Upload Moondream

config.json

@@ -8,8 +8,8 @@
   },
   "model_type": "moondream1",
   "phi_config": {
-    "model_type": "phi-msft"
+    "model_type": "phi"
   },
   "torch_dtype": "float16",
-  "transformers_version": "4.36.2"
+  "transformers_version": "4.38.2"
 }

configuration_moondream.py

@@ -5,65 +5,92 @@ import math
 
 
 class PhiConfig(PretrainedConfig):
-    model_type = "phi-msft"
+    model_type = "phi"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,
-        vocab_size: int = 51200,
-        n_positions: int = 2048,
-        n_embd: int = 2048,
-        n_layer: int = 24,
-        n_inner: Optional[int] = None,
-        n_head: int = 32,
-        n_head_kv: Optional[int] = None,
-        rotary_dim: Optional[int] = 32,
-        activation_function: Optional[str] = "gelu_new",
-        flash_attn: bool = False,
-        flash_rotary: bool = False,
-        fused_dense: bool = False,
-        attn_pdrop: float = 0.0,
-        embd_pdrop: float = 0.0,
-        resid_pdrop: float = 0.0,
-        layer_norm_epsilon: float = 1e-5,
-        initializer_range: float = 0.02,
-        tie_word_embeddings: bool = False,
-        pad_vocab_size_multiple: int = 64,
-        gradient_checkpointing: bool = False,
-        **kwargs
+        vocab_size=51200,
+        hidden_size=2048,
+        intermediate_size=8192,
+        num_hidden_layers=24,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        resid_pdrop=0.0,
+        embd_pdrop=0.0,
+        attention_dropout=0.0,
+        hidden_act="gelu_new",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        layer_norm_eps=1e-5,
+        use_cache=True,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        partial_rotary_factor=0.5,
+        qk_layernorm=False,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs,
     ):
-        pad_vocab_size = (
-            math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
-        )
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attention_dropout = attention_dropout
+        self.hidden_act = hidden_act
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.partial_rotary_factor = partial_rotary_factor
+        self.qk_layernorm = qk_layernorm
+        self._rope_scaling_validation()
+
         super().__init__(
-            vocab_size=pad_vocab_size,
-            n_positions=n_positions,
-            n_embd=n_embd,
-            n_layer=n_layer,
-            n_inner=n_inner,
-            n_head=n_head,
-            n_head_kv=n_head_kv,
-            activation_function=activation_function,
-            attn_pdrop=attn_pdrop,
-            embd_pdrop=embd_pdrop,
-            resid_pdrop=resid_pdrop,
-            layer_norm_epsilon=layer_norm_epsilon,
-            initializer_range=initializer_range,
-            pad_vocab_size_multiple=pad_vocab_size_multiple,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
             tie_word_embeddings=tie_word_embeddings,
-            gradient_checkpointing=gradient_checkpointing,
-            **kwargs
+            **kwargs,
         )
-        self.rotary_dim = min(rotary_dim, n_embd // n_head)
-        self.flash_attn = flash_attn
-        self.flash_rotary = flash_rotary
-        self.fused_dense = fused_dense
 
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "n_layer",
-    }
+    # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if (
+            rope_scaling_factor is None
+            or not isinstance(rope_scaling_factor, float)
+            or rope_scaling_factor <= 1.0
+        ):
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}"
+            )
 
 
 class MoondreamConfig(PretrainedConfig):

generation_config.json

@@ -1,4 +1,4 @@
 {
   "_from_model_config": true,
-  "transformers_version": "4.36.2"
+  "transformers_version": "4.38.2"
 }

modeling_phi.py

@@ -1,720 +1,1221 @@
-# Copyright (c) Microsoft Corporation.
-# Licensed under the MIT license.
+# coding=utf-8
+# Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
 #
-# Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
-# Licensed under the BSD 3-Clause License.
+# 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.
+
+""" PyTorch Phi model."""
 
