Upload FlaxTransformerLMForCausalLM

config.json

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+{
+  "architectures": [
+    "TransformerLMForCausalLM"
+  ],
+  "attention_dropout_rate": 0.1,
+  "auto_map": {
+    "AutoConfig": "configuration_transformerlm.TransformerLMConfig",
+    "FlaxAutoModelForCausalLM": "modeling_transformerlm_flax.FlaxTransformerLMForCausalLM"
+  },
+  "bos_token_id": 50256,
+  "decode": true,
+  "deterministic": true,
+  "dropout_rate": 0.1,
+  "emb_dim": 768,
+  "eos_token_id": 50256,
+  "logits_via_embedding": false,
+  "max_len": 512,
+  "mlp_dim": 3072,
+  "model_type": "transformerlm",
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_vocab_size": 30000,
+  "qkv_dim": 768,
+  "share_embeddings": false,
+  "tokenizer_class": "TransformerLMTokenizer",
+  "transformers_version": "4.38.2",
+  "vocab_size": 30000
+}

configuration_transformerlm.py

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+from transformers import PretrainedConfig
+
+
+class TransformerLMConfig(PretrainedConfig):
+  model_type = "transformerlm"
+
+  def __init__(
+      self,
+      vocab_size: int = 30000,
+      output_vocab_size: int = 30000,
+      share_embeddings: bool = False,
+      logits_via_embedding: bool = False,
+      emb_dim: int = 512,
+      num_heads: int = 8,
+      num_layers: int = 6,
+      qkv_dim: int = 512,
+      mlp_dim: int = 2048,
+      max_len: int = 2048,
+      dropout_rate: float = 0.1,
+      attention_dropout_rate: float = 0.1,
+      deterministic: bool = False,
+      decode: bool = False,
+      bos_token_id=50256,
+      eos_token_id=50256,
+      **kwargs,
+  ):
+    self.vocab_size = vocab_size
+    self.output_vocab_size = output_vocab_size
+    self.share_embeddings = share_embeddings
+    self.logits_via_embedding = logits_via_embedding
+    self.emb_dim = emb_dim
+    self.num_heads = num_heads
+    self.num_layers = num_layers
+    self.qkv_dim = qkv_dim
+    self.mlp_dim = mlp_dim
+    self.max_len = max_len
+    self.dropout_rate = dropout_rate
+    self.attention_dropout_rate = attention_dropout_rate
+    self.deterministic = deterministic
+    self.decode = decode
+    self.bos_token_id = bos_token_id
+    self.eos_token_id = eos_token_id
+    super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

flax_model.msgpack

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f146d3ff30cefcdecb23e15f175395be65b13de37821c4d78b32feb8415f3666
+size 524522413

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "transformers_version": "4.38.2"
+}

