Witllm/seqgpt/modeling_flax_bloom.py

# coding=utf-8
# Copyright 2023 HuggingFace Inc. Team and Bigscience Workshop. 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 BLOOM model."""

import math
from functools import partial
from typing import 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, dot_product_attention_weights, make_causal_mask
from flax.linen.activation import tanh
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax

from ...modeling_flax_outputs import (
    FlaxBaseModelOutput,
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxCausalLMOutput,
)
from ...modeling_flax_utils import FlaxPreTrainedModel, append_call_sample_docstring
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_bloom import BloomConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "bigscience/bloom"
_CONFIG_FOR_DOC = "BloomConfig"


BLOOM_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 ([`BloomConfig`]): 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`].
"""

BLOOM_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `(batch_size, input_ids_length)`):
            `input_ids_length` = `sequence_length`. Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`BloomTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.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)
        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.
"""


def build_alibi_tensor(attention_mask: jnp.ndarray, num_heads: int, dtype: Optional[jnp.dtype] = jnp.float32):
    """
    Flax implementation of the BLOOM Alibi tensor. BLOOM Alibi tensor is not causal as the original paper mentions, it
    relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value
    `softmax(l+a) = softmax(l)`. Based on
    https://github.com/ofirpress/attention_with_linear_biases/blob/a35aaca144e0eb6b789dfcb46784c4b8e31b7983/fairseq/models/transformer.py#L742
    Link to paper: https://arxiv.org/abs/2108.12409

    Args:
        attention_mask (`jnp.ndarray`):
            Token-wise attention mask, this should be of shape `(batch_size, max_seq_len)`.
        num_heads (`int`):
            Number of attention heads.
        dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
            The data type (dtype) of the output tensor.

    Returns: Alibi tensor of shape `(batch_size * num_heads, 1, max_seq_len)`.
    """
    batch_size, seq_length = attention_mask.shape
    closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
    base = jnp.array(2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), dtype=jnp.float32)
    powers = jnp.arange(1, 1 + closest_power_of_2, dtype=jnp.float32)
    slopes = jax.lax.pow(base, powers)

    if closest_power_of_2 != num_heads:
        extra_base = jnp.array(2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), dtype=jnp.float32)
        num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
        extra_powers = jnp.arange(1, 1 + 2 * num_remaining_heads, 2, dtype=jnp.float32)
        slopes = jnp.cat([slopes, jax.lax.pow(extra_base, extra_powers)], axis=0)

    # Note: the Alibi tensor will added to the attention bias that will be applied to the query, key product of attention
    # therefore, Alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
    # => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
    # so that the query_length dimension will then be broadcast correctly.
    # This is more or less identical to T5's relative position bias:
    # https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
    arange_tensor = ((attention_mask.cumsum(axis=-1) - 1) * attention_mask)[:, None, :]
    alibi = slopes[..., None] * arange_tensor
    alibi = jnp.expand_dims(alibi, axis=2)
    return jnp.asarray(alibi, dtype)


class FlaxBloomAttention(nn.Module):
    config: BloomConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.hidden_size = self.config.hidden_size
        self.num_heads = self.config.n_head
        self.head_dim = self.hidden_size // self.num_heads
        self.attention_softmax_in_fp32 = self.dtype is not jnp.float32

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

        dense = partial(
            nn.Dense,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

        self.query_key_value = dense(self.hidden_size * 3)
        self.dense = dense(self.hidden_size)
        self.resid_dropout = nn.Dropout(rate=self.config.hidden_dropout)

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:-1] + (self.num_heads, self.head_dim * 3))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.hidden_size,))

    @nn.compact
    # Copied from transformers.models.gptj.modeling_flax_gptj.FlaxGPTJAttention._concatenate_to_cache
    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,
        residual,
        alibi,
        attention_mask=None,
        deterministic: bool = True,
        init_cache: bool = False,
        output_attentions: bool = False,
    ):
        batch_size, seq_length = hidden_states.shape[:2]

        # proj q, k, v
        fused_qkv = self.query_key_value(hidden_states)
        fused_qkv = self._split_heads(fused_qkv)
        query, key, value = jnp.split(fused_qkv, 3, axis=-1)

        causal_attention_mask = make_causal_mask(attention_mask, dtype="bool")

        # for fast decoding causal attention mask should be shifted
        causal_attention_mask_shift = (
            self.variables["cache"]["cache_index"] if self.has_variable("cache", "cached_key") else 0
        )

        # fast decoding for generate requires special attention_mask
        if self.has_variable("cache", "cached_key"):
            max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
            causal_attention_mask = jax.lax.dynamic_slice(
                causal_attention_mask,
                (0, 0, causal_attention_mask_shift, 0),
                (1, 1, seq_length, max_decoder_length),
            )

