Upload StableLMAlphaForCausalLM

config.json

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+{
+  "_name_or_path": "stabilityai/stablelm-base-alpha-3b-v2",
+  "architectures": [
+    "StableLMAlphaForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_stablelm_alpha.StableLMAlphaConfig",
+    "AutoModelForCausalLM": "modeling_stablelm_alpha.StableLMAlphaForCausalLM"
+  },
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 4096,
+  "model_type": "stablelm_alpha",
+  "norm_eps": 1e-05,
+  "num_heads": 32,
+  "num_hidden_layers": 32,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 0.25,
+  "rotary_scaling_factor": 1.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.35.0",
+  "use_cache": true,
+  "vocab_size": 50432
+}

configuration_stablelm_alpha.py

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+# coding=utf-8
+# Copyright 2023 Stability 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.
+""" StableLM β model configuration"""
+
+from transformers import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+STABLE_LM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class StableLMAlphaConfig(PretrainedConfig):
+    r"""
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 50432):
+            Vocabulary size of the StableLM model. Defines the number of different tokens that
+            can be represented by the `inputs_ids` passed when calling [`StableLMAlphaModel`].
+        hidden_size (`int`, *optional*, defaults to 6144):
+            Dimension of the decoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 44):
+            Number of hidden layers in the Transformer decoder.
+        num_heads (`int`, *optional*, defaults to 64):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string).
+        rotary_pct (`float`, *optional*, defaults to 0.25):
+            Percentage of hidden dimensions to allocate to rotary embeddings.
+        rotary_emb_base (`int`, *optional*, defaults to 10000)
+            Base for computing rotary embeddings frequency.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 1e-5):
+            The standard deviation of the truncated_normal_initializer for initializing
+             all weight matrices.
+        norm_eps (`float`, *optional*, defaults to 1e-5):
+            The epsilon used by the normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions
+            (not used by all models). Only relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+
+    Example:
+
+    ```python
+    >>> from transformers import StableLMAlphaConfig, StableLMAlphaModel
+
+    >>> # Initializing a StableLMAlphaConfig style configuration
+    >>> configuration = StableLMAlphaConfig()
+
+    >>> # Initializing a model (with random weights) from the style configuration
+    >>> model = StableLMAlphaModel(configuration)  # doctest: +SKIP
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config  # doctest: +SKIP
+    ```"""
+    model_type = "stablelm_alpha"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=50_432,
+        hidden_size=2_560,
+        num_hidden_layers=32,
+        num_heads=32,
+        hidden_act="silu",
+        rotary_pct=0.25,
+        rotary_emb_base=10_000,
+        max_position_embeddings=2_048,
+        initializer_range=0.02,
+        norm_eps=1e-5,
+        use_cache=True,
+        bos_token_id=0,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_heads = num_heads
+        self.hidden_act = hidden_act
+        self.rotary_pct = rotary_pct
+        self.rotary_emb_base = rotary_emb_base
+        self.initializer_range = initializer_range
+        self.norm_eps = norm_eps
+        self.use_cache = use_cache
+        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.35.0"
+}

model-00001-of-00002.safetensors

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model-00002-of-00002.safetensors

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+    "transformer.layers.7.attention.out_proj.weight": "model-00001-of-00002.safetensors",
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+    "transformer.layers.9.attention.out_proj.weight": "model-00001-of-00002.safetensors",
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+    "transformer.layers.9.norm.bias": "model-00001-of-00002.safetensors",
+    "transformer.layers.9.norm.weight": "model-00001-of-00002.safetensors"
+  }
+}