modeling_transformerlm_flax.py

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+# Copyright 2023 The Flax Authors.
+#
+# 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.
+
+from typing import Callable, Any, Optional, Tuple
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax import linen as nn
+from flax import struct
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import lax
+from transformers.modeling_flax_outputs import FlaxBaseModelOutput, FlaxCausalLMOutput
+from transformers.modeling_flax_utils import FlaxPreTrainedModel
+
+from .configuration_transformerlm import TransformerLMConfig
+
+
+@struct.dataclass
+class TransformerConfig:
+  """Global hyperparameters used to minimize obnoxious kwarg plumbing."""
+  vocab_size: int
+  output_vocab_size: int
+  share_embeddings: bool = False
+  logits_via_embedding: bool = False
+  dtype: Any = jnp.float32
+  emb_dim: int = 512
+  num_heads: int = 8
+  num_layers: int = 6
+  qkv_dim: int = 512
+  mlp_dim: int = 2048
+  max_len: int = 2048
+  dropout_rate: float = 0.1
+  attention_dropout_rate: float = 0.1
+  deterministic: bool = False
+  decode: bool = False
+  kernel_init: Callable = nn.initializers.xavier_uniform()
+  bias_init: Callable = nn.initializers.normal(stddev=1e-6)
+  posemb_init: Optional[Callable] = None
+
+
+def shift_right(x, axis=1):
+  """Shift the input to the right by padding and slicing on axis."""
+  pad_widths = [(0, 0)] * len(x.shape)
+  pad_widths[axis] = (1, 0)
+  padded = jnp.pad(
+    x, pad_widths, mode='constant', constant_values=x.dtype.type(0))
+  return lax.dynamic_slice_in_dim(padded, 0, padded.shape[axis] - 1, axis)
+
+
+def shift_inputs(x, segment_ids=None, axis=1):
+  """Shift inputs and replace EOS by 0 for packed inputs."""
+  shifted = shift_right(x, axis=axis)
+  # For packed targets, the first shifted token of a new sequence is made
+  # 0, rather than being the EOS token for the last sequence.
+  if segment_ids is not None:
+    shifted *= (segment_ids==shift_right(segment_ids, axis=axis))
+  return shifted
+
+
+def sinusoidal_init(max_len=2048,
+                    min_scale=1.0,
+                    max_scale=10000.0):
+  """1D Sinusoidal Position Embedding Initializer.
+
+  Args:
+      max_len: maximum possible length for the input.
+      min_scale: float: minimum frequency-scale in sine grating.
+      max_scale: float: maximum frequency-scale in sine grating.
+
+  Returns:
+      output: init function returning `(1, max_len, d_feature)`
+  """
+
+  def init(key, shape, dtype=np.float32):
+    """Sinusoidal init."""
+    del key, dtype
+    d_feature = shape[-1]
+    pe = np.zeros((max_len, d_feature), dtype=np.float32)
+    position = np.arange(0, max_len)[:, np.newaxis]
+    scale_factor = -np.log(max_scale / min_scale) / (d_feature // 2 - 1)
+    div_term = min_scale * np.exp(np.arange(0, d_feature // 2) * scale_factor)
+    pe[:, :d_feature // 2] = np.sin(position * div_term)
+    pe[:, d_feature // 2: 2 * (d_feature // 2)] = np.cos(position * div_term)
+    pe = pe[np.newaxis, :, :]  # [1, max_len, d_feature]
+    return jnp.array(pe)
+
+  return init
+
+
+class AddPositionEmbs(nn.Module):
+  """Adds (optionally learned) positional embeddings to the inputs.
+
+  Args:
+    config: TransformerConfig dataclass containing hyperparameters.
+    decode: whether to run in single-position autoregressive mode.
+  """
+  config: TransformerConfig
+  decode: bool = False
+
+  @nn.compact
+  def __call__(self,
+               inputs,
+               inputs_positions=None):
+    """Applies AddPositionEmbs module.
+
+    By default this layer uses a fixed sinusoidal embedding table. If a
+    learned position embedding is desired, pass an initializer to
+    posemb_init in the configuration.
+
+    Args:
+      inputs: input data.
+      inputs_positions: input position indices for packed sequences.
+
+    Returns:
+      output: `(bs, timesteps, in_dim)`
