# coding=utf-8 # Copyright 2021 The Facebook, Inc. and The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Flax BlenderbotSmall model.""" import math import random from functools import partial from typing import Callable, Optional, Tuple import flax.linen as nn import jax import jax.numpy as jnp from flax.core.frozen_dict import FrozenDict, freeze, unfreeze from flax.linen import combine_masks, make_causal_mask from flax.linen.attention import dot_product_attention_weights from flax.traverse_util import flatten_dict, unflatten_dict from jax import lax from jax.random import PRNGKey from ...modeling_flax_outputs import ( FlaxBaseModelOutput, FlaxBaseModelOutputWithPastAndCrossAttentions, FlaxCausalLMOutputWithCrossAttentions, FlaxSeq2SeqLMOutput, FlaxSeq2SeqModelOutput, ) from ...modeling_flax_utils import ( ACT2FN, FlaxPreTrainedModel, append_call_sample_docstring, append_replace_return_docstrings, overwrite_call_docstring, ) from ...utils import add_start_docstrings, logging, replace_return_docstrings from .configuration_blenderbot_small import BlenderbotSmallConfig logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "facebook/blenderbot_small-90M" _CONFIG_FOR_DOC = "BlenderbotSmallConfig" BLENDERBOT_SMALL_START_DOCSTRING = r""" This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a Flax Linen [flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior. Finally, this model supports inherent JAX features such as: - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit) - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation) - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap) - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap) Parameters: config ([`BlenderbotSmallConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights. dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`): The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and `jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`. **Note that this only specifies the dtype of the computation and does not influence the dtype of model parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and [`~FlaxPreTrainedModel.to_bf16`]. """ BLENDERBOT_SMALL_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ BLENDERBOT_SMALL_ENCODE_INPUTS_DOCSTRING = r""" Args: input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING = r""" Args: decoder_input_ids (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) For translation and summarization training, `decoder_input_ids` should be provided. If no `decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to the right for denoising pre-training following the paper. encoder_outputs (`tuple(tuple(jnp.ndarray)`): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`jnp.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_attention_mask (`jnp.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. past_key_values (`Dict[str, np.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`): Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.bart.modeling_flax_bart.shift_tokens_right def shift_tokens_right(input_ids: jnp.array, pad_token_id: int, decoder_start_token_id: int) -> jnp.ndarray: """ Shift input ids one token to the right. """ shifted_input_ids = jnp.zeros_like(input_ids) shifted_input_ids = shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]) shifted_input_ids = shifted_input_ids.at[:, 0].set(decoder_start_token_id) shifted_input_ids = jnp.where(shifted_input_ids == -100, pad_token_id, shifted_input_ids) return shifted_input_ids # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention with Bart->BlenderbotSmall class FlaxBlenderbotSmallAttention(nn.Module): config: BlenderbotSmallConfig embed_dim: int num_heads: int dropout: float = 0.0 causal: bool = False bias: bool = True dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self) -> None: self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}" f" and `num_heads`: {self.num_heads})." ) dense = partial( nn.Dense, self.embed_dim, use_bias=self.bias, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.q_proj, self.k_proj, self.v_proj = dense(), dense(), dense() self.out_proj = dense() self.dropout_layer = nn.Dropout(rate=self.dropout) if self.causal: self.causal_mask = make_causal_mask( jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool" ) def _split_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.num_heads, self.head_dim)) def _merge_heads(self, hidden_states): return hidden_states.reshape(hidden_states.shape[:2] + (self.embed_dim,)) @nn.compact def _concatenate_to_cache(self, key, value, query, attention_mask): """ This function takes projected key, value states from a single input token and concatenates the states to cached states from previous steps. This function is slighly adapted from the official Flax repository: https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252 """ # detect if we're initializing by absence of existing cache data. is_initialized = self.has_variable("cache", "cached_key") cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype) cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype) cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32)) if is_initialized: *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape # update key, value caches with our new 1d spatial slices cur_index = cache_index.value indices = (0,) * len(batch_dims) + (cur_index, 0, 0) key = lax.dynamic_update_slice(cached_key.value, key, indices) value = lax.dynamic_update_slice(cached_value.value, value, indices) cached_key.value = key cached_value.value = value num_updated_cache_vectors = query.shape[1] cache_index.value = cache_index.value + num_updated_cache_vectors # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements. pad_mask = jnp.broadcast_to( jnp.arange(max_length) < cur_index + num_updated_cache_vectors, tuple(batch_dims) + (1, num_updated_cache_vectors, max_length), ) attention_mask = combine_masks(pad_mask, attention_mask) return key, value, attention_mask def __call__( self, hidden_states: jnp.ndarray, key_value_states: Optional[jnp.ndarray] = None, attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: """Input shape: Batch x Time x Channel""" # if key_value_states are provided this layer is used as a cross-attention layer # for the decoder is_cross_attention = key_value_states is not None batch_size = hidden_states.shape[0] # get query proj query_states = self.q_proj(hidden_states) # get key, value proj if is_cross_attention: # cross_attentions key_states = self.k_proj(key_value_states) value_states = self.v_proj(key_value_states) else: # self_attention key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = self._split_heads(query_states) key_states = self._split_heads(key_states) value_states = self._split_heads(value_states) # handle cache prepare causal attention mask if self.causal: query_length, key_length = query_states.shape[1], key_states.shape[1] if self.has_variable("cache", "cached_key"): mask_shift = self.variables["cache"]["cache_index"] max_decoder_length = self.variables["cache"]["cached_key"].shape[1] causal_mask = lax.dynamic_slice( self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length) ) else: causal_mask = self.causal_mask[:, :, :query_length, :key_length] causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:]) # combine masks if needed if attention_mask is not None and self.causal: attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape) attention_mask = combine_masks(attention_mask, causal_mask) elif self.causal: attention_mask = causal_mask elif attention_mask is not None: attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2)) # During fast autoregressive decoding, we feed one position at a time, # and cache the keys and values step by step. if self.causal and (self.has_variable("cache", "cached_key") or init_cache): key_states, value_states, attention_mask = self._concatenate_to_cache( key_states, value_states, query_states, attention_mask ) # Convert the boolean attention mask to an attention bias. if attention_mask is not None: # attention mask in the form of attention bias attention_bias = lax.select( attention_mask > 0, jnp.full(attention_mask.shape, 0.0).astype(self.dtype), jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype), ) else: attention_bias = None dropout_rng = None if not deterministic and self.dropout > 0.0: dropout_rng = self.make_rng("dropout") attn_weights = dot_product_attention_weights( query_states, key_states, bias=attention_bias, dropout_rng=dropout_rng, dropout_rate=self.dropout, broadcast_dropout=True, deterministic=deterministic, dtype=self.dtype, precision=None, ) attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states) attn_output = self._merge_heads(attn_output) attn_output = self.out_proj(attn_output) return attn_output, attn_weights # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayer with Bart->BlenderbotSmall class FlaxBlenderbotSmallEncoderLayer(nn.Module): config: BlenderbotSmallConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxBlenderbotSmallAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.encoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.fc1 = nn.Dense( self.config.encoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states hidden_states, attn_weights = self.self_attn(hidden_states=hidden_states, attention_mask=attention_mask) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartEncoderLayerCollection with Bart->BlenderbotSmall class FlaxBlenderbotSmallEncoderLayerCollection(nn.Module): config: BlenderbotSmallConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxBlenderbotSmallEncoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.encoder_layers) ] self.layerdrop = self.config.encoder_layerdrop def __call__( self, hidden_states, attention_mask, deterministic: bool = True, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): all_attentions = () if output_attentions else None all_hidden_states = () if output_hidden_states else None for encoder_layer in self.layers: if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): # skip the layer layer_outputs = (None, None) else: layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions, deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: all_hidden_states += (hidden_states,) outputs = (hidden_states, all_hidden_states, all_attentions) if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutput( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayer with Bart->BlenderbotSmall class FlaxBlenderbotSmallDecoderLayer(nn.Module): config: BlenderbotSmallConfig dtype: jnp.dtype = jnp.float32 def setup(self) -> None: self.embed_dim = self.config.d_model self.self_attn = FlaxBlenderbotSmallAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, causal=True, dtype=self.dtype, ) self.dropout_layer = nn.Dropout(rate=self.config.dropout) self.activation_fn = ACT2FN[self.config.activation_function] self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout) self.self_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.encoder_attn = FlaxBlenderbotSmallAttention( config=self.config, embed_dim=self.embed_dim, num_heads=self.config.decoder_attention_heads, dropout=self.config.attention_dropout, dtype=self.dtype, ) self.encoder_attn_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) self.fc1 = nn.Dense( self.config.decoder_ffn_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.fc2 = nn.Dense( self.embed_dim, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std) ) self.final_layer_norm = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, hidden_states: jnp.ndarray, attention_mask: jnp.ndarray, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = True, deterministic: bool = True, ) -> Tuple[jnp.ndarray]: residual = hidden_states # Self Attention hidden_states, self_attn_weights = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, init_cache=init_cache ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.self_attn_layer_norm(hidden_states) # Cross-Attention Block cross_attn_weights = None if encoder_hidden_states is not None: residual = hidden_states hidden_states, cross_attn_weights = self.encoder_attn( hidden_states=hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask, ) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.encoder_attn_layer_norm(hidden_states) # Fully Connected residual = hidden_states hidden_states = self.activation_fn(self.fc1(hidden_states)) hidden_states = self.activation_dropout_layer(hidden_states, deterministic=deterministic) hidden_states = self.fc2(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) hidden_states = residual + hidden_states hidden_states = self.final_layer_norm(hidden_states) outputs = (hidden_states,) if output_attentions: outputs += (self_attn_weights, cross_attn_weights) return outputs # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderLayerCollection with Bart->BlenderbotSmall class FlaxBlenderbotSmallDecoderLayerCollection(nn.Module): config: BlenderbotSmallConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.layers = [ FlaxBlenderbotSmallDecoderLayer(self.config, name=str(i), dtype=self.dtype) for i in range(self.config.decoder_layers) ] self.layerdrop = self.config.decoder_layerdrop def __call__( self, hidden_states, attention_mask, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, deterministic: bool = True, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, ): # decoder layers all_hidden_states = () if output_hidden_states else None all_self_attns = () if output_attentions else None all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None for decoder_layer in self.layers: if output_hidden_states: all_hidden_states += (hidden_states,) # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) dropout_probability = random.uniform(0, 1) if not deterministic and (dropout_probability < self.layerdrop): layer_outputs = (None, None, None) else: layer_outputs = decoder_layer( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, init_cache=init_cache, output_attentions=output_attentions, deterministic=deterministic, ) hidden_states = layer_outputs[0] if output_attentions: all_self_attns += (layer_outputs[1],) if encoder_hidden_states is not None: all_cross_attentions += (layer_outputs[2],) # add hidden states from the last decoder layer if output_hidden_states: all_hidden_states += (hidden_states,) outputs = [hidden_states, all_hidden_states, all_self_attns, all_cross_attentions] if not return_dict: return tuple(v for v in outputs if v is not None) return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attns, cross_attentions=all_cross_attentions, ) class FlaxBlenderbotSmallEncoder(nn.Module): config: BlenderbotSmallConfig embed_tokens: nn.Embed dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_source_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0 self.embed_positions = nn.Embed( self.config.max_position_embeddings, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = FlaxBlenderbotSmallEncoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale embed_pos = self.embed_positions(position_ids) hidden_states = inputs_embeds + embed_pos hidden_states = self.layernorm_embedding(hidden_states) hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, deterministic=deterministic, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs return FlaxBaseModelOutput( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class FlaxBlenderbotSmallDecoder(nn.Module): config: BlenderbotSmallConfig embed_tokens: nn.Embed dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.dropout_layer = nn.Dropout(rate=self.config.dropout) embed_dim = self.config.d_model self.padding_idx = self.config.pad_token_id self.max_target_positions = self.config.max_position_embeddings self.embed_scale = math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0 self.embed_positions = nn.Embed( self.config.max_position_embeddings, embed_dim, embedding_init=jax.nn.initializers.normal(self.config.init_std), ) self.layers = FlaxBlenderbotSmallDecoderLayerCollection(self.config, self.dtype) self.layernorm_embedding = nn.LayerNorm(dtype=self.dtype, epsilon=1e-05) def __call__( self, input_ids, attention_mask, position_ids, encoder_hidden_states: Optional[jnp.ndarray] = None, encoder_attention_mask: Optional[jnp.ndarray] = None, init_cache: bool = False, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): input_shape = input_ids.shape input_ids = input_ids.reshape(-1, input_shape[-1]) inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale # embed positions positions = self.embed_positions(position_ids) # BlenderbotSmall applies layer norm on inputs_embeds in decoder inputs_embeds = self.layernorm_embedding(inputs_embeds) hidden_states = inputs_embeds + positions hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic) outputs = self.layers( hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, deterministic=deterministic, init_cache=init_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) if not return_dict: return outputs return FlaxBaseModelOutputWithPastAndCrossAttentions( last_hidden_state=outputs.last_hidden_state, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartModule with Bart->BlenderbotSmall class FlaxBlenderbotSmallModule(nn.Module): config: BlenderbotSmallConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation def setup(self): self.shared = nn.Embed( self.config.vocab_size, self.config.d_model, embedding_init=jax.nn.initializers.normal(self.config.init_std), dtype=self.dtype, ) self.encoder = FlaxBlenderbotSmallEncoder(self.config, dtype=self.dtype, embed_tokens=self.shared) self.decoder = FlaxBlenderbotSmallDecoder(self.config, dtype=self.dtype, embed_tokens=self.shared) def _get_encoder_module(self): return self.encoder def _get_decoder_module(self): return self.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): encoder_outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) decoder_outputs = self.decoder( input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], encoder_attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) if not return_dict: return decoder_outputs + encoder_outputs return FlaxSeq2SeqModelOutput( last_hidden_state=decoder_outputs.last_hidden_state, decoder_hidden_states=decoder_outputs.hidden_states, decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state, encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions, ) class FlaxBlenderbotSmallPreTrainedModel(FlaxPreTrainedModel): config_class = BlenderbotSmallConfig base_model_prefix: str = "model" module_class: nn.Module = None def __init__( self, config: BlenderbotSmallConfig, input_shape: Tuple[int] = (1, 1), seed: int = 0, dtype: jnp.dtype = jnp.float32, _do_init: bool = True, **kwargs, ): module = self.module_class(config=config, dtype=dtype, **kwargs) super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init) def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict: # init input tensors input_ids = jnp.zeros(input_shape, dtype="i4") # make sure initialization pass will work for FlaxBlenderbotSmallForSequenceClassificationModule input_ids = input_ids.at[(..., -1)].set(self.config.eos_token_id) attention_mask = jnp.ones_like(input_ids) decoder_input_ids = input_ids decoder_attention_mask = jnp.ones_like(input_ids) batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) params_rng, dropout_rng = jax.random.split(rng) rngs = {"params": params_rng, "dropout": dropout_rng} random_params = self.module.init( rngs, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, )["params"] 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, encoder_outputs): r""" Args: batch_size (`int`): batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache. max_length (`int`): maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized cache. encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`): `encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. """ # init input variables to retrieve cache decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4") decoder_attention_mask = jnp.ones_like(decoder_input_ids) decoder_position_ids = jnp.broadcast_to( jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]), decoder_input_ids.shape ) def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) init_variables = self.module.init( jax.random.PRNGKey(0), decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, decoder_position_ids=decoder_position_ids, encoder_hidden_states=encoder_outputs[0], init_cache=True, method=_decoder_forward, # we only need to call the decoder to init the cache ) return unfreeze(init_variables["cache"]) @add_start_docstrings(BLENDERBOT_SMALL_ENCODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=BlenderbotSmallConfig) def encode( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="np") >>> encoder_outputs = model.encode(**inputs) ```""" 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 if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng def _encoder_forward(module, input_ids, attention_mask, position_ids, **kwargs): encode_module = module._get_encoder_module() return encode_module(input_ids, attention_mask, position_ids, **kwargs) return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, method=_encoder_forward, ) @add_start_docstrings(BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings( output_type=FlaxBaseModelOutputWithPastAndCrossAttentions, config_class=BlenderbotSmallConfig ) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="np") >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> last_decoder_hidden_states = outputs.last_hidden_state ```""" 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 encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxBlenderbotSmallAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() return decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, mutable=mutable, method=_decoder_forward, ) # add updated cache to model output if past_key_values is not None and return_dict: outputs, past = outputs outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs, past = outputs outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def __call__( self, input_ids: jnp.ndarray, attention_mask: Optional[jnp.ndarray] = None, decoder_input_ids: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, position_ids: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, train: bool = False, params: dict = None, dropout_rng: PRNGKey = None, ): 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 # prepare encoder inputs if attention_mask is None: attention_mask = jnp.ones_like(input_ids) if position_ids is None: batch_size, sequence_length = input_ids.shape position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)) # prepare decoder inputs if decoder_input_ids is None: decoder_input_ids = shift_tokens_right( input_ids, self.config.pad_token_id, decoder_start_token_id=self.config.decoder_start_token_id ) if decoder_attention_mask is None: decoder_attention_mask = jnp.ones_like(decoder_input_ids) if decoder_position_ids is None: batch_size, sequence_length = decoder_input_ids.shape decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {"dropout": dropout_rng} if dropout_rng is not None else {} return self.module.apply( {"params": params or self.params}, input_ids=jnp.array(input_ids, dtype="i4"), attention_mask=jnp.array(attention_mask, dtype="i4"), position_ids=jnp.array(position_ids, dtype="i4"), decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=not train, rngs=rngs, ) @add_start_docstrings( "The bare BlenderbotSmall Model transformer outputting raw hidden-states without any specific head on top.", BLENDERBOT_SMALL_START_DOCSTRING, ) class FlaxBlenderbotSmallModel(FlaxBlenderbotSmallPreTrainedModel): config: BlenderbotSmallConfig dtype: jnp.dtype = jnp.float32 # the dtype of the computation module_class = FlaxBlenderbotSmallModule append_call_sample_docstring(FlaxBlenderbotSmallModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC) # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartForConditionalGenerationModule with Bart->BlenderbotSmall class FlaxBlenderbotSmallForConditionalGenerationModule(nn.Module): config: BlenderbotSmallConfig dtype: jnp.dtype = jnp.float32 bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros def setup(self): self.model = FlaxBlenderbotSmallModule(config=self.config, dtype=self.dtype) self.lm_head = nn.Dense( self.model.shared.num_embeddings, use_bias=False, dtype=self.dtype, kernel_init=jax.nn.initializers.normal(self.config.init_std), ) self.final_logits_bias = self.param("final_logits_bias", self.bias_init, (1, self.model.shared.num_embeddings)) def _get_encoder_module(self): return self.model.encoder def _get_decoder_module(self): return self.model.decoder def __call__( self, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask, position_ids, decoder_position_ids, output_attentions: bool = False, output_hidden_states: bool = False, return_dict: bool = True, deterministic: bool = True, ): outputs = self.model( input_ids=input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, decoder_attention_mask=decoder_attention_mask, position_ids=position_ids, decoder_position_ids=decoder_position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = self.model.variables["params"]["shared"]["embedding"] lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = self.lm_head(hidden_states) lm_logits += jax.lax.stop_gradient(self.final_logits_bias.astype(self.dtype)) if not return_dict: output = (lm_logits,) + outputs[1:] return output return FlaxSeq2SeqLMOutput( logits=lm_logits, decoder_hidden_states=outputs.decoder_hidden_states, decoder_attentions=outputs.decoder_attentions, cross_attentions=outputs.cross_attentions, encoder_last_hidden_state=outputs.encoder_last_hidden_state, encoder_hidden_states=outputs.encoder_hidden_states, encoder_attentions=outputs.encoder_attentions, ) @add_start_docstrings( "The BLENDERBOT_SMALL Model with a language modeling head. Can be used for summarization.", BLENDERBOT_SMALL_START_DOCSTRING, ) class FlaxBlenderbotSmallForConditionalGeneration(FlaxBlenderbotSmallPreTrainedModel): module_class = FlaxBlenderbotSmallForConditionalGenerationModule dtype: jnp.dtype = jnp.float32 @add_start_docstrings(BLENDERBOT_SMALL_DECODE_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=BlenderbotSmallConfig) def decode( self, decoder_input_ids, encoder_outputs, encoder_attention_mask: Optional[jnp.ndarray] = None, decoder_attention_mask: Optional[jnp.ndarray] = None, decoder_position_ids: Optional[jnp.ndarray] = None, past_key_values: dict = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, deterministic: bool = True, params: dict = None, dropout_rng: PRNGKey = None, ): r""" Returns: Example: ```python >>> import jax.numpy as jnp >>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") >>> text = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer(text, max_length=1024, return_tensors="np") >>> encoder_outputs = model.encode(**inputs) >>> decoder_start_token_id = model.config.decoder_start_token_id >>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id >>> outputs = model.decode(decoder_input_ids, encoder_outputs) >>> logits = outputs.logits ```""" 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 encoder_hidden_states = encoder_outputs[0] if encoder_attention_mask is None: batch_size, sequence_length = encoder_hidden_states.shape[:2] encoder_attention_mask = jnp.ones((batch_size, sequence_length)) batch_size, sequence_length = decoder_input_ids.shape if decoder_attention_mask is None: decoder_attention_mask = jnp.ones((batch_size, sequence_length)) if decoder_position_ids is None: if past_key_values is not None: raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.") decoder_position_ids = jnp.broadcast_to( jnp.arange(sequence_length)[None, :], (batch_size, sequence_length) ) # Handle any PRNG if needed rngs = {} if dropout_rng is not None: rngs["dropout"] = dropout_rng inputs = {"params": params or self.params} # if past_key_values are passed then cache is already initialized a private flag init_cache has to be # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that # it can be changed by FlaxBlenderbotSmallAttention module if past_key_values: inputs["cache"] = past_key_values mutable = ["cache"] else: mutable = False def _decoder_forward(module, decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs): decoder_module = module._get_decoder_module() outputs = decoder_module( decoder_input_ids, decoder_attention_mask, decoder_position_ids, **kwargs, ) hidden_states = outputs[0] if self.config.tie_word_embeddings: shared_embedding = module.model.variables["params"]["shared"]["embedding"] lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states) else: lm_logits = module.lm_head(hidden_states) lm_logits += module.final_logits_bias.astype(self.dtype) return lm_logits, outputs outputs = self.module.apply( inputs, decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"), decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"), decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"), encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"), output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, deterministic=deterministic, rngs=rngs, mutable=mutable, method=_decoder_forward, ) if past_key_values is None: lm_logits, decoder_outputs = outputs else: (lm_logits, decoder_outputs), past = outputs if return_dict: outputs = FlaxCausalLMOutputWithCrossAttentions( logits=lm_logits, hidden_states=decoder_outputs.hidden_states, attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, ) else: outputs = (lm_logits,) + decoder_outputs[1:] # add updated cache to model output if past_key_values is not None and return_dict: outputs["past_key_values"] = unfreeze(past["cache"]) return outputs elif past_key_values is not None and not return_dict: outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:] return outputs def prepare_inputs_for_generation( self, decoder_input_ids, max_length, attention_mask: Optional[jax.Array] = None, decoder_attention_mask: Optional[jax.Array] = None, encoder_outputs=None, **kwargs, ): # initializing the cache batch_size, seq_length = decoder_input_ids.shape past_key_values = self.init_cache(batch_size, max_length, encoder_outputs) # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length. # But since the decoder uses a causal mask, those positions are masked anyways. # Thus we can create a single static attention_mask here, which is more efficient for compilation extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4") if decoder_attention_mask is not None: position_ids = decoder_attention_mask.cumsum(axis=-1) - 1 extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_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, "encoder_outputs": encoder_outputs, "encoder_attention_mask": attention_mask, "decoder_attention_mask": extended_attention_mask, "decoder_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["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1 return model_kwargs FLAX_BLENDERBOT_SMALL_CONDITIONAL_GENERATION_DOCSTRING = """ Returns: Summarization example: ```py >>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") >>> ARTICLE_TO_SUMMARIZE = "My friends are cool but they eat too many carbs." >>> inputs = tokenizer([ARTICLE_TO_SUMMARIZE], max_length=1024, return_tensors="np") >>> # Generate Summary >>> summary_ids = model.generate(inputs["input_ids"]).sequences >>> print(tokenizer.batch_decode(summary_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)) ``` Mask filling example: ```py >>> from transformers import AutoTokenizer, FlaxBlenderbotSmallForConditionalGeneration >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M") >>> TXT = "My friends are but they eat too many carbs." >>> model = FlaxBlenderbotSmallForConditionalGeneration.from_pretrained("facebook/blenderbot_small-90M") >>> input_ids = tokenizer([TXT], return_tensors="np")["input_ids"] >>> logits = model(input_ids).logits >>> masked_index = (input_ids[0] == tokenizer.mask_token_id).nonzero().item() >>> probs = jax.nn.softmax(logits[0, masked_index], axis=0) >>> values, predictions = jax.lax.top_k(probs) >>> tokenizer.decode(predictions).split() ``` """ overwrite_call_docstring( FlaxBlenderbotSmallForConditionalGeneration, BLENDERBOT_SMALL_INPUTS_DOCSTRING + FLAX_BLENDERBOT_SMALL_CONDITIONAL_GENERATION_DOCSTRING, ) append_replace_return_docstrings( FlaxBlenderbotSmallForConditionalGeneration, output_type=FlaxSeq2SeqLMOutput, config_class=_CONFIG_FOR_DOC )