llama

config.json

@@ -1,40 +1,28 @@
 {
-  "activation_function": "swiglu",
-  "add_bias": true,
-  "apply_residual_connection_post_layernorm": false,
   "architectures": [
-    "GraniteForCausalLM"
+    "LlamaForCausalLM"
   ],
-  "attention_head_type": "mha",
-  "attention_multiplier": null,
-  "attention_softmax_in_fp32": true,
-  "attn_pdrop": 0.1,
-  "auto_map": {
-    "AutoConfig": "configuration_granite.GraniteConfig",
-    "AutoModel": "modeling_granite.GraniteModel",
-    "AutoModelForCausalLM": "modeling_granite.GraniteForCausalLM"
-  },
-  "bos_token_id": 0,
-  "embd_pdrop": 0.1,
-  "eos_token_id": 0,
+  "attention_bias": true,
+  "attention_dropout": 0.1,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
   "initializer_range": 0.02,
-  "layer_norm_epsilon": 1e-05,
-  "model_type": "granite",
-  "n_embd": 2560,
-  "n_head": 32,
-  "n_inner": 10240,
-  "n_layer": 32,
-  "n_positions": 2048,
-  "normalization_function": "rmsnorm",
+  "intermediate_size": 10240,
+  "max_position_embeddings": 2048,
+  "mlp_bias": true,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
   "num_key_value_heads": 32,
-  "pad_token_id": 0,
-  "position_embedding_type": "rope",
-  "resid_pdrop": 0.1,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
   "rope_theta": 10000,
-  "scale_attention_softmax_in_fp32": true,
-  "scale_attn_weights": true,
+  "tie_word_embeddings": true,
   "torch_dtype": "bfloat16",
-  "transformers_version": "4.38.1",
+  "transformers_version": "4.41.0.dev0",
   "use_cache": true,
   "vocab_size": 49152
 }

configuration_granite.py

@@ -1,98 +0,0 @@
-from transformers import PretrainedConfig
-
-
-class GraniteConfig(PretrainedConfig):
-    model_type = "granite"
-
-    keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "hidden_size": "n_embd",
-        "max_position_embeddings": "n_positions",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "n_layer",
-    }
-
-    def __init__(
-        self,
-        vocab_size: int = 50257,
-        n_positions: int = 1024,
-        n_embd: int = 768,
-        n_layer: int = 12,
-        n_head: int = 12,
-        num_key_value_heads: int = None,
-        n_inner: int = None,
-        activation_function: str = "gelu_pytorch_tanh",
-        attention_head_type: str = "mqa",
-        resid_pdrop: float = 0.1,
-        embd_pdrop: float = 0.1,
-        attn_pdrop: float = 0.1,
-        normalization_function: str = "layernorm",
-        layer_norm_epsilon: float = 1e-5,
-        initializer_range: float = 0.02,
-        scale_attn_weights: bool = True,
-        attention_multiplier: float = None,
-        use_cache: bool = True,
-        bos_token_id: int = 50256,
-        eos_token_id: int = 50256,
-        pad_token_id: int = 50256,
-        attention_softmax_in_fp32: bool = True,
-        scale_attention_softmax_in_fp32: bool = True,
-        add_bias: bool = True,
-        position_embedding_type: str = "learned_absolute",
-        rope_theta: int = 10000,
-        **kwargs,
-    ) -> None:
-        self.vocab_size = vocab_size
-        self.n_positions = n_positions
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.num_key_value_heads = num_key_value_heads
-        self.n_inner = 4 * n_embd if n_inner is None else n_inner
-        self.activation_function = activation_function
-        self.attention_head_type = attention_head_type
-        self.resid_pdrop = resid_pdrop
-        self.embd_pdrop = embd_pdrop
-        self.attn_pdrop = attn_pdrop
-        self.normalization_function = normalization_function
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.scale_attn_weights = scale_attn_weights
-        self.attention_multiplier = attention_multiplier
-        self.use_cache = use_cache
-        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
-        self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32
-        self.position_embedding_type = position_embedding_type
-        self.add_bias = add_bias
-        self.rope_theta = rope_theta
-
-        if self.attention_multiplier is not None:
-            assert self.scale_attn_weights
-
-        # for compatibility with some features
-        self.multi_query = attention_head_type == "mqa"
-
-        if attention_head_type == "mha":
-            if self.num_key_value_heads is None:
-                self.num_key_value_heads = self.n_head
-
-            assert (
-                self.n_head == self.num_key_value_heads
-            ), "MultiHeadAttention should have same number of heads for query, keys and values"
-        elif attention_head_type == "mqa":
-            if self.num_key_value_heads is None:
-                self.num_key_value_heads = 1
-
-            assert self.num_key_value_heads == 1, "MultiQueryAttention should have 1 head for keys and values"
-        elif attention_head_type == "gqa":
-            assert (
-                self.num_key_value_heads is not None
-            ), "`num_key_value_heads` needs to be specified with GroupedQueryAttention"
-
-            assert (
-                self.n_head % self.num_key_value_heads == 0
-            ), "GroupedQueryAttention should have more than 1 head for keys and values"
-        else:
-            raise ValueError(f"unexpected attention_head_type ({attention_head_type})")
-
-        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)

generation_config.json

@@ -1,7 +1,6 @@
 {
   "_from_model_config": true,
-  "bos_token_id": 0,
-  "eos_token_id": 0,
-  "pad_token_id": 0,
-  "transformers_version": "4.38.1"
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.41.0.dev0"
 }

model-00001-of-00002.safetensors

@@ -1,3 +1,3 @@
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model-00002-of-00002.safetensors

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+    "model.layers.9.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
+    "model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.o_proj.bias": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
+    "model.norm.weight": "model-00002-of-00002.safetensors"
   }
 }

modeling_granite.py

@@ -1,1376 +0,0 @@
-import math
-import numbers
-import warnings
-from enum import Enum
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from transformers import DynamicCache, PreTrainedModel
-from transformers.activations import get_activation as get_base_activation
