modules.deepseek_v2.modeling_deepseek_flax

DeepseekV2PreTrainedModel

Bases: EasyDeLFlaxPretrainedModel

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

class DeepseekV2PreTrainedModel(EasyDeLFlaxPretrainedModel):
    config_class: DeepseekV2Config = DeepseekV2Config
    module_class: nn.Module = None
    base_model_prefix = "model"

    def __init__(
            self,
            config: DeepseekV2Config,
            dtype: jnp.dtype = jnp.bfloat16,
            param_dtype: jnp.dtype = jnp.bfloat16,
            precision: Optional[jax.lax.Precision] = jax.lax.Precision("fastest"),
            input_shape: Tuple[int, int] = (1, 1),
            seed: int = 0,
            _do_init: bool = False,
            **kwargs
    ):
        module = self.module_class(
            config=config,
            dtype=dtype,
            param_dtype=param_dtype,
            precision=precision,
            **kwargs
        )

        super().__init__(
            dtype=dtype, _do_init=_do_init,
            module=module, config=config, input_shape=input_shape,
            seed=seed,
        )

    def init_weights(
            self,
            rng: jax.random.PRNGKey,
            input_shape: Tuple,
            params: FrozenDict = None
    ) -> FrozenDict:
        """
        The init_weights function is used to initialize the weights of a model.
        It takes in a rng, which is a random number generator key that can be used to generate random numbers.
        The input_shape parameter specifies the shape of the inputs that will be fed into this model.
        The params parameter allows you to pass in pre-trained weights for your model, if you have them available.

        :param self: Access variables that belong to the class
        :param rng: jax.random.PRNGKey: Initialize the weights of the model
        :param input_shape: Tuple: Initialize the input_ids, attention_mask and position_ids
        :param params: flax.core.FrozenDict: Pass in the parameters of a pre-trained model
        :return: A frozendict of parameters
        """

        self.config.initialization_of_moe = True
        input_ids = jnp.zeros(input_shape, dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(
            jnp.arange(jnp.atleast_2d(input_ids).shape[-1], dtype="i4"),
            input_shape,
        )
        params_rng, dropout_rng = jax.random.split(rng)
        rngs = {"params": params_rng, "dropout": dropout_rng}
        if self.config.add_cross_attention:
            encoder_hidden_states = jnp.zeros(
                input_shape + (self.config.hidden_size,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                position_ids,
                encoder_hidden_states,
                encoder_attention_mask,
                return_dict=False,
            )
        else:
            module_init_outputs = self.module.init(
                rngs,
                input_ids=input_ids,
                attention_mask=attention_mask,
                position_ids=position_ids,
                return_dict=False
            )
        random_params = module_init_outputs["params"]

        self.config.initialization_of_moe = False
        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    def init_cache(self, batch_size, max_length):

        input_ids = jnp.ones((batch_size, max_length))
        attention_mask = jnp.ones_like(input_ids)
        position_ids = jnp.broadcast_to(jnp.arange(
            jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        init_variables = self.module.init(
            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
        )
        return init_variables["cache"]

    def __call__(
            self,
            input_ids: chex.Array,
            attention_mask: Optional[chex.Array] = None,
            position_ids: Optional[chex.Array] = None,
            params: dict = None,
            past_key_values: dict = None,
            dropout_rng: jax.random.PRNGKey = None,
            train: bool = False,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            output_router_logits: Optional[bool] = None,
            return_dict: Optional[bool] = None,
            add_params_field: bool = False,
            **kwargs
    ):
        """
        The __call__ function is the main function of a JAX module.
        It takes as input:
        - The parameters of the model (self.params)
        - The inputs to the model (input_ids, attention_mask, position_ids)
        - Whether we are training (train=True/False) and whether we want to return all hidden states and
        attentions weights at each layer in addition to just the last layer output (output_hidden_states=True/False).

