaTokenizerFast class.

        Returns:
            bool:
                A boolean value indicating whether the slow tokenizer can be saved.
                Returns True if the vocab_file exists, otherwise False.

        Raises:
            None.
        """
        return os.path.isfile(self.vocab_file) if self.vocab_file else False

    # Copied from transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast.update_post_processor
    def update_post_processor(self):
        """
        Updates the underlying post processor with the current `bos_token` and `eos_token`.
        """
        bos = self.bos_token
        bos_token_id = self.bos_token_id
        if bos is None and self.add_bos_token:
            raise ValueError("add_bos_token = True but bos_token = None")

        eos = self.eos_token
        eos_token_id = self.eos_token_id
        if eos is None and self.add_eos_token:
            raise ValueError("add_eos_token = True but eos_token = None")

        single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
        pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"

        special_tokens = []
        if self.add_bos_token:
            special_tokens.append((bos, bos_token_id))
        if self.add_eos_token:
            special_tokens.append((eos, eos_token_id))
        self._tokenizer.post_processor = processors.TemplateProcessing(
            single=single, pair=pair, special_tokens=special_tokens
        )

    @property
    def add_eos_token(self):
        """
        Adds an end-of-sentence (EOS) token to the GemmaTokenizerFast object.

        Args:
            self: An instance of the GemmaTokenizerFast class.

        Returns:
            None.

        Raises:
            None.

        This method adds an EOS token to the GemmaTokenizerFast object.
        The EOS token is used to mark the end of a sentence or text sequence.
        It is commonly used in natural language processing tasks such as language modeling and text generation.
        By adding an EOS token, the GemmaTokenizerFast object can handle text sequences more effectively,
        allowing for better analysis and processing.
        """
        return self._add_eos_token

    @property
    def add_bos_token(self):
        """
        This method adds the beginning of sentence (BOS) token to the tokenizer.

        Args:
            self (GemmaTokenizerFast): The instance of GemmaTokenizerFast class.

        Returns:
            None.

        Raises:
            None.
        """
        return self._add_bos_token

    @add_eos_token.setter
    def add_eos_token(self, value):
        """Sets the value of the add_eos_token property in the GemmaTokenizerFast class.

        Args:
            self (GemmaTokenizerFast): The instance of GemmaTokenizerFast.
            value (bool): The value to set for the add_eos_token property.
                It determines whether to add an end-of-sequence token to the tokenized output.

        Returns:
            None.

        Raises:
            None.
        """
        self._add_eos_token = value
        self.update_post_processor()

    @add_bos_token.setter
    def add_bos_token(self, value):
        """
        Method: add_bos_token

        Description:
        Setter method for adding a beginning of sentence (BOS) token to the GemmaTokenizerFast.

        Args:
            self: (GemmaTokenizerFast) The instance of GemmaTokenizerFast.
            value: (bool) A boolean value indicating whether to add the BOS token.
                True enables adding the BOS token, while False disables it.

        Returns:
            None.

        Raises:
            None.
        """
        self._add_bos_token = value
        self.update_post_processor()

    # Copied from transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast.save_vocabulary
    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Save the vocabulary of the GemmaTokenizerFast instance to the specified directory with an optional filename prefix.

        Args:
            self (GemmaTokenizerFast): The instance of the GemmaTokenizerFast class.
            save_directory (str): The directory path where the vocabulary will be saved.
            filename_prefix (Optional[str], optional): An optional prefix to be added to the filename. Defaults to None.

        Returns:
            Tuple[str]: A tuple containing the path to the saved vocabulary file.

        Raises:
            ValueError:
                If the fast tokenizer does not have the necessary information to save the vocabulary for a slow
                tokenizer.
            OSError: If the save_directory provided is not a valid directory path.
            IOError: If an error occurs during the file copying process.
        """
        if not self.can_save_slow_tokenizer:
            raise ValueError(
                "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                "tokenizer."
            )

        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
            copyfile(self.vocab_file, out_vocab_file)

        return (out_vocab_file,)

    # Copied from transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast.build_inputs_with_special_tokens
    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        """
        Build inputs with special tokens for the GemmaTokenizerFast.