-from transformers.modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
-from transformers.utils import is_flash_attn_2_available
-
-from .configuration_granite import GraniteConfig
-
-
-class PositionEmbeddingType(Enum):
-    learned_absolute = "learned_absolute"
-    alibi = "alibi"
-    rope = "rope"
-
-
-class AttentionHeadType(Enum):
-    mha = "mha"
-    mqa = "mqa"
-    gqa = "gqa"
-
-
-if is_flash_attn_2_available():
-    from flash_attn.bert_padding import IndexFirstAxis, pad_input, unpad_input
-    from flash_attn.flash_attn_interface import flash_attn_varlen_func
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def get_unpad_data(attention_mask: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return indices, cu_seqlens, max_seqlen_in_batch
-
-
-def repeat_key_value(x: torch.Tensor, num_heads: int, num_key_value_heads: int) -> torch.Tensor:
-    num_groups = num_heads // num_key_value_heads
-
-    # mha
-    if num_groups == 1:
-        return x
-
-    # mqa
-    if num_key_value_heads == 1:
-        return x.expand(-1, num_heads, -1, -1)
-
-    # gqa
-    return x.repeat_interleave(num_groups, dim=1)
-
-
-##################################################
-# activation functions
-
-
-_GLU_BASE_MAPPING = {
-    "geglu": "gelu",
-    "miglu": "mish",
-    "mishglu": "mish",
-    "swiglu": "swish",
-}
-
-
-class GLUActivation(nn.Module):
-    def __init__(self, base_activation: nn.Module) -> None:
-        super().__init__()
-        self.base_activation = base_activation
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = x.chunk(2, dim=-1)
-        return x[0] * self.base_activation(x[1])
-
-
-def is_glu(name: str) -> bool:
-    return name.endswith("glu")
-
-
-def get_activation_function(name: str) -> nn.Module:
-    if is_glu(name):
-        # for glu and sigmoid_glu, we directly return the pytorch's GLU
-        if name in ["glu", "sigmoid_glu"]:
-            activation_function = nn.modules.GLU()
-        else:
-            if name in _GLU_BASE_MAPPING:
-                name = _GLU_BASE_MAPPING[name]
-            elif name.endswith("_glu"):
-                name = name.rstrip("_glu")
-            else:
-                raise ValueError("invalid activation function")
-
-            base_activation = get_base_activation(name)
-            activation_function = GLUActivation(base_activation)
-    else:
-        activation_function = get_base_activation(name)
-
-    return activation_function
-
-
-##################################################
-# normalization functions
-
-
-class RMSNorm(nn.Module):
-    def __init__(self, normalized_shape: int, eps: float = 1e-6) -> None:
-        super().__init__()
-
-        self.weight = nn.Parameter(torch.ones(normalized_shape))
-        self.eps = eps
-
-        if isinstance(normalized_shape, numbers.Integral):
-            normalized_shape = (normalized_shape,)
-        self.normalized_shape = normalized_shape
-
-    def forward(self, input: torch.Tensor) -> torch.Tensor:
-        input_dtype = input.dtype
-
-        input = input.to(torch.float32)
-        variance = input.pow(2).mean(-1, keepdim=True)
-        input = input * torch.rsqrt(variance + self.eps)
-
-        return self.weight * input.to(input_dtype)
-
-    def extra_repr(self) -> str:
-        return f"{self.normalized_shape}, eps={self.eps}"
-
-    def reset_parameters(self) -> None:
-        nn.init.ones_(self.weight)
-
-
-_NORMALIZATION_FUNCTIONS = {
-    "layernorm": nn.LayerNorm,
-    "rmsnorm": RMSNorm,
-}
-
-
-def get_normalization_function(name: str, normalized_shape: int, eps: float = 1e-5) -> nn.Module:
-    if name in _NORMALIZATION_FUNCTIONS:
-        return _NORMALIZATION_FUNCTIONS[name](normalized_shape, eps=eps)
-
-    raise ValueError(f"unexpected `normalization_function` {name}")
-
-
-##################################################
-# attention modules
-
-
-class GraniteAttention(nn.Module):
-    def __init__(self, config: GraniteConfig, causal: bool, layer_idx: Optional[int] = None) -> None:
-        super().__init__()
-
-        self.causal = causal
-        self.hidden_size = config.n_embd
-        self.num_heads = config.n_head
-        self.num_key_value_heads = config.num_key_value_heads
-        self.add_bias = config.add_bias
-
-        assert (
-            self.hidden_size % self.num_heads == 0
-        ), f"`hidden_size` ({self.hidden_size}) must be divisible by `num_heads` ({self.num_heads})"
-
-        self.head_dim = self.hidden_size // self.num_heads
-        self.attention_head_type = AttentionHeadType(config.attention_head_type)
-
-        self.position_embedding_type = PositionEmbeddingType(config.position_embedding_type)
-        self.scale_attn_weights = config.scale_attn_weights
-        self.attention_multiplier = config.attention_multiplier
-
-        self.layer_idx = layer_idx
-        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
-        self.scale_attention_softmax_in_fp32 = (
-            config.scale_attention_softmax_in_fp32 and config.attention_softmax_in_fp32
-        )
-
-        if self.attention_head_type == AttentionHeadType.mha:
-            if self.num_key_value_heads is None:
-                self.num_key_value_heads = self.num_heads
-
-            assert (
-                self.num_heads == self.num_key_value_heads
-            ), f"{self.__class__.__name__} should have same number of heads for query, keys and values"
-        elif self.attention_head_type == AttentionHeadType.gqa:
-            assert (
-                self.num_key_value_heads is not None
-            ), "`num_key_value_heads` needs to be specified with GroupedQueryAttention"
-
-            assert self.num_heads % self.num_key_value_heads == 0, (
-                f"`num_heads` ({self.num_heads}) should be a multiple of `num_key_value_heads` "
-                f"({self.num_key_value_heads})"
-            )
-        elif self.attention_head_type == AttentionHeadType.mqa:
-            if self.num_key_value_heads is None:
-                self.num_key_value_heads = 1
-
-            assert self.num_key_value_heads == 1, f"{self.__class__.__name__} should have 1 head for keys and values"
-        else:
-            raise ValueError(f"unexpected attention_head_type ({self.attention_head_type})")
-
-        # note that the actual layout is different for the output and depends on whether we are using MHA, MQA or GQA