+    """
+    config = self.config
+    # inputs.shape is (batch_size, seq_len, emb_dim)
+    assert inputs.ndim==3, ('Number of dimensions should be 3,'
+                            ' but it is: %d' % inputs.ndim)
+    length = inputs.shape[1]
+    pos_emb_shape = (1, config.max_len, inputs.shape[-1])
+    if config.posemb_init is None:
+      # Use a fixed (non-learned) sinusoidal position embedding.
+      pos_embedding = sinusoidal_init(max_len=config.max_len)(None,
+        pos_emb_shape,
+        None)
+    else:
+      pos_embedding = self.param('pos_embedding', config.posemb_init,
+        pos_emb_shape)
+    pe = pos_embedding[:, :length, :]
+
+    # We use a cache position index for tracking decoding position.
+    if self.decode:
+      is_initialized = self.has_variable('cache', 'cache_index')
+      cache_index = self.variable('cache', 'cache_index',
+        lambda: jnp.array(0, dtype=jnp.uint32))
+      if is_initialized:
+        i = cache_index.value
+        cache_index.value = i + 1
+        _, _, df = pos_embedding.shape
+        pe = lax.dynamic_slice(pos_embedding,
+          jnp.array((0, i, 0)),
+          (1, 1, df))
+    if inputs_positions is None:
+      # normal unpacked case:
+      return inputs + pe
+    else:
+      # for packed data we need to use known position indices:
+      return inputs + jnp.take(pe[0], inputs_positions, axis=0)
+
+
+class MlpBlock(nn.Module):
+  """Transformer MLP / feed-forward block.
+
+  Args:
+    config: TransformerConfig dataclass containing hyperparameters.
+    out_dim: optionally specify out dimension.
+  """
+  config: TransformerConfig
+  out_dim: Optional[int] = None
+
+  @nn.compact
+  def __call__(self, inputs):
+    """Applies Transformer MlpBlock module."""
+    config = self.config
+    actual_out_dim = (inputs.shape[-1] if self.out_dim is None
+                      else self.out_dim)
+    x = nn.Dense(
+      config.mlp_dim,
+      dtype=config.dtype,
+      kernel_init=config.kernel_init,
+      bias_init=config.bias_init)(
+      inputs)
+    x = nn.relu(x)
+    x = nn.Dropout(rate=config.dropout_rate)(
+      x, deterministic=config.deterministic)
+    output = nn.Dense(
+      actual_out_dim,
+      dtype=config.dtype,
+      kernel_init=config.kernel_init,
+      bias_init=config.bias_init)(
+      x)
+    output = nn.Dropout(rate=config.dropout_rate)(
+      output, deterministic=config.deterministic)
+    return output
+
+
+class EncoderDecoder1DBlock(nn.Module):
+  """Transformer encoder-decoder layer.
+
+  Args:
+    config: TransformerConfig dataclass containing hyperparameters.
+  """
+  config: TransformerConfig
+
+  @nn.compact
+  def __call__(self,
+               inputs,
+               decoder_mask=None,
+               encoder_decoder_mask=None):
+    """Applies EncoderDecoder1DBlock module.
+
+    Args:
+      inputs: input data for decoder
+      decoder_mask: decoder self-attention mask.
+      encoder_decoder_mask: encoder-decoder attention mask.
+
+    Returns:
+      output after transformer encoder-decoder block.
+    """
+    config = self.config
+
+    # Decoder block.
+    assert inputs.ndim==3
+    x = nn.LayerNorm(dtype=config.dtype)(inputs)
+    x = nn.SelfAttention(
+      num_heads=config.num_heads,
+      dtype=config.dtype,
+      qkv_features=config.qkv_dim,
+      kernel_init=config.kernel_init,
+      bias_init=config.bias_init,
+      use_bias=False,
+      broadcast_dropout=False,
+      dropout_rate=config.attention_dropout_rate,
+      deterministic=config.deterministic,
+      decode=config.decode)(x, decoder_mask)
+    x = nn.Dropout(rate=config.dropout_rate)(
+      x, deterministic=config.deterministic)
+    x = x + inputs
+
+    # MLP block.
+    z = nn.LayerNorm(dtype=config.dtype)(x)
+    z = MlpBlock(config=config)(z)
+
+    return x + z
+
+
+# Copyright 2021 The Eleuther AI and The Google Flax Team Authors and The HuggingFace Inc. team.