        :param self: Represent the instance of the class
        :param input_ids: Pass the input sequence to the model
        :param attention_mask: Mask out the padding tokens
        :param position_ids: Specify the position of each token in the sequence
        :param params: dict: Pass in the parameters of the model
        :param past_key_values: dict: Pass the past key values to the model
        :param dropout_rng: jax.random.PRNGKey: Pass in a random number generator key to the model
        :param train: bool: Determine whether to use dropout or not
        :param output_attentions: Optional[bool]: Determine whether to return the attention weights
        :param output_hidden_states: Optional[bool]: Determine whether to return the hidden states of all layers
        :param return_dict: Optional[bool]: Return a dictionary of the outputs
        :param add_params_field: bool: Add a params field to the inputs dictionary
        :return: A tuple of (last_hidden_state, past_key_values)

        """

        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.return_dict

        batch_size, sequence_length = input_ids.shape

        if position_ids is None:
            if past_key_values is not None:
                raise ValueError(
                    "Make sure to provide `position_ids` when passing `past_key_values`.")

            position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[
                                            None, :], (batch_size, sequence_length))

        if attention_mask is None:
            attention_mask = jnp.ones((batch_size, sequence_length))

        rng_s = {}
        if dropout_rng is not None:
            rng_s["dropout"] = dropout_rng

        inputs = {
            "params": params or self.params} if add_params_field else params or self.params

        if self.config.bits is not None:
            rng_s['params'] = jax.random.key(0)
        if past_key_values:
            inputs["cache"] = past_key_values
            mutable = ["cache"]
        else:
            mutable = False

        outputs = self.module.apply(
            inputs,
            jnp.array(input_ids, dtype="i4"),  # input_ids: chex.Array
            # attention_mask: Optional[chex.Array] = None
            jnp.array(attention_mask, dtype="i4"),
            # position_ids: Optional[chex.Array] = None
            jnp.array(position_ids, dtype="i4"),
            None,  # inputs_embeds: Optional[chex.Array] = None
            output_attentions,  # output_attentions: Optional[bool] = None
            # output_hidden_states: Optional[bool] = None
            output_hidden_states,
            # output_router_logits: Optional[bool] = None
            output_router_logits,
            False,  # init_cache: bool = False
            not train,  # deterministic: bool = True
            return_dict,  # return_dict: bool = True
            rngs=rng_s,
            mutable=mutable,
        )

        if past_key_values is not None and return_dict:
            outputs, past_key_values = outputs
            outputs["past_key_values"] = unfreeze(past_key_values["cache"])
            return outputs
        elif past_key_values is not None and not return_dict:
            outputs, past_key_values = outputs
            outputs = outputs[:1] + \
                      (unfreeze(past_key_values["cache"]),) + outputs[1:]

        return outputs

__call__(input_ids, attention_mask=None, position_ids=None, params=None, past_key_values=None, dropout_rng=None, train=False, output_attentions=None, output_hidden_states=None, output_router_logits=None, return_dict=None, add_params_field=False, **kwargs)

The call function is the main function of a JAX module. It takes as input: - The parameters of the model (self.params) - The inputs to the model (input_ids, attention_mask, position_ids) - Whether we are training (train=True/False) and whether we want to return all hidden states and attentions weights at each layer in addition to just the last layer output (output_hidden_states=True/False).

Parameters:

Name	Type	Description	Default
self		Represent the instance of the class	required
input_ids	Array	Pass the input sequence to the model	required
attention_mask	Optional[Array]	Mask out the padding tokens	None
position_ids	Optional[Array]	Specify the position of each token in the sequence	None
params	dict	dict: Pass in the parameters of the model	None
past_key_values	dict	dict: Pass the past key values to the model	None
dropout_rng	PRNGKey	jax.random.PRNGKey: Pass in a random number generator key to the model	None
train	bool	bool: Determine whether to use dropout or not	False
output_attentions	Optional[bool]	Optional[bool]: Determine whether to return the attention weights	None
output_hidden_states	Optional[bool]	Optional[bool]: Determine whether to return the hidden states of all layers	None
return_dict	Optional[bool]	Optional[bool]: Return a dictionary of the outputs	None
add_params_field	bool	bool: Add a params field to the inputs dictionary	False

Returns:

Type	Description
	A tuple of (last_hidden_state, past_key_values)

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

def __call__(
        self,
        input_ids: chex.Array,
        attention_mask: Optional[chex.Array] = None,
        position_ids: Optional[chex.Array] = None,
        params: dict = None,
        past_key_values: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        add_params_field: bool = False,
        **kwargs
):
    """
    The __call__ function is the main function of a JAX module.
    It takes as input:
    - The parameters of the model (self.params)
    - The inputs to the model (input_ids, attention_mask, position_ids)
    - Whether we are training (train=True/False) and whether we want to return all hidden states and
    attentions weights at each layer in addition to just the last layer output (output_hidden_states=True/False).