-        # (self.hidden_size + 2 * self.num_key_value_heads * self.head_dim) is just the actual number output features
-        self.c_attn = nn.Linear(
-            self.hidden_size, self.hidden_size + 2 * self.num_key_value_heads * self.head_dim, bias=self.add_bias
-        )
-        self.c_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=self.add_bias)
-
-        self.attn_pdrop = config.attn_pdrop
-        self.resid_pdrop = config.resid_pdrop
-
-        self.attn_dropout = nn.Identity() if self.attn_pdrop == 0 else nn.Dropout(self.attn_pdrop)
-        self.resid_dropout = nn.Identity() if self.resid_pdrop == 0 else nn.Dropout(self.resid_pdrop)
-
-    def _prepare_qkv_for_forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        # ==========================================================================================
-        # hidden_states -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        # the output of following is a tuple if using MQA with tensor parallel
-        hidden_states = self.c_attn(hidden_states)
-
-        # ==========================================================================================
-        # hidden_states -> (batch_size, query_length, [num_heads + num_key_value_heads * 2] * head_dim)
-        # ==========================================================================================
-
-        # for MHA, we can get away with doing just 1 transpose which is not true for GQA
-        if self.attention_head_type == AttentionHeadType.mha:
-            query, key, value = self._prepare_qkv_for_forward_mha(hidden_states)
-        elif self.attention_head_type == AttentionHeadType.gqa:
-            query, key, value = self._prepare_qkv_for_forward_gqa(hidden_states)
-        elif self.attention_head_type == AttentionHeadType.mqa:
-            query, key, value = self._prepare_qkv_for_forward_mqa(hidden_states)
-        else:
-            raise ValueError(f"unexpected attention_head_type ({self.attention_head_type})")
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # value -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # ==========================================================================================
-
-        return query, key, value
-
-    def _prepare_qkv_for_forward_mha(
-        self, hidden_states: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        batch_size, query_length = hidden_states.shape[:-1]
-
-        hidden_states = hidden_states.view(batch_size, query_length, self.num_heads, -1)
-        hidden_states = hidden_states.transpose(1, 2)
-
-        query, key, value = hidden_states.chunk(3, dim=-1)
-
-        return query, key, value
-
-    def _prepare_qkv_for_forward_gqa(
-        self, hidden_states: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        batch_size, query_length = hidden_states.shape[:-1]
-
-        hidden_states = hidden_states.view(batch_size, query_length, self.num_key_value_heads, -1)
-
-        query, key, value = hidden_states.split(
-            ((self.num_heads // self.num_key_value_heads) * self.head_dim, self.head_dim, self.head_dim), dim=-1
-        )
-
-        # this needs to be a reshape instead of view sadly
-        query = query.reshape(batch_size, query_length, -1, self.head_dim)
-
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        return query, key, value
-
-    def _prepare_qkv_for_forward_mqa(
-        self, hidden_states: torch.Tensor
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        batch_size, query_length = hidden_states.shape[:-1]
-
-        query, key, value = hidden_states.split((self.hidden_size, self.head_dim, self.head_dim), dim=-1)
-
-        query = query.view(batch_size, query_length, self.num_heads, -1)
-
-        query = query.transpose(1, 2)
-        key = key.unsqueeze(1)
-        value = value.unsqueeze(1)
-
-        return query, key, value
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        past_key_values: Optional[DynamicCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        rope_cos_sin: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> torch.Tensor:
-        # ==========================================================================================
-        # hidden_states -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        query, key, value = self._prepare_qkv_for_forward(hidden_states)
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # value -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # ==========================================================================================
-
-        if self.position_embedding_type == PositionEmbeddingType.rope:
-            query = apply_rotary_pos_emb(query, rope_cos_sin)
-            key = apply_rotary_pos_emb(key, rope_cos_sin)
-
-        if past_key_values is not None:
-            key, value = past_key_values.update(key, value, self.layer_idx)
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, key_length, head_dim)
-        # value -> (batch_size, num_key_value_heads, key_length, head_dim)
-        # ==========================================================================================
-
-        key = key.transpose(-1, -2)
-
-        dtype = query.dtype
-        softmax_dtype = torch.float32 if self.attention_softmax_in_fp32 else dtype
-
-        if self.scale_attn_weights:
-            if self.attention_multiplier is None:
-                scale_factor = 1 / self.head_dim**0.5
-            else:
-                scale_factor = self.attention_multiplier
-        else:
-            scale_factor = 1
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, head_dim, key_length)
-        # value -> (batch_size, num_key_value_heads, key_length, head_dim)
-        # ==========================================================================================
-
-        batch_size = query.shape[0]
-        query_length = query.shape[2]
-        key_length = key.shape[-1]
-
-        key = repeat_key_value(key, self.num_heads, self.num_key_value_heads)
-        value = repeat_key_value(value, self.num_heads, self.num_key_value_heads)
-
-        # Always copies
-        query = query.reshape(batch_size * self.num_heads, query_length, self.head_dim)
-        # No copy when layer_past is provided.