+#
+# 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.
+
+class FlaxTransformerLMPreTrainedModel(FlaxPreTrainedModel):
+  config_class = TransformerLMConfig
+  base_model_prefix = "decoder"
+  module_class: nn.Module = None
+
+  def __init__(
+      self,
+      config: TransformerLMConfig,
+      input_shape: Tuple = (1, 1),
+      seed: int = 0,
+      dtype: jnp.dtype = jnp.bfloat16,
+      _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)
+    position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
+    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, position_ids, 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))
+    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 unfreeze(init_variables["cache"])
+
+  def __call__(
+      self,
+      input_ids,
+      attention_mask=None,
+      position_ids=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,
+      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.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))
+
+    # 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 FlaxGPTNeoAttention module
+    if past_key_values:
+      inputs["cache"] = past_key_values
+      mutable = ["cache"]
+    else:
+      mutable = False
+
+    if input_ids.shape[1] > 1:
+      input_ids = jnp.insert(input_ids, 0, 0, axis=1) # Insert 0 at the beginning of prompt
+
+    # Progressive cache loop
+    if self.module.use_cache:
+      batch_size, seq_length = input_ids.shape
+      shape = (batch_size, seq_length, self.module.config.vocab_size)
+      logits = jnp.zeros(shape, dtype=self.dtype)
+
+      def loop_body_fn(i, state):
+        logits, cache = state
+        input_id = lax.dynamic_slice(input_ids, (0, i), (input_ids.shape[0], 1))
+        output = self.module.apply(
+          {
+            "params": inputs["params"],
+            "cache": cache
+          },
+          jnp.array(input_id, dtype="i4"),
+          jnp.array(attention_mask, dtype="i4"),
+          jnp.array(position_ids, dtype="i4"),
+          not train,
+          False,
+          output_attentions,
+          output_hidden_states,
+          return_dict,
+          rngs=rngs,
+          mutable=mutable,
+        )
+        lm_output, new_vars = output
+        logits = logits.at[:, i, :].set(lm_output.logits.squeeze(1))
+        return logits, new_vars["cache"]
+
+      cache = freeze(inputs["cache"])
+      initial_state = (logits, cache)
+      lm_logits, lm_cache = lax.fori_loop(0, seq_length, loop_body_fn, initial_state)
+
+      if seq_length > 1:
+        lm_logits = lm_logits[:, 1:, :] # Ignore leading zeros in prompts
+
+      lm_cache = {"cache": lm_cache}
+
+      if not return_dict:
+        outputs = (lm_logits,) + lm_cache["cache"]
+      else:
+        outputs = (FlaxCausalLMOutput(logits=lm_logits, hidden_states=None, attentions=None), lm_cache)
+    else:
+      output = self.module.apply(
+        inputs,
+        jnp.array(input_ids, dtype="i4"),
+        jnp.array(attention_mask, dtype="i4"),
+        jnp.array(position_ids, dtype="i4"),
+        not train,
+        False,
+        output_attentions,
+        output_hidden_states,
+        return_dict,
+        rngs=rngs,
+        mutable=mutable,
+      )
+      lm_logits = output.logits
+      if input_ids.shape[1] > 1:
+        lm_logits = lm_logits[:, 1:, :] # Ignore leading zeros in prompts
+
+      if not return_dict:
+        outputs = (lm_logits,) + output[1:]
+      else:
+        outputs = FlaxCausalLMOutput(logits=lm_logits, hidden_states=output.hidden_states, attentions=output.attentions)
+
+    # 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 FlaxTransformerLMModule(nn.Module):
+  config: TransformerConfig
+
+  def setup(self):
+    config = self.config
+    self.output_embed = nn.Embed(
+      num_embeddings=config.output_vocab_size,
+      features=config.emb_dim,
+      embedding_init=nn.initializers.normal(stddev=1.0),