    :param self: Represent the instance of the class
    :param input_ids: Pass the input sequence to the model
    :param attention_mask: Mask out the padding tokens
    :param position_ids: Specify the position of each token in the sequence
    :param params: dict: Pass in the parameters of the model
    :param past_key_values: dict: Pass the past key values to the model
    :param dropout_rng: jax.random.PRNGKey: Pass in a random number generator key to the model
    :param train: bool: Determine whether to use dropout or not
    :param output_attentions: Optional[bool]: Determine whether to return the attention weights
    :param output_hidden_states: Optional[bool]: Determine whether to return the hidden states of all layers
    :param return_dict: Optional[bool]: Return a dictionary of the outputs
    :param add_params_field: bool: Add a params field to the inputs dictionary
    :return: A tuple of (last_hidden_state, past_key_values)

    """

    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.return_dict

    batch_size, sequence_length = input_ids.shape

    if position_ids is None:
        if past_key_values is not None:
            raise ValueError(
                "Make sure to provide `position_ids` when passing `past_key_values`.")

        position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[
                                        None, :], (batch_size, sequence_length))

    if attention_mask is None:
        attention_mask = jnp.ones((batch_size, sequence_length))

    rng_s = {}
    if dropout_rng is not None:
        rng_s["dropout"] = dropout_rng

    inputs = {
        "params": params or self.params} if add_params_field else params or self.params

    if self.config.bits is not None:
        rng_s['params'] = jax.random.key(0)
    if past_key_values:
        inputs["cache"] = past_key_values
        mutable = ["cache"]
    else:
        mutable = False

    outputs = self.module.apply(
        inputs,
        jnp.array(input_ids, dtype="i4"),  # input_ids: chex.Array
        # attention_mask: Optional[chex.Array] = None
        jnp.array(attention_mask, dtype="i4"),
        # position_ids: Optional[chex.Array] = None
        jnp.array(position_ids, dtype="i4"),
        None,  # inputs_embeds: Optional[chex.Array] = None
        output_attentions,  # output_attentions: Optional[bool] = None
        # output_hidden_states: Optional[bool] = None
        output_hidden_states,
        # output_router_logits: Optional[bool] = None
        output_router_logits,
        False,  # init_cache: bool = False
        not train,  # deterministic: bool = True
        return_dict,  # return_dict: bool = True
        rngs=rng_s,
        mutable=mutable,
    )

    if past_key_values is not None and return_dict:
        outputs, past_key_values = outputs
        outputs["past_key_values"] = unfreeze(past_key_values["cache"])
        return outputs
    elif past_key_values is not None and not return_dict:
        outputs, past_key_values = outputs
        outputs = outputs[:1] + \
                  (unfreeze(past_key_values["cache"]),) + outputs[1:]

    return outputs

init_weights(rng, input_shape, params=None)

The init_weights function is used to initialize the weights of a model. It takes in a rng, which is a random number generator key that can be used to generate random numbers. The input_shape parameter specifies the shape of the inputs that will be fed into this model. The params parameter allows you to pass in pre-trained weights for your model, if you have them available.

Parameters:

Name	Type	Description	Default
self		Access variables that belong to the class	required
rng	PRNGKey	jax.random.PRNGKey: Initialize the weights of the model	required
input_shape	Tuple	Tuple: Initialize the input_ids, attention_mask and position_ids	required
params	FrozenDict	flax.core.FrozenDict: Pass in the parameters of a pre-trained model	None

Returns:

Type	Description
FrozenDict	A frozendict of parameters

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

def init_weights(
        self,
        rng: jax.random.PRNGKey,
        input_shape: Tuple,
        params: FrozenDict = None
) -> FrozenDict:
    """
    The init_weights function is used to initialize the weights of a model.
    It takes in a rng, which is a random number generator key that can be used to generate random numbers.
    The input_shape parameter specifies the shape of the inputs that will be fed into this model.
    The params parameter allows you to pass in pre-trained weights for your model, if you have them available.