-        key = key.reshape(batch_size * self.num_heads, self.head_dim, key_length)
-
-        # ==========================================================================================
-        # query -> (batch_size * num_heads, query_length, head_dim)
-        # key -> (batch_size * num_heads, head_dim, key_length)
-        # value -> (batch_size, num_heads, key_length, head_dim)
-        # ==========================================================================================
-
-        attn_weights = torch.empty(
-            (batch_size * self.num_heads, query_length, key_length), device=query.device, dtype=query.dtype
-        )
-
-        attn_weights = torch.baddbmm(attn_weights, query, key, beta=0, alpha=scale_factor).view(
-            batch_size, self.num_heads, query_length, key_length
-        )
-
-        # ==========================================================================================
-        # attn_weights -> (batch_size, num_heads, query_length, key_length)
-        # ==========================================================================================
-
-        attn_weights = attn_weights.to(softmax_dtype)
-
-        if attention_mask is not None:
-            attn_weights = attn_weights + attention_mask
-
-        attn_weights = F.softmax(attn_weights, dim=-1).to(dtype)
-
-        attn_weights = self.attn_dropout(attn_weights)
-
-        # ==========================================================================================
-        # value -> (batch_size, num_heads, key_length, head_dim)
-        # attn_weights -> (batch_size, num_heads, query_length, key_length)
-        # ==========================================================================================
-
-        attn_output = torch.matmul(attn_weights, value)
-
-        # ==========================================================================================
-        # attn_output -> (batch_size, num_heads, query_length, head_dim)
-        # ==========================================================================================
-
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(batch_size, -1, self.num_heads * self.head_dim)
-
-        # ==========================================================================================
-        # attn_output -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        attn_output = self.c_proj(attn_output)
-        attn_output = self.resid_dropout(attn_output)
-
-        return attn_output
-
-
-class GraniteSDPA(GraniteAttention):
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        past_key_values: Optional[DynamicCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        rope_cos_sin: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> torch.Tensor:
-        # ==========================================================================================
-        # hidden_states -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        query, key, value = self._prepare_qkv_for_forward(hidden_states)
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # value -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # ==========================================================================================
-
-        if self.position_embedding_type == PositionEmbeddingType.rope:
-            query = apply_rotary_pos_emb(query, rope_cos_sin)
-            key = apply_rotary_pos_emb(key, rope_cos_sin)
-
-        if past_key_values is not None:
-            key, value = past_key_values.update(key, value, self.layer_idx)
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, key_length, head_dim)
-        # value -> (batch_size, num_key_value_heads, key_length, head_dim)
-        # ==========================================================================================
-
-        key = repeat_key_value(key, self.num_heads, self.num_key_value_heads)
-        value = repeat_key_value(value, self.num_heads, self.num_key_value_heads)
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_heads, key_length, head_dim)
-        # value -> (batch_size, num_heads, key_length, head_dim)
-        # ==========================================================================================
-
-        attn_output = F.scaled_dot_product_attention(
-            query,
-            key,
-            value,
-            attn_mask=attention_mask,
-            dropout_p=self.attn_pdrop if self.training else 0,
-            is_causal=self.causal if attention_mask is None else False,
-            scale=self.attention_multiplier if self.scale_attn_weights else 1,
-        )
-
-        # ==========================================================================================
-        # attn_output -> (batch_size, num_heads, query_length, head_dim)
-        # ==========================================================================================
-
-        batch_size = attn_output.shape[0]
-        attn_output = attn_output.transpose(1, 2)
-        attn_output = attn_output.reshape(batch_size, -1, self.num_heads * self.head_dim)
-
-        # ==========================================================================================
-        # attn_output -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        attn_output = self.c_proj(attn_output)
-        attn_output = self.resid_dropout(attn_output)
-
-        return attn_output
-
-
-class GraniteFlashAttention2(GraniteAttention):
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        past_key_values: Optional[DynamicCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        rope_cos_sin: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> torch.Tensor:
-        # ==========================================================================================
-        # hidden_states -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        query, key, value = self._prepare_qkv_for_forward(hidden_states)
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # value -> (batch_size, num_key_value_heads, query_length, head_dim)
-        # ==========================================================================================
-
-        if self.position_embedding_type == PositionEmbeddingType.rope:
-            query = apply_rotary_pos_emb(query, rope_cos_sin)
-            key = apply_rotary_pos_emb(key, rope_cos_sin)
-
-        if past_key_values is not None:
-            key, value = past_key_values.update(key, value, self.layer_idx)
-
-        # ==========================================================================================
-        # query -> (batch_size, num_heads, query_length, head_dim)
-        # key -> (batch_size, num_key_value_heads, key_length, head_dim)
-        # value -> (batch_size, num_key_value_heads, key_length, head_dim)
-        # ==========================================================================================
-
-        # TODO avoid this extra transpose
-        query = query.transpose(1, 2)
-        if self.attention_head_type == AttentionHeadType.mqa:
-            key = key.squeeze(1).unsqueeze(2)
-            value = value.squeeze(1).unsqueeze(2)
-        else:
-            key = key.transpose(1, 2)
-            value = value.transpose(1, 2)
-
-        # ==========================================================================================
-        # query -> (batch_size, query_length, num_heads, head_dim)
-        # key -> (batch_size, key_length, num_heads, head_dim)
-        # value -> (batch_size, key_length, num_heads, head_dim)
-        # ==========================================================================================
-
-        batch_size, query_length = query.shape[:2]
-        key_length = key.shape[1]
-        indices_k, cu_seqlens_k, max_seqlen_k = get_unpad_data(attention_mask)
-
-        key = IndexFirstAxis.apply(
-            key.reshape(batch_size * key_length, self.num_key_value_heads, self.head_dim), indices_k
-        )
-        value = IndexFirstAxis.apply(
-            value.reshape(batch_size * key_length, self.num_key_value_heads, self.head_dim), indices_k
-        )
-
-        if query_length == key_length:
-            query = IndexFirstAxis.apply(
-                query.reshape(batch_size * key_length, self.num_heads, self.head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_q = max_seqlen_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query = query.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(query, attention_mask)
-
-        # ==========================================================================================
-        # query -> (total_q, num_heads, head_dim)
-        # key -> (total_q, num_heads, head_dim)
-        # value -> (total_q, num_heads, head_dim)
-        # ==========================================================================================
-
-        attn_output = flash_attn_varlen_func(
-            query,
-            key,
-            value,
-            cu_seqlens_q=cu_seqlens_q,
-            cu_seqlens_k=cu_seqlens_k,
-            max_seqlen_q=max_seqlen_q,
-            max_seqlen_k=max_seqlen_k,
-            dropout_p=self.attn_pdrop if self.training else 0,
-            softmax_scale=self.attention_multiplier if self.scale_attn_weights else 1,
-            causal=self.causal,
-        )
-
-        # ==========================================================================================
-        # attn_output -> (total_q, num_heads, head_dim)
-        # ==========================================================================================
-
-        attn_output = pad_input(attn_output, indices_q, batch_size, query_length)
-        attn_output = attn_output.view(batch_size, query_length, -1)
-
-        # ==========================================================================================
-        # attn_output -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        attn_output = self.c_proj(attn_output)
-        attn_output = self.resid_dropout(attn_output)
-
-        return attn_output
-
-
-_ATTENTION_MODULES = {
-    "eager": GraniteAttention,
-    "sdpa": GraniteSDPA,
-    "flash_attention_2": GraniteFlashAttention2,
-}
-
-
-def get_attention_module(
-    config: GraniteConfig, causal: bool, attention_implementation: str, layer_idx: int
-) -> GraniteAttention:
-    if attention_implementation in _ATTENTION_MODULES:
-        return _ATTENTION_MODULES[attention_implementation](config, causal=causal, layer_idx=layer_idx)
-    raise ValueError(f"unexpected `attention_implementation` {attention_implementation}")
-
-
-##################################################
-# position embeddings
-
-
-class Alibi(nn.Module):
-    def __init__(self, num_heads: int) -> None:
-        super().__init__()
-        self.num_heads = num_heads
-
-        self.reset_parameters()
-
-    def forward(
-        self, attention_mask: torch.Tensor, batch_size: int, key_length: int, device: torch.device, dtype: torch.dtype
-    ) -> torch.Tensor:
-        """
-        Link to paper: https://arxiv.org/abs/2108.12409 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
-        TODO @thomasw21 this doesn't work as nicely due to the masking strategy, and so masking varies slightly.