modeling_stablelm_alpha.py

@@ -0,0 +1,656 @@
+# coding=utf-8
+# Copyright 2023 Stability AI, EleutherAI, 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.
+#
+# This code is based off the following work:
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+""" PyTorch StableLM-Alpha model. """
+from typing import Optional, Tuple, Union
+import math
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+
+from .configuration_stablelm_alpha import StableLMAlphaConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, tgt_seq_len, src_seq_len]`."""
+    batch_size, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(batch_size, 1, tgt_len, src_len).to(dtype)
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class LayerNorm(nn.LayerNorm):
+    def __init__(self, normalized_shape: torch.Size, bias: bool = True, **kwargs):
+        r"""
+        bias (`bool`, default = True): whether to use the bias term.
+        """
+        super().__init__(normalized_shape, **kwargs)
+        if not bias:
+            self.bias = None
+
+
+class DecoderLayer(nn.Module):
+    def __init__(self, config: StableLMAlphaConfig):
+        super().__init__()
+
+        self.norm = LayerNorm(config.hidden_size, eps=config.norm_eps)
+        self.attention = Attention(config)
+        self.mlp = MLP(config)
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.FloatTensor],
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
+        residual = hidden_states
+
+        # Pre-Norm
+        hidden_states = self.norm(hidden_states)
+
+        # Self-Attention
+        attn_output, attn_weights, present_key_value = self.attention(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+
+        # Feed-forward
+        mlp_output = self.mlp(hidden_states)
+
+        hidden_states = residual + attn_output + mlp_output
+
+        outputs = (hidden_states,)
+        if output_attentions:
+            outputs += (attn_weights,)
+        if use_cache:
+            outputs += (present_key_value,)
+        return outputs  # hidden_states, (optional: attn_weights), (optional: present_key_value)
+
+
+class MLP(nn.Module):
+    def __init__(self, config: StableLMAlphaConfig):
+        super().__init__()
+
+        hidden_size = config.hidden_size
+        multiple_of = 256
+        ff_dim = int(8 * hidden_size / 3)
+        intermediate_size = multiple_of * ((ff_dim + multiple_of - 1) // multiple_of)
+
+        self.gate_proj = nn.Linear(hidden_size, 2 * intermediate_size, bias=False)
+        self.out_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+        self.act = nn.SiLU()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        ff, ff_gate = self.gate_proj(x).chunk(2, dim=-1)
+        return self.out_proj(ff * self.act(ff_gate))
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        max_position_embeddings: int,
+        base: int = 10_000,
+        device: Optional[torch.device] = None,
+    ):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x: torch.Tensor, seq_len: Optional[int] = None):
+        # x: [batch_size, num_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=torch.get_default_dtype())
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+def rotate_half(x: torch.Tensor):
+    """Rotates half the hidden dims of the input."""
+    x1, x2 = torch.chunk(x, 2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [batch_size, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class Attention(nn.Module):
+    def __init__(self, config: StableLMAlphaConfig):
+        super().__init__()
+
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        if self.hidden_size % self.num_heads != 0:
+            raise ValueError(
+                "`hidden_size` is not divisble by the number of attention heads! Make sure to update them"
+            )
+
+        self.qkv_proj = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
+        self.out_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        self.rotary_ndims = int(self.head_dim * self.config.rotary_pct)
+        self.rotary_emb = RotaryEmbedding(
+            self.rotary_ndims,
+            max_position_embeddings=self.config.max_position_embeddings,
+            base=self.config.rotary_emb_base,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        has_past_key_value = past_key_value is not None
+
+        # Compute QKV
+        # [batch_size, seq_len, (num_heads * 3 * head_dim)]
+        qkv = self.qkv_proj(hidden_states)
+
+        # [batch_size, seq_len, num_heads, 3 * head_dim]
+        new_qkv_shape = qkv.size()[:-1] + (self.num_heads, 3 * self.head_dim)
+        qkv = qkv.view(*new_qkv_shape)
+
+        # 3 * [batch_size, num_heads, seq_len, head_dim]
+        query = qkv[..., : self.head_dim].permute(0, 2, 1, 3)
+        key = qkv[..., self.head_dim:(2 * self.head_dim)].permute(0, 2, 1, 3)
+        value = qkv[..., (2 * self.head_dim):].permute(0, 2, 1, 3)
+
+        # Compute rotary embeddings on rotary_ndims
+        # [batch_size, num_heads, seq_len, rotary_ndims]
+        query_rot = query[..., :self.rotary_ndims]
+        query_pass = query[..., self.rotary_ndims:]
+        key_rot = key[..., :self.rotary_ndims]
+        key_pass = key[..., self.rotary_ndims:]
+
+        # Compute token offset for rotary embeddings (when decoding)
+        kv_seq_len = key.shape[-2]
+        if has_past_key_value:
+            kv_seq_len += past_key_value[0].shape[-2]
+
+        # Add rotary embeddings to query and key
+        cos, sin = self.rotary_emb(value, seq_len=kv_seq_len)
+        query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
+
+        # Concatenate rotary embeddings with pass-through query and key
+        # [batch_size, num_heads, seq_len, head_dim]
+        query = torch.cat((query, query_pass), dim=-1)
+        key = torch.cat((key, key_pass), dim=-1)
+
+        # Reuse past key-value states
+        if has_past_key_value:
+            key = torch.cat((past_key_value[0], key), dim=2)
+            value = torch.cat((past_key_value[1], value), dim=2)
+        present_key_value = (key, value) if use_cache else None
+
+        # [batch_size, num_heads, seq_len, head_dim]
+        query = query.transpose(1, 2).contiguous()
+        key = key.transpose(1, 2).contiguous()
+        value = value.transpose(1, 2).contiguous()
+
+        # Compute attention
+        softmax_scale = 1 / math.sqrt(self.head_dim)
+        attn_scores = torch.einsum('bthd,bshd->bhts', query, key * softmax_scale)
+        # Apply the attention mask
+        if attention_mask is not None:
+            attn_scores = attn_scores + attention_mask
+        attn_weights = nn.functional.softmax(attn_scores, dim=-1, dtype=torch.float32).to(query.dtype)
+        attn_output = torch.einsum('bhts,bshd->bthd', attn_weights, value)
+
+        # Merge heads
+        attn_output = attn_output.reshape(attn_output.shape[0], attn_output.shape[1], -1)
+
+        # Final linear projection
+        attn_output = self.out_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, present_key_value
+
+
+def attention_mask_func(attention_scores: torch.Tensor, ltor_mask: torch.Tensor):
+    attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min)
+    return attention_scores
+
+
+class StableLMAlphaPreTrainedModel(PreTrainedModel):
+    """An abstract class to handle weights initialization and a simple interface
+    for downloading and loading pretrained models.
+    """
+
+    config_class = StableLMAlphaConfig
+    base_model_prefix = "transformer"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def _set_gradient_checkpointing(self, module: nn.Module, value=False):
+        if isinstance(module, StableLMAlphaModel):
+            module.gradient_checkpointing = value
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0
+):
+    """Make causal mask used for bi-directional self-attention."""
+    batch_size, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(torch.float16).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(batch_size, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+class StableLMAlphaModel(StableLMAlphaPreTrainedModel):
+    def __init__(self, config: StableLMAlphaConfig):
+        super().__init__(config)
+        self.config = config
+
+        self.embed = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.final_norm = LayerNorm(config.hidden_size, eps=config.norm_eps)
+
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.embed = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_shape: torch.Size,
+        inputs_embeds: torch.Tensor,
+        past_key_values_length: int,
+    ):
+        # Create causal mask
+        # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        r"""
+        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers`
+            with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks.
+            Can be used to speed up decoding. If `past_key_values` are used, the user
+            can optionally input only the last `decoder_input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)`
+            instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and
+            can be used to speed up decoding (see `past_key_values`).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+
+        if past_key_values is None:
+            past_key_values_length = 0
+            past_key_values = tuple([None] * self.config.num_hidden_layers)
+            seq_length_with_past = seq_length
+        else:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed(input_ids)
+
+        # Attention mask.
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+        present_key_values = () if use_cache else None
+
+        for _, (decoder_layer, past_key_value) in enumerate(zip(self.layers, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # `None` for `use_cache`
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    # `None` for `past_key_value`
+                    None,
+                )
+            else:
+                outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = outputs[0]
+
+            if output_attentions:
+                all_attentions = all_attentions + (outputs[1],)
+
+            if use_cache:
+                present_key_values += (outputs[2 if output_attentions else 1],)
+
+        hidden_states = self.final_norm(hidden_states)
+
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        present_key_values = present_key_values if use_cache else None
+        if not return_dict:
+            return tuple(v for v in [hidden_states, present_key_values, all_hidden_states, all_attentions] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=present_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+
+
+class StableLMAlphaForCausalLM(StableLMAlphaPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: StableLMAlphaConfig):
+        super().__init__(config)
+
+        self.transformer = StableLMAlphaModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings: nn.Module):
+        self.lm_head = new_embeddings
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, StableLMAlphaForCausalLM, StableLMAlphaConfig
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2", trust_remote_code=True)
+        >>> config = StableLMAlphaConfig.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2")
+        >>> config.is_decoder = True
+        >>> model = StableLMAlphaForCausalLM.from_pretrained("stabilityai/stablelm-base-alpha-3b-v2", config=config)
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> outputs = model(**inputs)
+
+        >>> logits = outputs.logits
+        ```
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        lm_loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shift_logits = logits[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=lm_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs
+    ):
+        # Cut decoder_input_ids if past is used
+        if past_key_values and past_key_values[0] is not None:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # Create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # If `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+        )
+
+        return model_inputs
+
+    def _reorder_cache(self, past_key_values: torch.Tensor, beam_idx: int):
+        reordered_past = ()
+        for past_key_value in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx) for past_state in past_key_value[:2]) + past_key_value[2:],
+            )
+        return reordered_past
+
+
+StableLMAlphaConfig.register_for_auto_class()
+StableLMAlphaForCausalLM.register_for_auto_class("AutoModelForCausalLM")