    :param self: Access variables that belong to the class
    :param rng: jax.random.PRNGKey: Initialize the weights of the model
    :param input_shape: Tuple: Initialize the input_ids, attention_mask and position_ids
    :param params: flax.core.FrozenDict: Pass in the parameters of a pre-trained model
    :return: A frozendict of parameters
    """

    self.config.initialization_of_moe = True
    input_ids = jnp.zeros(input_shape, dtype="i4")
    attention_mask = jnp.ones_like(input_ids, dtype="i4")
    position_ids = jnp.broadcast_to(
        jnp.arange(jnp.atleast_2d(input_ids).shape[-1], dtype="i4"),
        input_shape,
    )
    params_rng, dropout_rng = jax.random.split(rng)
    rngs = {"params": params_rng, "dropout": dropout_rng}
    if self.config.add_cross_attention:
        encoder_hidden_states = jnp.zeros(
            input_shape + (self.config.hidden_size,))
        encoder_attention_mask = attention_mask
        module_init_outputs = self.module.init(
            rngs,
            input_ids,
            attention_mask,
            position_ids,
            encoder_hidden_states,
            encoder_attention_mask,
            return_dict=False,
        )
    else:
        module_init_outputs = self.module.init(
            rngs,
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            return_dict=False
        )
    random_params = module_init_outputs["params"]

    self.config.initialization_of_moe = False
    if params is not None:
        random_params = flatten_dict(unfreeze(random_params))
        params = flatten_dict(unfreeze(params))
        for missing_key in self._missing_keys:
            params[missing_key] = random_params[missing_key]
        self._missing_keys = set()
        return freeze(unflatten_dict(params))
    else:
        return random_params

FlaxDeepseekV2ForCausalLM

Bases: DeepseekV2PreTrainedModel

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

class FlaxDeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
    module_class = FlaxDeepseekV2ForCausalLMModule

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

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

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

    def get_decoder(self):
        return self.module.model

    def get_output_embeddings(self):
        return self.module.lm_head

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

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[chex.Array] = None):
        """
        The prepare_inputs_for_generation function is used to prepare the inputs for a generation task.

        :param self: Access variables that belong to the class
        :param input_ids: Pass in the input tokens
        :param max_length: Set the length of the sequence to be generated
        :param attention_mask: Optional[chex.Array]: Mask the attention weights
        :return: A dictionary of the past_key_values, attention_mask and position ids

        """
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        extended_attention_mask = jnp.ones(
            (batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(
                extended_attention_mask, attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[
                                            None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "attention_mask": extended_attention_mask,
            "position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs

prepare_inputs_for_generation(input_ids, max_length, attention_mask=None)

The prepare_inputs_for_generation function is used to prepare the inputs for a generation task.

Parameters:

Name	Type	Description	Default
self		Access variables that belong to the class	required
input_ids		Pass in the input tokens	required
max_length		Set the length of the sequence to be generated	required
attention_mask	Optional[Array]	Optional[chex.Array]: Mask the attention weights	None

Returns:

Type	Description
	A dictionary of the past_key_values, attention_mask and position ids

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[chex.Array] = None):
    """
    The prepare_inputs_for_generation function is used to prepare the inputs for a generation task.

    :param self: Access variables that belong to the class
    :param input_ids: Pass in the input tokens
    :param max_length: Set the length of the sequence to be generated
    :param attention_mask: Optional[chex.Array]: Mask the attention weights
    :return: A dictionary of the past_key_values, attention_mask and position ids

    """
    batch_size, seq_length = input_ids.shape

    past_key_values = self.init_cache(batch_size, max_length)
    extended_attention_mask = jnp.ones(
        (batch_size, max_length), dtype="i4")
    if attention_mask is not None:
        position_ids = attention_mask.cumsum(axis=-1) - 1
        extended_attention_mask = lax.dynamic_update_slice(
            extended_attention_mask, attention_mask, (0, 0))
    else:
        position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[
                                        None, :], (batch_size, seq_length))

    return {
        "past_key_values": past_key_values,
        "attention_mask": extended_attention_mask,
        "position_ids": position_ids,
    }