-from dataclasses import dataclass, field
-from typing import Any, Dict, Optional, Union, Tuple
 
 import math
+from typing import List, Optional, Tuple, Union
+
 import torch
-import torch.nn as nn
-from einops import rearrange, repeat
-from transformers import PretrainedConfig, PreTrainedModel
-from transformers.activations import ACT2FN
-from transformers.modeling_outputs import CausalLMOutputWithPast
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+)
 from .configuration_moondream import PhiConfig
 
-FusedDense = None
 
+try:  # noqa: SIM105
+    if is_flash_attn_2_available():
+        from flash_attn import flash_attn_func, flash_attn_varlen_func
+        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input
+except ImportError:
+    # Workaround for https://github.com/huggingface/transformers/issues/28459,
+    # don't move to contextlib.suppress(ImportError)
+    pass
 
-@dataclass
-class InferenceParams:
-    max_seqlen: int
-    max_batch_size: int
-    seqlen_offset: int = 0
-    batch_size_offset: int = 0
-    key_value_memory_dict: Dict[str, Any] = field(default_factory=dict)
-    lengths_per_sample: torch.Tensor = None
 
+logger = logging.get_logger(__name__)
 
-class Embedding(nn.Module):
-    def __init__(self, config: PretrainedConfig):
-        super().__init__()
-        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
-        self.drop = nn.Dropout(config.embd_pdrop)
 
-    def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
-        return self.drop(self.wte(input_ids.view(-1, input_ids.size(-1))))
-
-
-def _apply_rotary_emb(x, cos, sin):
-    seqlen, rotary_dim = x.size(1), cos.size(1) * 2
-    x_rot, x_pass = x[..., :rotary_dim], x[..., rotary_dim:]
-    x1, x2 = x_rot.chunk(2, dim=-1)
-    c, s = cos[:seqlen].unsqueeze(1), sin[:seqlen].unsqueeze(1)
-    x_rot = torch.cat([x1 * c - x2 * s, x1 * s + x2 * c], dim=-1)
-    return torch.cat([x_rot.to(x.dtype), x_pass], dim=-1)
-
-
-def _apply_rotary_emb_kv(
-    kv: torch.FloatTensor, cos: torch.FloatTensor, sin: torch.FloatTensor
-) -> torch.FloatTensor:
-    seqlen, rotary_dim = kv.shape[1], cos.shape[-1] * 2
-    k_rot = kv[:, :, 0, :, :rotary_dim].chunk(2, dim=-1)
-    k_pass = kv[:, :, 0, :, rotary_dim:]
-    c, s = cos[:seqlen].unsqueeze(1), sin[:seqlen].unsqueeze(1)
-    k_rot = torch.cat(
-        [k_rot[0] * c - k_rot[1] * s, k_rot[0] * s + k_rot[1] * c], dim=-1
-    )
-    return torch.cat(
-        [torch.cat([k_rot, k_pass], dim=-1).unsqueeze(2), kv[:, :, 1:2, :, :]], dim=2
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    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.torch.int32), (1, 0)
     )
-
-
-def _apply_rotary_emb_qkv(
-    qkv: torch.FloatTensor, cos: torch.FloatTensor, sin: torch.FloatTensor
-) -> torch.FloatTensor:
-    seqlen, rotary_dim = qkv.shape[1], cos.shape[1] * 2
-
-    c = cos[:seqlen].unsqueeze(1)
-    s = sin[:seqlen].unsqueeze(1)
-
-    qkv_rot = torch.stack(
-        [
-            torch.cat(
-                [
-                    qkv[:, :, i, :, : rotary_dim // 2] * c
-                    - qkv[:, :, i, :, rotary_dim // 2 : rotary_dim] * s,
-                    qkv[:, :, i, :, : rotary_dim // 2] * s
-                    + qkv[:, :, i, :, rotary_dim // 2 : rotary_dim] * c,
-                ],
-                dim=-1,
-            ).to(qkv.dtype)
-            for i in range(2)
-        ],
-        dim=2,
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
     )
 
-    qkv_pass = qkv[:, :, :2, :, rotary_dim:].unsqueeze(2)
-    qkv_v = qkv[:, :, 2:3, :, :]
-    return torch.cat([qkv_rot, qkv_pass, qkv_v], dim=2)
 
-
-class RotaryEmbedding(nn.Module):
-    # Enhanced Transformer with Rotary Position Embedding (https://arxiv.org/pdf/2104.09864.pdf)
-    def __init__(
-        self,
-        dim: int,
-        base: int = 10000,
-        scale_base: Optional[float] = None,
-        pos_idx_in_fp32: bool = True,
-        max_position_embeddings: int = 2048,
-        device: Optional[str] = None,
-    ) -> None:
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Phi
+class PhiRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         super().__init__()
-        # fp32 is preferred since the output of `torch.arange` can be quite large and bf16 would lose a lot of precision
-        self.dim, self.base, self.pos_idx_in_fp32, self.device = (
-            dim,
-            float(base),
-            pos_idx_in_fp32,
-            device,
-        )
-        self.max_position_embeddings = max_position_embeddings
-        if scale_base is not None:
-            raise NotImplementedError
 