-
-        Args:
-            attention_mask (torch.Tensor): attention_mask tensor of shape (`batch_size`, `key_length`)
-            num_heads (int): `num_heads` for the model
-            batch_size (int): `batch_size`
-            key_length (int): `key_length`
-            device (torch.device): device for the tensors
-            dtype (torch.dtype): dtype to use for the tensors
-
-        Returns:
-            torch.Tensor: alibi tensor of shape (`batch_size`, `num_heads`, `key_length`)
-        """
-
-        # Note: alibi 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)
-        # => the query_length dimension will then be broadcasted 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
-        if attention_mask is None:
-            arange_tensor = (
-                torch.arange(key_length, device=device).unsqueeze(0).unsqueeze(0).expand(batch_size, -1, -1)
-            )
-        else:
-            arange_tensor = (attention_mask.cumsum(dim=-1) - 1).masked_fill_(attention_mask == 0, 0).unsqueeze(1)
-
-        alibi = self.slopes.unsqueeze(1) * arange_tensor
-        return alibi.to(dtype)
-
-    def reset_parameters(self) -> None:
-        closest_power_of_2 = 2 ** math.floor(math.log2(self.num_heads))
-        base = torch.tensor(2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), dtype=torch.float32)
-        powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
-        slopes = torch.pow(base, powers)
-
-        if closest_power_of_2 != self.num_heads:
-            extra_base = torch.tensor(2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), dtype=torch.float32)
-            num_remaining_heads = min(closest_power_of_2, self.num_heads - closest_power_of_2)
-            extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, dtype=torch.int32)
-            slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
-
-        self.register_buffer("slopes", slopes, persistent=False)
-
-
-class RoPE(nn.Module):
-    def __init__(
-        self,
-        head_dim: int,
-        max_position_embeddings: int = 2048,
-        base: int = 10000,
-    ) -> None:
-        super().__init__()
-
-        self.head_dim = head_dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        self.mscale = 1
-
-        self.reset_parameters()
-
-    def forward(self, seq_len: int, dtype: torch.dtype, device: torch.device) -> Tuple[torch.Tensor, torch.Tensor]:
-        if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=seq_len, device=device, dtype=dtype)
-
-        cos = self.cos_cached[:seq_len].to(dtype)
-        sin = self.sin_cached[:seq_len].to(dtype)
-
-        return cos, sin
-
-    def reset_parameters(self) -> None:
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.head_dim, 2).float() / self.head_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=self.max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
-        )
-
-    @torch.no_grad()
-    def _set_cos_sin_cache(self, seq_len: int, device: torch.device, dtype: torch.dtype) -> None:
-        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() * self.mscale).to(dtype), persistent=False)
-        self.register_buffer("sin_cached", (emb.sin() * self.mscale).to(dtype), persistent=False)
-
-
-def apply_rotary_pos_emb(x: torch.Tensor, cos_sin: Tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
-    cos, sin = cos_sin
-    x = (x * cos) + (_rotate_half(x) * sin)
-    return x
-
-
-def _rotate_half(x: torch.Tensor) -> torch.Tensor:
-    x1, x2 = torch.chunk(x, 2, dim=-1)
-    return torch.cat((-x2, x1), dim=-1)
-
-
-##################################################
-# MLP
-
-
-class GraniteMLP(nn.Module):
-    def __init__(self, config: GraniteConfig) -> None:
-        super().__init__()
-
-        hidden_size = config.n_embd
-        intermediate_size = config.n_inner
-        activation_function = config.activation_function
-        add_bias = config.add_bias
-        residual_dropout = config.resid_pdrop
-
-        self.c_fc = nn.Linear(
-            hidden_size,
-            2 * intermediate_size if is_glu(activation_function) else intermediate_size,
-            bias=add_bias,
-        )
-        self.act = get_activation_function(activation_function)
-        self.c_proj = nn.Linear(intermediate_size, hidden_size, bias=add_bias)
-        self.dropout = nn.Identity() if residual_dropout == 0 else nn.Dropout(residual_dropout)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.c_fc(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.c_proj(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        return hidden_states
-
-
-##################################################
-# transformer layer
-
-
-class GraniteBlock(nn.Module):
-    def __init__(
-        self,
-        config: GraniteConfig,
-        attention_implementation: str,
-        layer_idx: Optional[int] = None,
-    ) -> None:
-        super().__init__()
-
-        hidden_size = config.hidden_size
-        self.inner_dim = config.n_inner
-        self.layer_idx = layer_idx
-
-        self.ln_1 = get_normalization_function(
-            config.normalization_function,
-            hidden_size,
-            eps=config.layer_norm_epsilon,
-        )
-        self.attn = get_attention_module(config, True, attention_implementation, layer_idx)
-        self.ln_2 = get_normalization_function(
-            config.normalization_function,
-            hidden_size,
-            eps=config.layer_norm_epsilon,
-        )
-        self.mlp = GraniteMLP(config)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        past_key_values: Optional[DynamicCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        rope_cos_sin: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    ) -> torch.Tensor:
-        residual = hidden_states
-        hidden_states = self.ln_1(hidden_states)
-
-        attn_output = self.attn(
-            hidden_states,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            rope_cos_sin=rope_cos_sin,
-        )
-
-        # residual connection
-        hidden_states = attn_output + residual
-
-        residual = hidden_states
-        hidden_states = self.ln_2(hidden_states)
-
-        feed_forward_hidden_states = self.mlp(hidden_states)
-
-        # residual connection
-        hidden_states = residual + feed_forward_hidden_states
-
-        return hidden_states
-
-
-##################################################
-# model classes
-
-
-class GranitePreTrainedModel(PreTrainedModel):
-    """
-    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
-    models.