FlaxDeepseekV2ForCausalLMModule

Bases: Module

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

class FlaxDeepseekV2ForCausalLMModule(nn.Module):
    config: DeepseekV2Config
    dtype: jnp.dtype = jnp.bfloat16
    param_dtype: jnp.dtype = jnp.bfloat16
    precision: Optional[jax.lax.Precision] = jax.lax.Precision("fastest")

    def setup(self) -> None:
        self.model = FlaxDeepseekV2Module(
            config=self.config,
            dtype=self.dtype,
            param_dtype=self.param_dtype,
            precision=self.precision
        )
        self.lm_head = nn.Linear(
            self.config.vocab_size,
            dtype=self.dtype,
            param_dtype=self.param_dtype,
            precision=self.precision,
            use_bias=False,
            kernel_init=nn.initializers.normal(self.config.initializer_range),
            **get_dot_general_by_bits(self.config.bits, self.config.easy_method)
        )

    def __call__(
            self,
            input_ids: chex.Array,
            attention_mask: chex.Array,
            position_ids: chex.Array,
            deterministic: bool = True,
            inputs_embeds: chex.Array = None,
            init_cache: bool = False,
            output_attentions: bool = False,
            output_hidden_states: bool = False,
            return_dict: bool = True,
    ):
        """
            The __call__ function is the main function of a Flax module. It defines how the model will be called,
            and what it returns. In this case, we are calling our Transformer model with input_ids and attention_mask
            as inputs (these are defined in __init__). We also have some optional arguments that can be passed to
            the call function: deterministic (whether to use dropout), inputs_embeds (if you want to pass your own embeddings),
            output_attentions and output_hidden states which return additional outputs from the transformer layers if set True. Finally,

            :param self: Refer to the object itself
            :param input_ids: chex.Array: Pass in the input tokens
            :param attention_mask: chex.Array: Mask out the padding tokens
            :param position_ids: chex.Array: Specify the position of each token in the sequence
            :param deterministic: bool: Determine whether to use dropout in the model
            :param inputs_embeds: chex.Array: Pass in the embeddings of the input tokens
            :param init_cache: bool: Initialize the cache for the decoder
            :param output_attentions: bool: Return the attention weights
            :param output_hidden_states: bool: Return the hidden states of all layers
            :param return_dict: bool: Return a dictionary of the outputs or just the logits
            :param : Determine whether to return the logits or not
            :return: A tuple of (lm_logits, hidden_states, attentions)

        """
        batch_size, seq_length = input_ids.shape

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)
        if position_ids is None:
            position_ids = jnp.broadcast_to(
                jnp.clip(jnp.cumsum(attention_mask, axis=-1) - 1, a_min=0),
                (batch_size, seq_length)
            )
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            deterministic=deterministic,
            inputs_embeds=inputs_embeds,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict
        )

        hidden_states = outputs[0]

        if self.config.tie_word_embeddings:
            shared_kernel = self.transformer.variables["params"]["embed_tokens"]["embedding"]
            shared_kernel = fjformer.linen.linen.control_quantization(shared_kernel, self.param_dtype).T
            lm_logits = self.lm_head.apply(
                {"params": {"kernel": shared_kernel}}, hidden_states)
        else:
            lm_logits = self.lm_head(hidden_states)

        # lm_logits = lm_logits.astype(jnp.float32)

        if not return_dict:
            return (lm_logits,) + outputs[1:]

        return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)

__call__(input_ids, attention_mask, position_ids, deterministic=True, inputs_embeds=None, init_cache=False, output_attentions=False, output_hidden_states=False, return_dict=True)

The call function is the main function of a Flax module. It defines how the model will be called, and what it returns. In this case, we are calling our Transformer model with input_ids and attention_mask as inputs (these are defined in init). We also have some optional arguments that can be passed to the call function: deterministic (whether to use dropout), inputs_embeds (if you want to pass your own embeddings), output_attentions and output_hidden states which return additional outputs from the transformer layers if set True. Finally,