-        # Generate and register the non-trainable buffers
-        self.register_buffer(
-            "inv_freq", self._compute_inv_freq(device), persistent=False
+        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).float().to(device) / self.dim)
         )
-        self.register_buffer(
-            "scale", self._calculate_scale(dim, scale_base, device), persistent=False
+        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(),
         )
-        self._update_cos_sin_cache(
-            max_position_embeddings, device=device, dtype=torch.float32
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
         )
 
-    def _calculate_scale(self, dim, scale_base, device):
+        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().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_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=x.dtype)
+
         return (
-            (
-                (
-                    torch.arange(0, dim, 2, device=device, dtype=torch.float32)
-                    + 0.4 * dim
-                )
-                / (1.4 * dim)
-            )
-            if scale_base is not None
-            else None
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
         )
 
-    def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
-        return 1.0 / (
-            self.base
-            ** (
-                torch.arange(0, self.dim, 2, device=device, dtype=torch.float32)
-                / self.dim
-            )
-        )
 
-    def _update_cos_sin_cache(
+# Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Phi
+class PhiLinearScalingRotaryEmbedding(PhiRotaryEmbedding):
+    """PhiRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(
         self,
-        seqlen: int,
-        device: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-    ) -> None:
-        self._seq_len_cached = seqlen
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
         t = torch.arange(
-            seqlen,
-            device=device,
-            dtype=torch.float32 if self.pos_idx_in_fp32 else self.inv_freq.dtype,
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
         )
-        inv_freq = (
-            self._compute_inv_freq(device=device)
-            if self.pos_idx_in_fp32 and self.inv_freq.dtype != torch.float32
-            else self.inv_freq
+        t = t / self.scaling_factor
+
+        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().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Phi
+class PhiDynamicNTKScalingRotaryEmbedding(PhiRotaryEmbedding):
+    """PhiRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings)
+                - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
         )
 
-        freqs = torch.outer(t, inv_freq)
+        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().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Phi
+class PhiMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv with llama->phi
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
 
-        def apply_scale(freqs, scale, operator, dtype):
-            result = operator(freqs)
-            return (result / scale).to(dtype) if scale is not None else result.to(dtype)
+class PhiAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
 
-        if scale := self.scale:
-            power = (
-                torch.arange(seqlen, dtype=scale.dtype, device=scale.device)
-                - seqlen // 2
-            ) / self.scale_base
-            scale = scale.to(device=power.device) ** power.unsqueeze(1)
+    def __init__(self, config: PhiConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
 
-        self._cos_cached = apply_scale(
-            freqs, 1 / scale if scale is not None else None, torch.cos, dtype
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.partial_rotary_factor = config.partial_rotary_factor
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.Wqkv = nn.Linear(
+            self.hidden_size, 3 * self.num_heads * self.head_dim, bias=True
         )
-        self._sin_cached = apply_scale(
-            freqs, 1 / scale if scale is not None else None, torch.sin, dtype
+        self.out_proj = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=True
         )
-        if scale is not None:
-            self._cos_k_cached = apply_scale(freqs, scale, torch.cos, dtype)
-            self._sin_k_cached = apply_scale(freqs, scale, torch.sin, dtype)
+
+        self.qk_layernorm = config.qk_layernorm
+        if self.qk_layernorm:
+            self.q_layernorm = nn.LayerNorm(
+                config.hidden_size // self.num_heads,
+                eps=config.layer_norm_eps,
+                elementwise_affine=True,
+            )
+            self.k_layernorm = nn.LayerNorm(
+                config.hidden_size // self.num_heads,
+                eps=config.layer_norm_eps,
+                elementwise_affine=True,
+            )
+
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = PhiRotaryEmbedding(
+                int(self.partial_rotary_factor * self.head_dim),
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = PhiLinearScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * self.head_dim),
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = PhiDynamicNTKScalingRotaryEmbedding(
+                    int(self.partial_rotary_factor * self.head_dim),
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def forward(
         self,
-        qkv: torch.Tensor,
-        kv: Optional[torch.Tensor] = None,
-        seqlen_offset: int = 0,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        should_update = (
-            self._seq_len_cached < qkv.shape[1] + seqlen_offset
-            or self._cos_cached.device != qkv.device
-            or self._cos_cached.dtype != qkv.dtype
-            or (self.training and self._cos_cached.is_inference())
-        )
-
-        if should_update:
-            self._update_cos_sin_cache(
-                qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        if self.qk_layernorm:
+            query_states = self.q_layernorm(query_states)
+            key_states = self.k_layernorm(key_states)
+
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
             )
 