        Args:
            self (GemmaTokenizerFast): An instance of the GemmaTokenizerFast class.
            token_ids_0 (list): A list of token IDs representing the first sequence.
            token_ids_1 (list, optional): A list of token IDs representing the second sequence.
                Defaults to None.

        Returns:
            list: A list of token IDs representing the input sequences with added special tokens.

        Raises:
            None.

        This method takes two sequences of token IDs and adds special tokens, such as
        beginning of sequence (bos) and end of sequence (eos) tokens. The special tokens
        are added based on the configuration of the tokenizer.

        The token_ids_0 parameter is a list of token IDs representing the first sequence.
        This parameter is required.

        The token_ids_1 parameter is an optional list of token IDs representing the second
        sequence. If provided, the method concatenates the first and second sequences with
        the special tokens in between.

        The method returns a list of token IDs representing the input sequences with the
        special tokens added.

        Example:
            ```python
            >>> tokenizer = GemmaTokenizerFast()
            >>> token_ids_0 = [101, 202, 303]
            >>> token_ids_1 = [404, 505]
            >>> inputs = tokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1)
            >>> print(inputs)
            Output:
            [101, 202, 303, 102, 404, 505, 102]
            ```
        """
        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
        eos_token_id = [self.eos_token_id] if self.add_eos_token else []

        output = bos_token_id + token_ids_0 + eos_token_id

        if token_ids_1 is not None:
            output = output + bos_token_id + token_ids_1 + eos_token_id

        return output

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast.add_bos_token property writable

This method adds the beginning of sentence (BOS) token to the tokenizer.

PARAMETER	DESCRIPTION
self	The instance of GemmaTokenizerFast class. TYPE: GemmaTokenizerFast

RETURNS	DESCRIPTION
	None.

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast.add_eos_token property writable

Adds an end-of-sentence (EOS) token to the GemmaTokenizerFast object.

PARAMETER	DESCRIPTION
self	An instance of the GemmaTokenizerFast class.

RETURNS	DESCRIPTION
	None.

This method adds an EOS token to the GemmaTokenizerFast object. The EOS token is used to mark the end of a sentence or text sequence. It is commonly used in natural language processing tasks such as language modeling and text generation. By adding an EOS token, the GemmaTokenizerFast object can handle text sequences more effectively, allowing for better analysis and processing.

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast.can_save_slow_tokenizer: bool property

Checks if the slow tokenizer can be saved.

PARAMETER	DESCRIPTION
self	An instance of the GemmaTokenizerFast class.

RETURNS	DESCRIPTION
bool	A boolean value indicating whether the slow tokenizer can be saved. Returns True if the vocab_file exists, otherwise False. TYPE: bool

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast.__init__(vocab_file=None, tokenizer_file=None, clean_up_tokenization_spaces=False, unk_token='<unk>', bos_token='<bos>', eos_token='<eos>', pad_token='<pad>', add_bos_token=True, add_eos_token=False, **kwargs)

Initialize GemmaTokenizerFast object.

PARAMETER	DESCRIPTION
self	The GemmaTokenizerFast object itself. TYPE: object
vocab_file	Path to the vocabulary file. Default is None. TYPE: str DEFAULT: None
tokenizer_file	Path to the tokenizer file. Default is None. TYPE: str DEFAULT: None
clean_up_tokenization_spaces	Whether to clean up tokenization spaces. Default is False. TYPE: bool DEFAULT: False
unk_token	Unknown token to be used. Default is ''. TYPE: str DEFAULT: '<unk>'
bos_token	Beginning of sentence token. Default is ''. TYPE: str DEFAULT: '<bos>'
eos_token	End of sentence token. Default is ''. TYPE: str DEFAULT: '<eos>'
pad_token	Padding token. Default is ''. TYPE: str DEFAULT: '<pad>'
add_bos_token	Whether to add the beginning of sentence token. Default is True. TYPE: bool DEFAULT: True
add_eos_token	Whether to add the end of sentence token. Default is False. TYPE: bool DEFAULT: False

RETURNS	DESCRIPTION
	None.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma_fast.py

def __init__(
    self,
    vocab_file=None,
    tokenizer_file=None,
    clean_up_tokenization_spaces=False,
    unk_token="<unk>",
    bos_token="<bos>",
    eos_token="<eos>",
    pad_token="<pad>",
    add_bos_token=True,
    add_eos_token=False,
    **kwargs,
):
    """
    Initialize GemmaTokenizerFast object.