Parameters:

Name	Type	Description	Default
self		Refer to the object itself	required
input_ids	Array	chex.Array: Pass in the input tokens	required
attention_mask	Array	chex.Array: Mask out the padding tokens	required
position_ids	Array	chex.Array: Specify the position of each token in the sequence	required
deterministic	bool	bool: Determine whether to use dropout in the model	True
inputs_embeds	Array	chex.Array: Pass in the embeddings of the input tokens	None
init_cache	bool	bool: Initialize the cache for the decoder	False
output_attentions	bool	bool: Return the attention weights	False
output_hidden_states	bool	bool: Return the hidden states of all layers	False
return_dict	bool	bool: Return a dictionary of the outputs or just the logits	True
		Determine whether to return the logits or not	required

Returns:

Type	Description
	A tuple of (lm_logits, hidden_states, attentions)

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

def __call__(
        self,
        input_ids: chex.Array,
        attention_mask: chex.Array,
        position_ids: chex.Array,
        deterministic: bool = True,
        inputs_embeds: chex.Array = None,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
):
    """
        The __call__ function is the main function of a Flax module. It defines how the model will be called,
        and what it returns. In this case, we are calling our Transformer model with input_ids and attention_mask
        as inputs (these are defined in __init__). We also have some optional arguments that can be passed to
        the call function: deterministic (whether to use dropout), inputs_embeds (if you want to pass your own embeddings),
        output_attentions and output_hidden states which return additional outputs from the transformer layers if set True. Finally,

        :param self: Refer to the object itself
        :param input_ids: chex.Array: Pass in the input tokens
        :param attention_mask: chex.Array: Mask out the padding tokens
        :param position_ids: chex.Array: Specify the position of each token in the sequence
        :param deterministic: bool: Determine whether to use dropout in the model
        :param inputs_embeds: chex.Array: Pass in the embeddings of the input tokens
        :param init_cache: bool: Initialize the cache for the decoder
        :param output_attentions: bool: Return the attention weights
        :param output_hidden_states: bool: Return the hidden states of all layers
        :param return_dict: bool: Return a dictionary of the outputs or just the logits
        :param : Determine whether to return the logits or not
        :return: A tuple of (lm_logits, hidden_states, attentions)

    """
    batch_size, seq_length = input_ids.shape

    if attention_mask is None:
        attention_mask = jnp.ones_like(input_ids)
    if position_ids is None:
        position_ids = jnp.broadcast_to(
            jnp.clip(jnp.cumsum(attention_mask, axis=-1) - 1, a_min=0),
            (batch_size, seq_length)
        )
    outputs = self.model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        position_ids=position_ids,
        deterministic=deterministic,
        inputs_embeds=inputs_embeds,
        init_cache=init_cache,
        output_attentions=output_attentions,
        output_hidden_states=output_hidden_states,
        return_dict=return_dict
    )

    hidden_states = outputs[0]

    if self.config.tie_word_embeddings:
        shared_kernel = self.transformer.variables["params"]["embed_tokens"]["embedding"]
        shared_kernel = fjformer.linen.linen.control_quantization(shared_kernel, self.param_dtype).T
        lm_logits = self.lm_head.apply(
            {"params": {"kernel": shared_kernel}}, hidden_states)
    else:
        lm_logits = self.lm_head(hidden_states)

    # lm_logits = lm_logits.astype(jnp.float32)

    if not return_dict:
        return (lm_logits,) + outputs[1:]

    return FlaxCausalLMOutput(logits=lm_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)