        # broadcast causal attention mask & attention mask to fit for merge
        causal_attention_mask = jnp.broadcast_to(
            causal_attention_mask, (batch_size,) + causal_attention_mask.shape[1:]
        )
        attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_attention_mask.shape)
        attention_mask = combine_masks(attention_mask, causal_attention_mask)

        dropout_rng = None
        if not deterministic and self.config.attention_dropout > 0.0:
            dropout_rng = self.make_rng("dropout")

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.has_variable("cache", "cached_key") or init_cache:
            key, value, attention_mask = self._concatenate_to_cache(key, value, query, attention_mask)

        # transform boolean mask into float mask
        mask_value = jnp.finfo(self.dtype).min
        attention_bias = lax.select(
            attention_mask > 0,
            jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
            jnp.full(attention_mask.shape, mask_value).astype(self.dtype),
        )

        attention_bias = attention_bias + alibi

        # Cast in fp32 if the original dtype is different from fp32
        attention_dtype = jnp.float32 if self.attention_softmax_in_fp32 else self.dtype

        attn_weights = dot_product_attention_weights(
            query,
            key,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.config.attention_dropout,
            deterministic=deterministic,
            dtype=attention_dtype,
        )

        # Cast back in the original dtype if the native dtype is not fp32
        if self.attention_softmax_in_fp32:
            attn_weights = attn_weights.astype(self.dtype)

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value)
        attn_output = self._merge_heads(attn_output)
        attn_output = self.dense(attn_output)
        attn_output = self.resid_dropout(attn_output, deterministic=deterministic)

        attn_output = attn_output + residual

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs


class BloomGELU(nn.Module):
    def setup(self):
        self.dtype = jnp.float32

    def __call__(self, x):
        return x * 0.5 * (1.0 + tanh(0.79788456 * x * (1 + 0.044715 * x * x)))


class FlaxBloomMLP(nn.Module):
    config: BloomConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        hidden_size = self.config.hidden_size

        kernel_init = jax.nn.initializers.normal(self.config.initializer_range)

        self.dense_h_to_4h = nn.Dense(4 * hidden_size, dtype=self.dtype, kernel_init=kernel_init)
        self.dense_4h_to_h = nn.Dense(hidden_size, dtype=self.dtype, kernel_init=kernel_init)
        self.hidden_dropout = nn.Dropout(self.config.hidden_dropout)
        self.act = BloomGELU()

    def __call__(self, hidden_states, residual, deterministic: bool = True):
        hidden_states = self.dense_h_to_4h(hidden_states)
        hidden_states = self.act(hidden_states)

        intermediate_output = self.dense_4h_to_h(hidden_states)

        intermediate_output = intermediate_output + residual
        hidden_states = self.hidden_dropout(intermediate_output, deterministic=deterministic)

        return hidden_states


class FlaxBloomBlock(nn.Module):
    config: BloomConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.input_layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)

        self.self_attention = FlaxBloomAttention(self.config, dtype=self.dtype)
        self.post_attention_layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)

        self.mlp = FlaxBloomMLP(self.config, dtype=self.dtype)

        self.apply_residual_connection_post_layernorm = self.config.apply_residual_connection_post_layernorm
        self.hidden_dropout = self.config.hidden_dropout

    def __call__(
        self,
        hidden_states,
        alibi,
        attention_mask=None,
        deterministic: bool = True,
        init_cache: bool = False,
        output_attentions: bool = False,
    ):
        layernorm_output = self.input_layernorm(hidden_states)

        # layer norm before saving residual if config calls for it
        if self.apply_residual_connection_post_layernorm:
            residual = layernorm_output
        else:
            residual = hidden_states

        # self-attention
        attn_outputs = self.self_attention(
            layernorm_output,
            residual=residual,
            alibi=alibi,
            attention_mask=attention_mask,
            deterministic=deterministic,
            init_cache=init_cache,
            output_attentions=output_attentions,
        )

        attention_output = attn_outputs[0]

        outputs = attn_outputs[1:]

        post_layernorm = self.post_attention_layernorm(attention_output)

        # set residual based on config
        if self.apply_residual_connection_post_layernorm:
            residual = post_layernorm
        else:
            residual = attention_output

        output = self.mlp(post_layernorm, residual, deterministic=deterministic)

        outputs = (output,) + outputs

        return outputs


class FlaxBloomPreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BloomConfig
    base_model_prefix = "transformer"
    module_class: nn.Module = None

    def __init__(
        self,
        config: BloomConfig,
        input_shape: Tuple = (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")
        attention_mask = jnp.ones_like(input_ids)
        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, return_dict=False)["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):
        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.
        """
        # init input variables to retrieve cache
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids)