-    """
-
-    config_class = GraniteConfig
-    base_model_prefix = "transformer"
-    causal = True
-    _no_split_modules = ["GraniteBlock"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_sdpa = True
-    _supports_flash_attn_2 = True
-
-    def __init__(self, config: GraniteConfig, *inputs, **kwargs):
-        super().__init__(config, *inputs, **kwargs)
-
-        self.attention_implementation = self.config._attn_implementation
-        self._use_eager_attention = self.attention_implementation == "eager"
-        self._use_sdpa = self.attention_implementation == "sdpa"
-        self._use_flash_attention_2 = self.attention_implementation == "flash_attention_2"
-
-        self.initializer_range = config.initializer_range
-
-    def _init_weights(self, module: nn.Module) -> None:
-        if isinstance(module, (nn.LayerNorm, RMSNorm, Alibi, RoPE)):
-            module.reset_parameters()
-        elif isinstance(module, nn.Linear):
-            nn.init.normal_(module.weight, mean=0, std=self.initializer_range)
-            if module.bias is not None:
-                module.bias.zero_()
-        elif isinstance(module, nn.Embedding):
-            nn.init.normal_(module.weight, mean=0, std=self.initializer_range)
-            if module.padding_idx is not None:
-                module.weight[module.padding_idx].zero_()
-
-
-class GraniteModel(GranitePreTrainedModel):
-    _keys_to_ignore_on_load_missing = ["attn.masked_bias"]
-    mask_value = None
-
-    def __init__(self, config: GraniteConfig, **kwargs) -> None:
-        super().__init__(config, **kwargs)
-
-        self.attention_head_type = AttentionHeadType(config.attention_head_type)
-        self.embed_dim = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.num_key_value_heads = config.num_key_value_heads
-
-        assert (
-            self.embed_dim % self.num_heads == 0
-        ), f"`embed_dim` ({self.embed_dim}) must be divisible by `num_heads` ({self.num_heads})"
-
-        self.head_dim = self.embed_dim // self.num_heads
-
-        self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
-
-        self.drop = nn.Identity() if config.embd_pdrop == 0 else nn.Dropout(config.embd_pdrop)
-        self.h = nn.ModuleList(
-            [GraniteBlock(config, self.attention_implementation, layer_idx=i) for i in range(config.num_hidden_layers)]
-        )
-        self.ln_f = get_normalization_function(
-            config.normalization_function,
-            self.embed_dim,
-            eps=config.layer_norm_epsilon,
-        )
-
-        self.position_embedding_type = PositionEmbeddingType(config.position_embedding_type)
-
-        if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
-            self.wpe = nn.Embedding(config.n_positions, self.embed_dim)
-        elif self.position_embedding_type == PositionEmbeddingType.alibi:
-            assert not self._use_flash_attention_2, "alibi is not implemented with FlashAttention"
-
-            self.alibi = Alibi(self.num_heads)
-        elif self.position_embedding_type == PositionEmbeddingType.rope:
-            self.rope = RoPE(self.head_dim, max_position_embeddings=config.n_positions, base=config.rope_theta)
-        else:
-            raise NotImplementedError()
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.wte
-
-    def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
-        self.wte = new_embeddings
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        past_key_values: Optional[DynamicCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        use_cache: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]:
-        (
-            output_hidden_states,
-            use_cache,
-            return_dict,
-            input_shape,
-            hidden_states,
-            attention_mask,
-            position_ids,
-            rope_cos_sin,
-            past_key_values,
-        ) = self._prepare_a_bunch_of_stuff(
-            input_ids=input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        # ==========================================================================================
-        # flash:
-        #     attention_mask -> (batch_size, key_length)
-        # else:
-        #     attention_mask -> (batch_size, 1, query_length, key_length)
-        # ==========================================================================================
-
-        output_shape = input_shape + (hidden_states.size(-1),)
-
-        past_key_values = DynamicCache() if use_cache and past_key_values is None else past_key_values
-        all_hidden_states = () if output_hidden_states else None
-        for block in self.h:
-            if output_hidden_states:
-                all_hidden_states += (hidden_states,)
-
-            hidden_states = block(
-                hidden_states,
-                past_key_values=past_key_values,
-                attention_mask=attention_mask,
-                rope_cos_sin=rope_cos_sin,
-            )
-
-        hidden_states = self.ln_f(hidden_states)
-
-        hidden_states = hidden_states.view(output_shape)
-        # Add last hidden state
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, past_key_values, all_hidden_states] if v is not None)
-
-        return BaseModelOutputWithPastAndCrossAttentions(
-            last_hidden_state=hidden_states,
-            past_key_values=past_key_values,
-            hidden_states=all_hidden_states,
-        )
-
-    def _get_position_ids(
-        self, attention_mask: torch.Tensor, past_length: int, query_length: int, key_length: int, device: torch.device
-    ) -> torch.Tensor:
-        if attention_mask is not None and len(attention_mask.shape) == 2:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 0)
-            if past_length > 0:
-                position_ids = position_ids[:, past_length:key_length:]
-        else:
-            position_ids = torch.arange(past_length, key_length, dtype=torch.long, device=device)
-            position_ids = position_ids.unsqueeze(0).view(-1, query_length)
-
-        return position_ids
-
-    def _get_alibi_bias(
-        self,
-        attention_mask: torch.Tensor,
-        batch_size: int,
-        query_length: int,
-        key_length: int,
-        device: torch.device,
-        dtype: torch.dtype,
-    ) -> torch.Tensor:
-        if self.position_embedding_type != PositionEmbeddingType.alibi:
-            return None
-
-        alibi_bias = self.alibi(attention_mask, batch_size, key_length, device, dtype)
-
-        # ==========================================================================================
-        # alibi_bias -> (batch_size, num_heads, key_length)
-        # ==========================================================================================
-
-        alibi_bias = alibi_bias.unsqueeze(2)