FlaxDeepseekV2Module

Bases: Module

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

class FlaxDeepseekV2Module(nn.Module):
    config: DeepseekV2Config
    dtype: jnp.dtype = jnp.bfloat16
    param_dtype: jnp.dtype = jnp.bfloat16
    precision: Optional[Union[jax.lax.Precision, str]] = None

    def setup(self):

        self.embed_tokens = nn.Embed(
            self.config.vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(
                stddev=self.config.initializer_range),
            dtype=self.dtype,
            param_dtype=self.param_dtype,
        )

        self.layers = FlaxDeepseekV2DecoratorCollection(
            self.config,
            dtype=self.dtype,
            param_dtype=self.param_dtype,
            precision=self.precision
        )
        self.norm = DeepseekV2RMSNorm(
            self.config.hidden_size,
            eps=self.config.rms_norm_eps,
            dtype=self.dtype,
            param_dtype=self.param_dtype
        )

        initial_rope_kwargs = {}
        method = None
        if self.config.rope_scaling is not None:
            scaling_type = self.config.rope_scaling["type"]
            method = scaling_type
            if scaling_type != "yarn":
                initial_rope_kwargs = dict(scaling_factor=self.config.rope_scaling["factor"])
            else:
                initial_rope_kwargs = {
                    key: self.config.rope_scaling[key]
                    for key in [
                        "original_max_position_embeddings",
                        "beta_fast",
                        "beta_slow",
                        "mscale",
                        "mscale_all_dim",
                    ]
                    if key in self.config.rope_scaling
                }
                initial_rope_kwargs["scaling_factor"] = self.config.rope_scaling["factor"]
        self.freq_cis = init_deepseek_rotary_embedding(
            dim=self.config.hidden_size // self.config.num_attention_heads,
            max_position_embeddings=(
                getattr(
                    self.config,
                    "freq_max_position_embeddings",
                    self.config.max_position_embeddings
                )
            ),
            base=self.config.rope_theta,
            method=method,  # type:ignore
            kwargs=initial_rope_kwargs
        )
        self.causal_mask = flax.linen.make_causal_mask(
            jnp.ones(
                (
                    1,
                    getattr(
                        self.config,
                        "c_max_position_embeddings",
                        self.config.max_position_embeddings
                    )
                ),
                dtype="bool"
            ),
            dtype="bool"
        )

    def __call__(
            self,
            input_ids: Optional[chex.Array] = None,
            attention_mask: Optional[chex.Array] = None,
            position_ids: Optional[chex.Array] = None,
            deterministic: bool = True,
            inputs_embeds: chex.Array = None,
            init_cache: bool = False,
            output_attentions: bool = False,
            output_hidden_states: bool = False,
            return_dict: bool = True,
    ) -> typing.Union[Tuple[chex.Array, ...], FlaxBaseModelOutput]:
        """
        The __call__ function is the main function of a Flax model.
        It takes in input_ids, attention_mask, and position_ids as inputs to the model.
        The output is a tuple containing: last hidden state (hidden states), all hidden states (if output_hidden_states=True), attentions (if output attentions=True).


        :param self: Represent the instance of the class
        :param input_ids: chex.Array: Pass in the input ids
        :param attention_mask: chex.Array: Mask out the attention weights for certain tokens
        :param position_ids: chex.Array: Determine the position of each token in a sequence
        :param deterministic: bool: Determine whether to use dropout or not
        :param inputs_embeds: chex.Array: Pass in the embedding of the input_ids
        :param init_cache: bool: Initialize the cache for the decoder
        :param output_attentions: bool: Determine whether to return the attention weights or not
        :param output_hidden_states: bool: Return all hidden states or just the last one
        :param return_dict: bool: Return a dictionary of the outputs or not
        :param : Determine whether the model is in training mode or not
        :return: A tuple of the hidden states, all hidden states, and attentions

        """
        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids.astype("i4"))
        if attention_mask.ndim == 2:
            b, s = attention_mask.shape
            attention_mask = attention_mask.reshape(b, 1, 1, s)

        outputs = self.layers(
            hidden_states=inputs_embeds,
            attention_mask=attention_mask,
            position_ids=position_ids,
            freq_cis=self.freq_cis,
            init_cache=init_cache,
            output_attentions=output_attentions,
            deterministic=deterministic,
            causal_mask=self.causal_mask
        )

        hidden_states = outputs[0]
        hidden_states = self.norm(hidden_states)

        if output_hidden_states:
            all_hidden_states = outputs[1] + (hidden_states,)
            outputs = (hidden_states, all_hidden_states) + outputs[2:]
        else:
            outputs = (hidden_states,) + outputs[1:]

        if not return_dict:
            return tuple(value for value in outputs if value is not None)

        return FlaxBaseModelOutput(
            last_hidden_state=hidden_states,
            hidden_states=outputs[1],
            attentions=outputs[-1],
        )

__call__(input_ids=None, attention_mask=None, position_ids=None, deterministic=True, inputs_embeds=None, init_cache=False, output_attentions=False, output_hidden_states=False, return_dict=True)

The call function is the main function of a Flax model. It takes in input_ids, attention_mask, and position_ids as inputs to the model. The output is a tuple containing: last hidden state (hidden states), all hidden states (if output_hidden_states=True), attentions (if output attentions=True).