    Args:
        self (object): The GemmaTokenizerFast object itself.
        vocab_file (str, optional): Path to the vocabulary file. Default is None.
        tokenizer_file (str, optional): Path to the tokenizer file. Default is None.
        clean_up_tokenization_spaces (bool, optional): Whether to clean up tokenization spaces. Default is False.
        unk_token (str, optional): Unknown token to be used. Default is '<unk>'.
        bos_token (str, optional): Beginning of sentence token. Default is '<bos>'.
        eos_token (str, optional): End of sentence token. Default is '<eos>'.
        pad_token (str, optional): Padding token. Default is '<pad>'.
        add_bos_token (bool, optional): Whether to add the beginning of sentence token. Default is True.
        add_eos_token (bool, optional): Whether to add the end of sentence token. Default is False.

    Returns:
        None.

    Raises:
        None.
    """
    super().__init__(
        vocab_file=vocab_file,
        tokenizer_file=tokenizer_file,
        clean_up_tokenization_spaces=clean_up_tokenization_spaces,
        unk_token=unk_token,
        bos_token=bos_token,
        eos_token=eos_token,
        pad_token=pad_token,
        add_bos_token=add_bos_token,
        add_eos_token=add_eos_token,
        **kwargs,
    )
    self._add_bos_token = add_bos_token
    self._add_eos_token = add_eos_token
    self.update_post_processor()
    self.vocab_file = vocab_file

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast.build_inputs_with_special_tokens(token_ids_0, token_ids_1=None)

Build inputs with special tokens for the GemmaTokenizerFast.

PARAMETER	DESCRIPTION
self	An instance of the GemmaTokenizerFast class. TYPE: GemmaTokenizerFast
token_ids_0	A list of token IDs representing the first sequence. TYPE: list
token_ids_1	A list of token IDs representing the second sequence. Defaults to None. TYPE: list DEFAULT: None

RETURNS	DESCRIPTION
list	A list of token IDs representing the input sequences with added special tokens.

This method takes two sequences of token IDs and adds special tokens, such as beginning of sequence (bos) and end of sequence (eos) tokens. The special tokens are added based on the configuration of the tokenizer.

The token_ids_0 parameter is a list of token IDs representing the first sequence. This parameter is required.

The token_ids_1 parameter is an optional list of token IDs representing the second sequence. If provided, the method concatenates the first and second sequences with the special tokens in between.

The method returns a list of token IDs representing the input sequences with the special tokens added.

Example

>>> tokenizer = GemmaTokenizerFast()
>>> token_ids_0 = [101, 202, 303]
>>> token_ids_1 = [404, 505]
>>> inputs = tokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1)
>>> print(inputs)
Output:
[101, 202, 303, 102, 404, 505, 102]

Source code in mindnlp\transformers\models\gemma\tokenization_gemma_fast.py

def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
    """
    Build inputs with special tokens for the GemmaTokenizerFast.

    Args:
        self (GemmaTokenizerFast): An instance of the GemmaTokenizerFast class.
        token_ids_0 (list): A list of token IDs representing the first sequence.
        token_ids_1 (list, optional): A list of token IDs representing the second sequence.
            Defaults to None.

    Returns:
        list: A list of token IDs representing the input sequences with added special tokens.

    Raises:
        None.

    This method takes two sequences of token IDs and adds special tokens, such as
    beginning of sequence (bos) and end of sequence (eos) tokens. The special tokens
    are added based on the configuration of the tokenizer.

    The token_ids_0 parameter is a list of token IDs representing the first sequence.
    This parameter is required.