+      name='Embed_0'
+    )
+    self.pos_embed = AddPositionEmbs(config=config, decode=config.decode, name='posembed_output')
+    self.dropout = nn.Dropout(rate=config.dropout_rate)
+    self.h_layers = [EncoderDecoder1DBlock(config=config, name=f'encoderdecoderblock_{i}')
+                     for i in range(config.num_layers)]
+    self.ln_f = nn.LayerNorm(dtype=config.dtype, name='encoderdecoder_norm')
+
+  @nn.compact
+  def __call__(
+      self,
+      input_ids,
+      attention_mask,
+      position_ids,
+      deterministic=True,
+      init_cache: bool = False,
+      output_attentions: bool = False,
+      output_hidden_states: bool = False,
+      return_dict: bool = True,
+  ):
+    config = self.config
+
+    y = input_ids.astype('int32')
+
+    y = self.output_embed(y)
+    y = self.pos_embed(y, inputs_positions=position_ids)
+    y = self.dropout(y, deterministic=config.deterministic)
+    y = y.astype(config.dtype)
+
+    for h in self.h_layers:
+      y = h(y, decoder_mask=attention_mask, encoder_decoder_mask=None)
+
+    outputs = (y, None, None)
+
+    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 if v is not None)
+
+    return FlaxBaseModelOutput(
+      last_hidden_state=hidden_states,
+      hidden_states=outputs[1],
+      attentions=outputs[-1],
+    )
+
+
+class FlaxTransformerLMModel(FlaxTransformerLMPreTrainedModel):
+  module_class = FlaxTransformerLMModule
+
+
+class FlaxTransformerLMForCausalLMModule(nn.Module):
+  config: TransformerLMConfig
+  dtype: jnp.dtype = jnp.bfloat16
+  kernel_init: Callable = nn.initializers.xavier_uniform()
+  bias_init: Callable = nn.initializers.normal(stddev=1e-6)
+  posemb_init: Callable = None
+  use_cache = False
+
+  def convert_config(self, cfg: TransformerLMConfig):
+    return TransformerConfig(
+      vocab_size=cfg.vocab_size,
+      output_vocab_size=cfg.vocab_size,
+      logits_via_embedding=cfg.logits_via_embedding,
+      dtype=self.dtype,
+      emb_dim=cfg.emb_dim,
+      num_heads=cfg.num_heads,
+      num_layers=cfg.num_layers,
+      qkv_dim=cfg.qkv_dim,
+      mlp_dim=cfg.mlp_dim,
+      max_len=cfg.max_len,
+      dropout_rate=cfg.dropout_rate,
+      attention_dropout_rate=cfg.attention_dropout_rate,
+      deterministic=cfg.deterministic,
+      decode=cfg.decode and self.use_cache,
+      kernel_init=self.kernel_init,
+      bias_init=self.bias_init,
+      posemb_init=self.posemb_init,
+    )
+
+  def setup(self):
+    config_ext = self.convert_config(self.config)
+    self.transformer = FlaxTransformerLMModule(config_ext, name='decoder')
+    self.lm_head = nn.Dense(
+      self.config.output_vocab_size,
+      dtype=self.dtype,
+      kernel_init=self.kernel_init,
+      bias_init=self.bias_init,
+      name='logitdense',
+    )
+
+  @nn.compact
+  def __call__(
+      self,
+      input_ids,
+      attention_mask,
+      position_ids,
+      deterministic: bool = True,
+      init_cache: bool = False,
+      output_attentions: bool = False,
+      output_hidden_states: bool = False,
+      return_dict: bool = True,
+  ):
+    decoder_mask = None
+    inputs_positions = None
+
+    outputs = self.transformer(
+      input_ids,
+      decoder_mask,
+      inputs_positions,
+      deterministic=deterministic,
+      init_cache=init_cache,
+      output_attentions=output_attentions,
+      output_hidden_states=output_hidden_states,
+      return_dict=return_dict,
+    )
+    hidden_states = outputs[0]
+    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)
+
+
+class FlaxTransformerLMForCausalLM(FlaxTransformerLMPreTrainedModel):
+  module_class = FlaxTransformerLMForCausalLMModule
+
+  def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
+
+    self.module_class.use_cache = True
+
+    # 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 GPTNeo 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 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