Parameters:

Name	Type	Description	Default
self		Represent the instance of the class	required
input_ids	Optional[Array]	chex.Array: Pass in the input ids	None
attention_mask	Optional[Array]	chex.Array: Mask out the attention weights for certain tokens	None
position_ids	Optional[Array]	chex.Array: Determine the position of each token in a sequence	None
deterministic	bool	bool: Determine whether to use dropout or not	True
inputs_embeds	Array	chex.Array: Pass in the embedding of the input_ids	None
init_cache	bool	bool: Initialize the cache for the decoder	False
output_attentions	bool	bool: Determine whether to return the attention weights or not	False
output_hidden_states	bool	bool: Return all hidden states or just the last one	False
return_dict	bool	bool: Return a dictionary of the outputs or not	True
		Determine whether the model is in training mode or not	required

Returns:

Type	Description
Union[Tuple[Array, ...], FlaxBaseModelOutput]	A tuple of the hidden states, all hidden states, and attentions

Source code in src/python/easydel/modules/deepseek_v2/modeling_deepseek_flax.py

def __call__(
        self,
        input_ids: Optional[chex.Array] = None,
        attention_mask: Optional[chex.Array] = None,
        position_ids: Optional[chex.Array] = None,
        deterministic: bool = True,
        inputs_embeds: chex.Array = None,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
) -> typing.Union[Tuple[chex.Array, ...], FlaxBaseModelOutput]:
    """
    The __call__ function is the main function of a Flax model.
    It takes in input_ids, attention_mask, and position_ids as inputs to the model.
    The output is a tuple containing: last hidden state (hidden states), all hidden states (if output_hidden_states=True), attentions (if output attentions=True).


    :param self: Represent the instance of the class
    :param input_ids: chex.Array: Pass in the input ids
    :param attention_mask: chex.Array: Mask out the attention weights for certain tokens
    :param position_ids: chex.Array: Determine the position of each token in a sequence
    :param deterministic: bool: Determine whether to use dropout or not
    :param inputs_embeds: chex.Array: Pass in the embedding of the input_ids
    :param init_cache: bool: Initialize the cache for the decoder
    :param output_attentions: bool: Determine whether to return the attention weights or not
    :param output_hidden_states: bool: Return all hidden states or just the last one
    :param return_dict: bool: Return a dictionary of the outputs or not
    :param : Determine whether the model is in training mode or not
    :return: A tuple of the hidden states, all hidden states, and attentions

    """
    if inputs_embeds is None:
        inputs_embeds = self.embed_tokens(input_ids.astype("i4"))
    if attention_mask.ndim == 2:
        b, s = attention_mask.shape
        attention_mask = attention_mask.reshape(b, 1, 1, s)

    outputs = self.layers(
        hidden_states=inputs_embeds,
        attention_mask=attention_mask,
        position_ids=position_ids,
        freq_cis=self.freq_cis,
        init_cache=init_cache,
        output_attentions=output_attentions,
        deterministic=deterministic,
        causal_mask=self.causal_mask
    )

    hidden_states = outputs[0]
    hidden_states = self.norm(hidden_states)

    if output_hidden_states:
        all_hidden_states = outputs[1] + (hidden_states,)
        outputs = (hidden_states, all_hidden_states) + outputs[2:]
    else:
        outputs = (hidden_states,) + outputs[1:]

    if not return_dict:
        return tuple(value for value in outputs if value is not None)

    return FlaxBaseModelOutput(
        last_hidden_state=hidden_states,
        hidden_states=outputs[1],
        attentions=outputs[-1],
    )