-        if query_length != 1:
-            alibi_bias = alibi_bias.expand(-1, -1, query_length, -1)
-
-        # ==========================================================================================
-        # alibi_bias -> (batch_size, num_heads, query_length, key_length)
-        # ==========================================================================================
-
-        return alibi_bias
-
-    def _get_rope_cos_sin(
-        self, key_length: int, position_ids: torch.Tensor, dtype: torch.dtype, device: torch.device
-    ) -> Optional[Tuple[torch.Tensor, torch.Tensor]]:
-        if self.position_embedding_type == PositionEmbeddingType.rope:
-            cos, sin = self.rope(key_length, dtype=dtype, device=device)
-            cos = cos[position_ids].unsqueeze(1)
-            sin = sin[position_ids].unsqueeze(1)
-            return cos, sin
-
-    def _prepare_causal_attention_mask(
-        self, attention_mask: torch.Tensor, batch_size: int, query_length: int, key_length: int, device: torch.device
-    ) -> torch.Tensor:
-        past_length = key_length - query_length
-
-        # ==========================================================================================
-        # attention_mask -> (batch_size, key_length)
-        # ==========================================================================================
-
-        if query_length > 1:
-            # (query_length, key_length)
-            causal_mask = torch.empty((query_length, key_length), dtype=torch.bool, device=device)
-            causal_mask[:, past_length:] = torch.tril(
-                torch.ones(query_length, query_length, dtype=torch.bool, device=device)
-            )
-
-            if past_length > 0:
-                causal_mask[:, :past_length] = True
-
-            # (query_length, key_length) -> (1, query_length, key_length)
-            causal_mask = causal_mask.unsqueeze(0)
-
-            if attention_mask is None:
-                # (1, query_length, key_length) -> (batch_size, query_length, key_length)
-                causal_mask = causal_mask.expand(batch_size, -1, -1)
-            else:
-                # (1, query_length, key_length) & (batch_size, 1, key_length) -> (batch_size, query_length, key_length)
-                causal_mask = causal_mask & attention_mask.unsqueeze(1).to(torch.bool)
-        else:
-            if attention_mask is None:
-                # (batch_size, query_length, key_length)
-                causal_mask = torch.ones(batch_size, query_length, key_length, dtype=torch.bool, device=device)
-            else:
-                # (batch_size, query_length, key_length)
-                causal_mask = attention_mask.unsqueeze(1).to(dtype=torch.bool, device=device)
-
-        # ==========================================================================================
-        # attention_mask -> (batch_size, query_length, key_length)
-        # ==========================================================================================
-
-        causal_mask = causal_mask.unsqueeze(1)
-
-        # ==========================================================================================
-        # attention_mask -> (batch_size, 1, query_length, key_length)
-        # ==========================================================================================
-
-        return causal_mask
-
-    def _get_initial_hidden_state(
-        self,
-        input_ids: torch.Tensor,
-        inputs_embeds: torch.Tensor,
-        position_ids: torch.Tensor,
-        token_type_ids: torch.Tensor,
-    ) -> torch.Tensor:
-        if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
-
-        if self.position_embedding_type == PositionEmbeddingType.learned_absolute:
-            inputs_embeds = inputs_embeds + self.wpe(position_ids)
-
-        if token_type_ids is not None:
-            inputs_embeds = inputs_embeds + self.wte(token_type_ids)
-
-        inputs_embeds = self.drop(inputs_embeds)
-
-        return inputs_embeds
-
-    def _prepare_a_bunch_of_stuff(
-        self,
-        input_ids: torch.Tensor,
-        past_key_values: DynamicCache,
-        attention_mask: torch.Tensor,
-        token_type_ids: torch.Tensor,
-        position_ids: torch.Tensor,
-        inputs_embeds: torch.Tensor,
-        use_cache: bool,
-        output_hidden_states: bool,
-        return_dict: bool,
-    ) -> Tuple[
-        bool,
-        bool,
-        bool,
-        torch.Size,
-        torch.Tensor,
-        torch.Tensor,
-        torch.Tensor,
-        Optional[Tuple[torch.Tensor, torch.Tensor]],
-        DynamicCache,
-    ]:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-
-        use_cache = self.config.use_cache if use_cache is None else use_cache
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        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:
-            # TODO special handling for padding free transformer needed here if we support inputs_embeds argument
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        batch_size = input_shape[0]
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
-        if self.position_embedding_type == PositionEmbeddingType.alibi:
-            if position_ids is not None:
-                warnings.warn("`position_ids` have no functionality with Alibi.", FutureWarning)
-
-        if token_type_ids is not None:
-            token_type_ids = token_type_ids.view(-1, input_shape[-1])
-
-        # ==========================================================================================
-        # input_ids -> (batch_size, query_length)
-        # attention_mask -> None or (batch_size, key_length)
-        # position_ids -> None or (batch_size, key_length)
-        # ==========================================================================================
-
-        past_length = 0 if past_key_values is None else past_key_values.get_seq_length()
-        query_length = input_shape[-1]
-        key_length = past_length + query_length
-
-        if position_ids is None:
-            position_ids = self._get_position_ids(attention_mask, past_length, query_length, key_length, device)
-
-        # ==========================================================================================
-        # input_ids -> (batch_size, query_length)
-        # attention_mask -> None or (batch_size, key_length)
-        # position_ids -> (batch_size, query_length)
-        # ==========================================================================================
-
-        hidden_states = self._get_initial_hidden_state(input_ids, inputs_embeds, position_ids, token_type_ids)
-
-        # ==========================================================================================
-        # hidden_states -> (batch_size, query_length, num_heads * head_dim)