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

    @add_start_docstrings_to_model_forward(BLOOM_INPUTS_DOCSTRING)
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        past_key_values: dict = None,
        params: dict = None,
        dropout_rng: jax.random.PRNGKey = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = 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.use_return_dict

        batch_size, sequence_length = input_ids.shape

        if attention_mask is None:
            attention_mask = jnp.ones((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 FlaxBloomAttention module
        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"),
            jnp.array(attention_mask, dtype="i4"),
            not train,
            False,
            output_attentions,
            output_hidden_states,
            return_dict,
            rngs=rngs,
            mutable=mutable,
        )

        # add updated cache to model output
        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


class FlaxBloomBlockCollection(nn.Module):
    config: BloomConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.layers = [
            FlaxBloomBlock(self.config, name=str(layer_number), dtype=self.dtype)
            for layer_number in range(self.config.num_hidden_layers)
        ]

    def __call__(
        self,
        hidden_states,
        alibi,
        attention_mask=None,
        deterministic: bool = True,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        for layer_number in range(self.config.num_hidden_layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = self.layers[layer_number](
                hidden_states,
                alibi=alibi,
                attention_mask=attention_mask,
                deterministic=deterministic,
                init_cache=init_cache,
                output_attentions=output_attentions,
            )
            hidden_states = layer_outputs[0]

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

        # this contains possible `None` values - `FlaxBloomModule` will filter them out
        outputs = (hidden_states, all_hidden_states, all_attentions)

        return outputs


class FlaxBloomModule(nn.Module):
    config: BloomConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.embed_dim = self.config.hidden_size

        # word embeddings (no positional embedding layer)
        self.word_embeddings = nn.Embed(
            self.config.vocab_size,
            self.embed_dim,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )

        # post-embedding layernorm
        self.word_embeddings_layernorm = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)

        # transformer layers
        self.h = FlaxBloomBlockCollection(self.config, dtype=self.dtype)

        # final layernorm
        self.ln_f = nn.LayerNorm(epsilon=self.config.layer_norm_epsilon, dtype=self.dtype)

    def __call__(
        self,
        input_ids=None,
        attention_mask=None,
        deterministic=True,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        inputs_embeds = self.word_embeddings(input_ids)
        # do post-embedding layernorm
        hidden_states = self.word_embeddings_layernorm(inputs_embeds)

        # build alibi depending on `attention_mask`
        alibi = build_alibi_tensor(attention_mask, self.config.n_head, dtype=hidden_states.dtype)

        outputs = self.h(
            hidden_states,
            alibi=alibi,
            attention_mask=attention_mask,
            deterministic=deterministic,
            init_cache=init_cache,
            output_hidden_states=output_hidden_states,
            output_attentions=output_attentions,
        )

        hidden_states = outputs[0]
        hidden_states = self.ln_f(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(v for v in [outputs[0], outputs[-1]] if v is not None)

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


@add_start_docstrings(
    "The bare Bloom Model transformer outputting raw hidden-states without any specific head on top.",
    BLOOM_START_DOCSTRING,
)
# Copied from transformers.models.gpt_neo.modeling_flax_gpt_neo.FlaxGPTNeoModel with GPTNeo->Bloom
class FlaxBloomModel(FlaxBloomPreTrainedModel):
    module_class = FlaxBloomModule


append_call_sample_docstring(FlaxBloomModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutput, _CONFIG_FOR_DOC)


class FlaxBloomForCausalLMModule(nn.Module):
    config: BloomConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.transformer = FlaxBloomModule(self.config, dtype=self.dtype)
        self.lm_head = nn.Dense(
            self.config.vocab_size,
            use_bias=False,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
        )

    def __call__(
        self,
        input_ids,
        attention_mask,
        deterministic: bool = True,
        init_cache: bool = False,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        outputs = self.transformer(
            input_ids,
            attention_mask=attention_mask,
            deterministic=deterministic,
            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"]["word_embeddings"]["embedding"].T
            lm_logits = self.lm_head.apply({"params": {"kernel": shared_kernel}}, hidden_states)
        else:
            lm_logits = self.lm_head(hidden_states)

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

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


@add_start_docstrings(
    """
    The Bloom Model transformer with a language modeling head on top (linear layer with weights tied to the input
    embeddings).
    """,
    BLOOM_START_DOCSTRING,
)
class FlaxBloomForCausalLM(FlaxBloomPreTrainedModel):
    module_class = FlaxBloomForCausalLMModule

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
        # initializing the cache
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        # 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 Bloom uses a causal mask,
        # those positions are masked anyway. 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 attention_mask is not None:
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))

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

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


append_call_sample_docstring(FlaxBloomForCausalLM, _CHECKPOINT_FOR_DOC, FlaxCausalLMOutput, _CONFIG_FOR_DOC)