    The token_ids_1 parameter is an optional list of token IDs representing the second
    sequence. If provided, the method concatenates the first and second sequences with
    the special tokens in between.

    The method returns a list of token IDs representing the input sequences with the
    special tokens added.

    Example:
        ```python
        >>> tokenizer = GemmaTokenizerFast()
        >>> token_ids_0 = [101, 202, 303]
        >>> token_ids_1 = [404, 505]
        >>> inputs = tokenizer.build_inputs_with_special_tokens(token_ids_0, token_ids_1)
        >>> print(inputs)
        Output:
        [101, 202, 303, 102, 404, 505, 102]
        ```
    """
    bos_token_id = [self.bos_token_id] if self.add_bos_token else []
    eos_token_id = [self.eos_token_id] if self.add_eos_token else []

    output = bos_token_id + token_ids_0 + eos_token_id

    if token_ids_1 is not None:
        output = output + bos_token_id + token_ids_1 + eos_token_id

    return output

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast.save_vocabulary(save_directory, filename_prefix=None)

Save the vocabulary of the GemmaTokenizerFast instance to the specified directory with an optional filename prefix.

PARAMETER	DESCRIPTION
self	The instance of the GemmaTokenizerFast class. TYPE: GemmaTokenizerFast
save_directory	The directory path where the vocabulary will be saved. TYPE: str
filename_prefix	An optional prefix to be added to the filename. Defaults to None. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Tuple[str]	Tuple[str]: A tuple containing the path to the saved vocabulary file.

RAISES	DESCRIPTION
ValueError	If the fast tokenizer does not have the necessary information to save the vocabulary for a slow tokenizer.
OSError	If the save_directory provided is not a valid directory path.
IOError	If an error occurs during the file copying process.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma_fast.py

def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
    """
    Save the vocabulary of the GemmaTokenizerFast instance to the specified directory with an optional filename prefix.

    Args:
        self (GemmaTokenizerFast): The instance of the GemmaTokenizerFast class.
        save_directory (str): The directory path where the vocabulary will be saved.
        filename_prefix (Optional[str], optional): An optional prefix to be added to the filename. Defaults to None.

    Returns:
        Tuple[str]: A tuple containing the path to the saved vocabulary file.

    Raises:
        ValueError:
            If the fast tokenizer does not have the necessary information to save the vocabulary for a slow
            tokenizer.
        OSError: If the save_directory provided is not a valid directory path.
        IOError: If an error occurs during the file copying process.
    """
    if not self.can_save_slow_tokenizer:
        raise ValueError(
            "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
            "tokenizer."
        )

    if not os.path.isdir(save_directory):
        logger.error(f"Vocabulary path ({save_directory}) should be a directory")
        return
    out_vocab_file = os.path.join(
        save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
    )

    if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
        copyfile(self.vocab_file, out_vocab_file)

    return (out_vocab_file,)

mindnlp.transformers.models.gemma.tokenization_gemma_fast.GemmaTokenizerFast.update_post_processor()

Updates the underlying post processor with the current bos_token and eos_token.

Source code in mindnlp\transformers\models\gemma\tokenization_gemma_fast.py

def update_post_processor(self):
    """
    Updates the underlying post processor with the current `bos_token` and `eos_token`.
    """
    bos = self.bos_token
    bos_token_id = self.bos_token_id
    if bos is None and self.add_bos_token:
        raise ValueError("add_bos_token = True but bos_token = None")

    eos = self.eos_token
    eos_token_id = self.eos_token_id
    if eos is None and self.add_eos_token:
        raise ValueError("add_eos_token = True but eos_token = None")

    single = f"{(bos+':0 ') if self.add_bos_token else ''}$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
    pair = f"{single}{(' '+bos+':1') if self.add_bos_token else ''} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"

    special_tokens = []
    if self.add_bos_token:
        special_tokens.append((bos, bos_token_id))
    if self.add_eos_token:
        special_tokens.append((eos, eos_token_id))
    self._tokenizer.post_processor = processors.TemplateProcessing(
        single=single, pair=pair, special_tokens=special_tokens
    )