-        # ==========================================================================================
-
-        alibi_bias = self._get_alibi_bias(
-            attention_mask, batch_size, query_length, key_length, device, hidden_states.dtype
-        )
-
-        # ==========================================================================================
-        # alibi_bias -> (batch_size, num_heads, query_length, key_length)
-        # ==========================================================================================
-
-        rope_cos_sin = self._get_rope_cos_sin(
-            key_length, position_ids, dtype=hidden_states.dtype, device=hidden_states.device
-        )
-
-        # ==========================================================================================
-        # rope_cos_sin -> 2 * (key_length, head_dim)
-        # ==========================================================================================
-
-        # prepare causal mask only if not using flash attention
-        if self._use_flash_attention_2:
-            if attention_mask is None:
-                attention_mask = torch.ones_like(input_ids)
-        elif self._use_sdpa:
-            # we use the causal/non-causal argument of SDPA for attention in this case
-            if attention_mask is not None:
-                attention_mask = self._prepare_causal_attention_mask(
-                    attention_mask, batch_size, query_length, key_length, device
-                )
-
-                attention_mask = torch.where(
-                    attention_mask,
-                    ~attention_mask if alibi_bias is None else alibi_bias,
-                    self._get_mask_value(attention_mask.device, hidden_states.dtype),
-                )
-        else:
-            attention_mask = self._prepare_causal_attention_mask(
-                attention_mask, batch_size, query_length, key_length, device
-            )
-
-            attention_mask = torch.where(
-                attention_mask,
-                ~attention_mask if alibi_bias is None else alibi_bias,
-                self._get_mask_value(attention_mask.device, hidden_states.dtype),
-            )
-
-        return (
-            output_hidden_states,
-            use_cache,
-            return_dict,
-            input_shape,
-            hidden_states,
-            attention_mask,
-            position_ids,
-            rope_cos_sin,
-            past_key_values,
-        )
-
-    def _get_mask_value(self, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
-        # torch.where expects a tensor. We use a cache to avoid recreating it every time.
-        if self.mask_value is None or self.mask_value.dtype != dtype or self.mask_value.device != device:
-            self.mask_value = torch.full([], torch.finfo(torch.float16).min, dtype=dtype, device=device)
-        return self.mask_value
-
-
-class GraniteForCausalLM(GranitePreTrainedModel):
-    _keys_to_ignore_on_load_missing = ["lm_head.weight"]
-
-    def __init__(self, config: GraniteConfig, **kwargs) -> None:
-        super().__init__(config, **kwargs)
-        self.transformer = GraniteModel(config, **kwargs)
-        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.transformer.wte
-
-    def set_input_embeddings(self, value: nn.Embedding) -> None:
-        self.transformer.wte = value
-
-    def get_output_embeddings(self) -> nn.Linear:
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
-        self.lm_head = new_embeddings
-
-    # FIXME typing
-    def prepare_inputs_for_generation(
-        self,
-        input_ids: torch.Tensor,
-        past_key_values: Optional[DynamicCache] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        **kwargs,
-    ) -> dict:
-        token_type_ids = kwargs.get("token_type_ids", None)
-        # Omit tokens covered by past_key_values
-        if past_key_values:
-            past_length = past_key_values.get_seq_length()
-
-            # Some generation methods already pass only the last input ID
-            if input_ids.shape[1] > past_length:
-                remove_prefix_length = past_length
-            else:
-                # Default to old behavior: keep only final ID
-                remove_prefix_length = input_ids.shape[1] - 1
-
-            input_ids = input_ids[:, remove_prefix_length:]
-            if token_type_ids is not None:
-                token_type_ids = token_type_ids[:, -input_ids.shape[1] :]
-
-        attention_mask = kwargs.get("attention_mask", None)
-        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, 0)
-            if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
-        else:
-            position_ids = None
-
-        # 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(
-            {
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "position_ids": position_ids,
-                "attention_mask": attention_mask,
-                "token_type_ids": token_type_ids,
-            }
-        )
-        return model_inputs
-
-    def forward(
-        self,
-        input_ids: Optional[Union[torch.Tensor]] = None,
-        past_key_values: Optional[DynamicCache] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[Union[torch.Tensor]] = None,
-        position_ids: Optional[Union[torch.Tensor]] = None,
-        inputs_embeds: Optional[Union[torch.Tensor]] = None,
-        labels: Optional[Union[torch.Tensor]] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithCrossAttentions]:
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # ==========================================================================================
-        # input_ids -> (batch_size, query_length)
-        # attention_mask -> None or (batch_size, key_length)
-        # position_ids -> None or (batch_size, key_length)
-        # ==========================================================================================
-
-        transformer_outputs = self.transformer(
-            input_ids,
-            past_key_values=past_key_values,
-            attention_mask=attention_mask,
-            token_type_ids=token_type_ids,
-            position_ids=position_ids,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-
-        lm_logits = self.lm_head(hidden_states)
-
-        loss = None
-        # Shift so that tokens < n predict n
-        if labels is not None:
-            shift_logits = lm_logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous().to(shift_logits.device)
-
-            # Flatten the tokens
-            loss_fct = nn.CrossEntropyLoss()
-            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
-
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return CausalLMOutputWithCrossAttentions(
-            loss=loss,
-            logits=lm_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )