modules.dbrx.modelling_dbrx_flax
DbrxPreTrainedModel
Bases: EasyDeLFlaxPretrainedModel
Source code in src/python/easydel/modules/dbrx/modelling_dbrx_flax.py
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__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/dbrx/modelling_dbrx_flax.py
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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/dbrx/modelling_dbrx_flax.py
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FlaxDbrxAttention
Bases: BaseJAXAttentionModule
Source code in src/python/easydel/modules/dbrx/modelling_dbrx_flax.py
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__call__(hidden_states, freq_cis, attention_mask, position_ids, causal_mask, segment_ids=None, deterministic=True, init_cache=False, output_attentions=False, fcm_mask=None)
The call function is the main function of a JAX module. It defines how the module behaves when called with inputs. The call function can be thought of as a "forward pass" through the model, and it should return all outputs that are needed for training or inference.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
self |
Access variables that belong to the class |
required | |
hidden_states |
Array
|
chex.Array: Pass the hidden states of the previous layer |
required |
freq_cis |
Tuple[Array, Array]
|
Tuple[chex.Array, chex.Array],: Pass in the frequency coefficients for each position |
required |
attention_mask |
Array
|
chex.Array: Mask out certain tokens in the input sequence |
required |
position_ids |
Array
|
chex.Array: Determine the position of each token in a sequence |
required |
causal_mask |
Array
|
chex.Array: Mask out the future tokens in the decoder |
required |
deterministic |
bool
|
bool: Determine whether to use dropout or not |
True
|
init_cache |
bool
|
bool: Initialize the cache |
False
|
output_attentions |
bool
|
bool: Determine whether to return the attention weights or not |
False
|
fcm_mask |
Mask out the attention weights between the input and output tokens |
None
|
|
|
Determine if the attention is causal or not |
required |
Returns:
Type | Description |
---|---|
A tuple of two arrays |
Source code in src/python/easydel/modules/dbrx/modelling_dbrx_flax.py
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apply_rotary(batch_size, sequence_length, query, key, value, freq_cis, position_ids)
The apply_rotary function is a modified version of the apply_attention function in the BertModel class. The main difference is that it takes in an additional argument, freq_cis, which are used to calculate the rotary attention weights. The other differences are minor and mostly related to reshaping tensors.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
self |
Access variables that belong to the class |
required | |
batch_size |
Reshape the query, key and value tensors |
required | |
sequence_length |
Reshape the query, key and value tensors |
required | |
query |
Calculate the attention weights |
required | |
key |
Calculate the attention |
required | |
value |
Compute the attention weights |
required | |
freq_cis |
Calculate the frequency of each word in the vocabulary |
required | |
position_ids |
Identify the position of each token in the sequence |
required |
Returns:
Type | Description |
---|---|
A tuple of 3 tensors: query, key and value |
Source code in src/python/easydel/modules/dbrx/modelling_dbrx_flax.py
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FlaxDbrxForCausalLM
Bases: DbrxPreTrainedModel
Source code in src/python/easydel/modules/dbrx/modelling_dbrx_flax.py
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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/dbrx/modelling_dbrx_flax.py
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