-        offset_cos = self._cos_cached[seqlen_offset:]
-        offset_sin = self._sin_cached[seqlen_offset:]
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
 
-        if kv is None:
-            return _apply_rotary_emb_qkv(qkv, offset_cos, offset_sin)
-        else:
-            return _apply_rotary_emb(qkv, offset_cos, offset_sin), _apply_rotary_emb_kv(
-                kv, offset_cos, offset_sin
+        # Queries and keys upcast to fp32 is required by Phi-2 to avoid overflow
+        attn_weights = torch.matmul(
+            query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
             )
 
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(value_states.dtype)
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.attention_dropout, training=self.training
+        )
 
-class MLP(nn.Module):
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        n_inner: Optional[int] = None,
-        act_fn: Optional[str] = None,
-    ) -> None:
-        super().__init__()
-        n_inner = n_inner or getattr(config, "n_inner", None) or 4 * config.n_embd
-        act_fn = act_fn or config.activation_function
+        attn_output = torch.matmul(attn_weights, value_states)
 
-        self.fc1 = nn.Linear(config.n_embd, n_inner)
-        self.fc2 = nn.Linear(n_inner, config.n_embd)
-        self.act = ACT2FN[act_fn]
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
 
-    def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
-        return self.fc2(self.act(self.fc1(hidden_states)))
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
 
+        attn_output = self.out_proj(attn_output)
 
-# Flash Attention (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py)
-class SelfAttention(nn.Module):
-    def __init__(
-        self,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        attention_dropout: float = 0.0,
-    ):
-        super().__init__()
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
+        if not output_attentions:
+            attn_weights = None
 
-    @torch.autocast("cpu", enabled=False)
-    @torch.autocast("cuda", enabled=False)
-    def forward(
-        self,
-        qkv: torch.FloatTensor,
-        causal: Optional[bool] = None,
-        key_padding_mask: Optional[torch.BoolTensor] = None,
-    ):
-        q, k, v = qkv.chunk(3, dim=-1)
-        scale = self.softmax_scale or 1.0 / q.size(-1) ** 0.5
+        return attn_output, attn_weights, past_key_value
 
-        scores = (
-            torch.einsum("bthd,bshd->bhts", q.to(torch.float32), k.to(torch.float32))
-            * scale
-        )
-        if causal or self.causal:
-            scores.triu_(1).fill_(-10000.0)
-        if key_padding_mask is not None:
-            scores.masked_fill_(key_padding_mask[:, None, None, :], -10000.0)
 
-        attn = self.drop(torch.softmax(scores, dim=-1).to(v.dtype))
-        return torch.einsum("bhts,bshd->bthd", attn, v)
+class PhiFlashAttention2(PhiAttention):
+    """
+    Phi flash attention module. This module inherits from `PhiAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
-# Flash Attention (https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py)
-class CrossAttention(nn.Module):
-    def __init__(self, causal=True, softmax_scale=None, attention_dropout=0.0):
-        super().__init__()
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
 
-    @torch.autocast("cpu", enabled=False)
-    @torch.autocast("cuda", enabled=False)
     def forward(
         self,
-        q: torch.FloatTensor,
-        kv: torch.FloatTensor,
-        causal: bool = None,
-        key_padding_mask: Optional[torch.BoolTensor] = None,
-    ) -> torch.FloatTensor:
-        batch_size, seqlen_q = q.shape[0], q.shape[1]
-        seqlen_k = kv.shape[1]
-
-        if kv.shape[3] != q.shape[2]:
-            kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
-        k, v = kv.unbind(dim=2)
-
-        q = q.to(torch.float32)
-        k = k.to(torch.float32)
-
-        causal = self.causal if causal is None else causal
-        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-
-        # Autocast is manually disabled to avoid `torch.einsum` performing the operation using float16, which might lead to overflow
-        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
-
-        if key_padding_mask is not None:
-            padding_mask = torch.full(
-                (batch_size, seqlen_k),
-                -10000.0,
-                dtype=scores.dtype,
-                device=scores.device,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # PhiFlashAttention2 attention does not support output_attentions
+
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states, key_states, value_states = self.Wqkv(hidden_states).chunk(
+            3, dim=-1
+        )
+
+        if self.qk_layernorm:
+            query_states = self.q_layernorm(query_states)
+            key_states = self.k_layernorm(key_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(
+            bsz, q_len, self.num_heads, self.head_dim
+        ).transpose(1, 2)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        # Partial rotary embedding
+        query_rot, query_pass = (
+            query_states[..., : self.rotary_emb.dim],
+            query_states[..., self.rotary_emb.dim :],
+        )
+        key_rot, key_pass = (
+            key_states[..., : self.rotary_emb.dim],
+            key_states[..., self.rotary_emb.dim :],
+        )
+        # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
+        query_rot, key_rot = apply_rotary_pos_emb(
+            query_rot, key_rot, cos, sin, position_ids
+        )
+
+        # [batch_size, seq_length, num_heads, head_dim]
+        query_states = torch.cat((query_rot, query_pass), dim=-1)
+        key_states = torch.cat((key_rot, key_pass), dim=-1)
+
+        if past_key_value is not None:
+            cache_kwargs = {
+                "sin": sin,
+                "cos": cos,
+                "partial_rotation_size": self.rotary_emb.dim,
+            }
+            key_states, value_states = past_key_value.update(
+                key_states, value_states, self.layer_idx, cache_kwargs
             )
-            padding_mask.masked_fill_(key_padding_mask, 0.0)
-            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
 
-        if causal:
-            rows = rearrange(
-                torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1"
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_dropout = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32.
+
+        if query_states.dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
             )
-            cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
-            causal_mask = cols > rows + seqlen_k - seqlen_q
-            scores = scores.masked_fill(causal_mask, -10000.0)
-
-        attention = torch.softmax(scores, dim=-1).to(v.dtype)
-        attention = self.drop(attention)
-        output = torch.einsum("bhts,bshd->bthd", attention, v)
-
-        return output
-
-
-def _find_mha_dims(
-    config: PretrainedConfig,
-    n_head: Optional[int] = None,
-    n_head_kv: Optional[int] = None,
-    head_dim: Optional[int] = None,
-) -> Tuple[int, int]:
-    if n_head is None and head_dim is None:
-        head_dim = config.n_embd // config.n_head
-        n_head = config.n_head
-    elif n_head is None or head_dim is None:
-        raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
-    if n_head_kv is None:
-        n_head_kv = getattr(config, "n_head_kv", None) or n_head
-    return n_head, n_head_kv, head_dim
-
-
-def _update_kv_cache(
-    kv: torch.FloatTensor, inference_params: InferenceParams, layer_idx: int
-) -> torch.FloatTensor:
-    num_heads, head_dim = kv.shape[-2:]
-    layer_memory = inference_params.key_value_memory_dict.setdefault(
-        layer_idx,
-        torch.empty(
-            inference_params.max_batch_size,
-            inference_params.max_seqlen,
-            2,
-            num_heads,
-            head_dim,
-            dtype=kv.dtype,
-            device=kv.device,
-        ),
-    )
 
-    batch_slice = slice(
-        inference_params.batch_size_offset,
-        inference_params.batch_size_offset + kv.shape[0],
-    )
-    seqlen_slice = slice(
-        inference_params.seqlen_offset, inference_params.seqlen_offset + kv.shape[1]
-    )
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=attn_dropout,
+            softmax_scale=None,
+        )
 
-    if seqlen_slice.stop >= inference_params.max_seqlen:
-        layer_memory = torch.cat((layer_memory, kv), dim=1)
-        inference_params.key_value_memory_dict[layer_idx] = layer_memory
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.out_proj(attn_output)
 
-    layer_memory[batch_slice, seqlen_slice, ...] = kv
-    return layer_memory[batch_slice, : seqlen_slice.stop, ...]
+        if not output_attentions:
+            attn_weights = None
 
+        return attn_output, attn_weights, past_key_value
 
-# Multi-head attention layer with rotary embeddings
-class MHA(nn.Module):
-    def __init__(
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
+    def _flash_attention_forward(
         self,
-        config,
-        dtype=None,
-        device=None,
-        rotary_dim=None,
-        rotary_base=10000.0,
-        rotary_scale_base=None,
-        n_head=None,
-        n_head_kv=None,
-        head_dim=None,
-        bias=True,
-        causal=True,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
         softmax_scale=None,
-        layer_idx=None,
-        return_residual=False,
-        checkpointing=False,
     ):
-        super().__init__()
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
 
-        # Set rotary embedding if specified
-        self.rotary_dim = rotary_dim or getattr(config, "rotary_dim", 0)
-        if self.rotary_dim:
-            self.rotary_emb = RotaryEmbedding(
-                self.rotary_dim,
-                base=rotary_base,
-                scale_base=rotary_scale_base,
-                device=device,
-                max_position_embeddings=config.n_positions,
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            (
+                query_states,
+                key_states,
+                value_states,
+                indices_q,
+                cu_seq_lens,
+                max_seq_lens,
+            ) = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
             )
 
-        # Determine MHA dims from arguments or config
-        self.n_head, self.n_head_kv, self.head_dim = _find_mha_dims(
-            config, n_head, n_head_kv, head_dim
-        )
-        op_size = self.head_dim * (self.n_head + 2 * self.n_head_kv)
-        hidden_size = config.n_embd
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
 
-        # Choose Linear class based on config, FusedDense is optional
-        LinearClass = (
-            FusedDense if config.fused_dense and FusedDense is not None else nn.Linear
-        )
-        self.Wqkv = LinearClass(
-            hidden_size, op_size, bias=bias, device=device, dtype=dtype
-        )
-        self.out_proj = LinearClass(
-            hidden_size, hidden_size, bias=bias, device=device, dtype=dtype
-        )
+            attn_output = pad_input(
+                attn_output_unpad, indices_q, batch_size, query_length
+            )
+        else:
+            attn_output = flash_attn_func(
+                query_states,
+                key_states,
+                value_states,
+                dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
 
-        # Initialize attention mechanisms
-        attn_kwargs = {
-            "causal": causal,
-            "softmax_scale": softmax_scale,
-            "attention_dropout": config.attn_pdrop,
-        }
-        self.inner_attn = SelfAttention(**attn_kwargs)
-        self.inner_cross_attn = CrossAttention(**attn_kwargs)
+        return attn_output
 
-        self.layer_idx = layer_idx
-        self.return_residual = return_residual
-        self.checkpointing = checkpointing
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
+    def _upad_input(
+        self, query_layer, key_layer, value_layer, attention_mask, query_length
+    ):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
 
-    def _forward_self_attn(
-        self, x: torch.FloatTensor, key_padding_mask: Optional[torch.BoolTensor]
-    ) -> torch.FloatTensor:
-        qkv = rearrange(
-            self.Wqkv(x), "... (three h d) -> ... three h d", three=3, d=self.head_dim
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
         )
-        if self.rotary_dim > 0:
-            qkv = self.rotary_emb(qkv)
-        attn_func = (
-            torch.utils.checkpoint.checkpoint
-            if self.checkpointing
-            else lambda f, *args, **kwargs: f(*args, **kwargs)
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
+            indices_k,
         )
-        return attn_func(self.inner_attn, qkv, key_padding_mask=key_padding_mask)
-
-    def _forward_cross_attn(
-        self,
-        x: torch.FloatTensor,
-        past_key_values: Optional[InferenceParams],
-        key_padding_mask: Optional[torch.BoolTensor],
-    ) -> torch.FloatTensor:
-        qkv = self.Wqkv(x)
-        q, kv = (
-            qkv[..., : self.n_head * self.head_dim],
-            qkv[..., self.n_head * self.head_dim :],
-        )
-        q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
-        kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim),
+                indices_k,
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(
+                query_layer, attention_mask
+            )
 
-        seqlen_offset = (
-            past_key_values.seqlen_offset if past_key_values is not None else 0
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
         )
-        causal = None if seqlen_offset == 0 else False
-        if self.rotary_dim > 0:
-            q, kv = self.rotary_emb(q, kv=kv, seqlen_offset=seqlen_offset)
 
-        if past_key_values is not None:
-            kv = _update_kv_cache(kv, past_key_values, self.layer_idx)
 
-        attn_func = (
-            torch.utils.checkpoint.checkpoint
-            if self.checkpointing
-            else lambda fn, *args, **kwargs: fn(*args, **kwargs)
-        )
+PHI_ATTENTION_CLASSES = {
+    "eager": PhiAttention,
+    "flash_attention_2": PhiFlashAttention2,
+}
 
-        return attn_func(
-            self.inner_cross_attn,
-            q,
-            kv,
-            key_padding_mask=key_padding_mask,
-            causal=causal,
-        )
 
-    def forward(
-        self,
-        x: torch.FloatTensor,
-        past_key_values: Optional[InferenceParams] = None,
-        attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
-    ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
-        attention_mask = attention_mask.bool() if attention_mask is not None else None
-        use_cross_attn = self.n_head != self.n_head_kv or past_key_values is not None
-        attn_output_function = (
-            self._forward_cross_attn if use_cross_attn else self._forward_self_attn
-        )
-        attn_output = (
-            attn_output_function(x, past_key_values, attention_mask)
-            if use_cross_attn
-            else attn_output_function(x, attention_mask)
-        )
-        output = self.out_proj(rearrange(attn_output, "... h d -> ... (h d)"))
-        return (output, x) if self.return_residual else output
-
-
-# Parallel block. This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
-class ParallelBlock(nn.Module):
-    def __init__(self, config: PretrainedConfig, block_idx: Optional[int] = None):
+class PhiDecoderLayer(nn.Module):
+    def __init__(self, config: PhiConfig, layer_idx: int):
         super().__init__()
-        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.mixer = PHI_ATTENTION_CLASSES[config._attn_implementation](
+            config, layer_idx=layer_idx
+        )
+        self.mlp = PhiMLP(config)
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
         self.resid_dropout = nn.Dropout(config.resid_pdrop)
-        self.block_idx = block_idx
-        self.mixer = MHA(config, layer_idx=block_idx)
-        self.mlp = MLP(config)
 
     def forward(
         self,
-        hidden_states: torch.FloatTensor,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-    ) -> torch.FloatTensor:
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`):
+                input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
+                `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            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`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
         residual = hidden_states
+
         hidden_states = self.ln(hidden_states)
 
-        attn_outputs = self.mixer(
-            hidden_states,
-            past_key_values=past_key_values,
+        # Self Attention
+        attn_outputs, self_attn_weights, present_key_value = self.mixer(
+            hidden_states=hidden_states,
             attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
         )
-        if isinstance(attn_outputs, tuple):
-            attn_outputs = attn_outputs[0]
-
         attn_outputs = self.resid_dropout(attn_outputs)
+
         feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
-        return attn_outputs + feed_forward_hidden_states + residual
+        hidden_states = attn_outputs + feed_forward_hidden_states + residual
+        outputs = (hidden_states,)
 
+        if output_attentions:
+            outputs += (self_attn_weights,)
 
-class CausalLMHead(nn.Module):
-    """Causal Language Modeling head. Simplified version."""
+        if use_cache:
+            outputs += (present_key_value,)
 
-    def __init__(self, config):
-        super().__init__()
-        self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.linear = nn.Linear(config.n_embd, config.vocab_size)
+        return outputs
 
-    def forward(self, hidden_states):
-        return self.linear(self.ln(hidden_states)).to(torch.float32)
 
+class PhiPreTrainedModel(PreTrainedModel):
+    config_class = PhiConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PhiDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
 
-# Improving Language Understanding by Generative Pre-Training
-# (https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)
-class CausalLMLoss(nn.Module):
-    def __init__(self, shift_labels: bool = True) -> None:
+
+class Embedding(nn.Module):
+    def __init__(self, config: PhiConfig):
         super().__init__()
-        self.shift_labels = shift_labels
-        self.loss_fct = nn.CrossEntropyLoss()
+        self.wte = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
 
-    def forward(
-        self, logits: torch.FloatTensor, labels: torch.LongTensor
-    ) -> torch.FloatTensor:
-        if self.shift_labels:
-            logits, labels = logits[..., :-1, :], labels[..., 1:]
-        return self.loss_fct(logits.reshape(-1, logits.size(-1)), labels.reshape(-1))
+    def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
+        return self.wte(input_ids)
 
 
-class PhiPreTrainedModel(PreTrainedModel):
-    config_class = PhiConfig
-    base_model_prefix = "transformer"
-    supports_gradient_checkpointing = False
-    _no_split_modules = ["ParallelBlock"]
+class PhiModel(PhiPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`PhiDecoderLayer`]
 
-    def __init__(self, *inputs, **kwargs) -> None:
-        super().__init__(*inputs, **kwargs)
+    Args:
+        config: PhiConfig
+    """
 
-    def prepare_inputs_for_generation(
+    def __init__(self, config: PhiConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embd = Embedding(config)
+        self.embed_dropout = nn.Dropout(config.embd_pdrop)
+        self.h = nn.ModuleList(
+            [
+                PhiDecoderLayer(config, layer_idx)
+                for layer_idx in range(config.num_hidden_layers)
+            ]
+        )
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embd.wte
+
+    def set_input_embeddings(self, value):
+        self.embd.wte = value
+
+    def forward(
         self,
         input_ids: torch.LongTensor = None,
-        inputs_embeds: torch.FloatTensor = None,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[Union[torch.LongTensor, torch.BoolTensor]] = None,
-        **kwargs,
-    ) -> Dict[str, Any]:
-        if input_ids is None and inputs_embeds is None:
-            raise ValueError(
-                "You have to specify either `input_ids` or `inputs_embeds`."
-            )
-
-        max_batch_size = (
-            inputs_embeds.shape[0] if inputs_embeds is not None else input_ids.shape[0]
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[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]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
         )
-        seqlen_offset = (
-            inputs_embeds.shape[1] + input_ids.shape[1] - 2
-            if inputs_embeds is not None
-            else input_ids.shape[1] - 1
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
         )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
 
-        args = (
-            {"inputs_embeds": inputs_embeds}
-            if inputs_embeds is not None
-            else {"input_ids": input_ids}
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
         )
 
-        if not isinstance(past_key_values, InferenceParams):
-            past_key_values = InferenceParams(
-                max_seqlen=self.config.n_positions,
-                max_batch_size=max_batch_size,
-                seqlen_offset=0,
-                batch_size_offset=0,
-                key_value_memory_dict={},
-                lengths_per_sample=None,
+        # retrieve input_ids and inputs_embeds
+        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:
+            batch_size, seq_length = input_ids.shape[:2]
+        elif inputs_embeds is not None:
+            batch_size, seq_length = inputs_embeds.shape[:2]
         else:
-            past_key_values.seqlen_offset = seqlen_offset
-            args = {"input_ids": input_ids[:, -1].unsqueeze(-1)}
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        return {
-            **args,
-            "past_key_values": past_key_values,
-            "attention_mask": attention_mask,
-        }
+        past_key_values_length = 0
 
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_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)
 
-class PhiModel(PhiPreTrainedModel):
-    _keys_to_ignore_on_load_missing = [""]
-    _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
+        if inputs_embeds is None:
+            inputs_embeds = self.embd(input_ids)
 
-    def __init__(self, config: PhiConfig) -> None:
-        super().__init__(config)
-        self.embd = Embedding(config)
-        self.h = nn.ModuleList(
-            [ParallelBlock(config, block_idx=i) for i in range(config.n_layer)]
-        )
-        self.gradient_checkpointing = config.gradient_checkpointing
-        self.post_init()
+        inputs_embeds = self.embed_dropout(inputs_embeds)
 
-    def get_input_embeddings(self) -> nn.Embedding:
-        return self.embd.wte
+        # Attention mask.
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            attention_mask = (
+                attention_mask
+                if (attention_mask is not None and 0 in attention_mask)
+                else None
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
 
-    def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
-        self.embd.wte = new_embeddings
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.h:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
 
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        inputs_embeds: torch.FloatTensor = None,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
-    ) -> torch.FloatTensor:
-        if (input_ids is None) == (inputs_embeds is None):
-            raise ValueError("Specify exactly one of `input_ids` or `inputs_embeds`.")
-        hidden_states = self.embd(input_ids) if input_ids is not None else inputs_embeds
-
-        for layer in self.h:
-            func = layer.__call__ if self.gradient_checkpointing else layer
-            args = (hidden_states, past_key_values, attention_mask)
-            hidden_states = (
-                torch.utils.checkpoint.checkpoint(func, *args, use_reentrant=True)
-                if self.gradient_checkpointing
-                else func(*args)
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = (
+                next_decoder_cache.to_legacy_cache()
+                if use_legacy_cache
+                else next_decoder_cache
+            )
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
             )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
 
-        return hidden_states
+
+class CausalLMHead(nn.Module):
+    """Causal Language Modeling head. Simplified version."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.linear = nn.Linear(config.hidden_size, config.vocab_size)
+
+    def forward(self, hidden_states):
+        return self.linear(self.ln(hidden_states))
 
 
 class PhiForCausalLM(PhiPreTrainedModel):
-    _keys_to_ignore_on_load_missing, _keys_to_ignore_on_load_unexpected = (
-        [""],
-        [r"transformer\.h\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"],
-    )
+    _tied_weights_keys = ["lm_head.linear.weight"]
 
-    def __init__(self, config: PhiConfig) -> None:
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Phi,bias=False->bias=True
+    def __init__(self, config):
         super().__init__(config)
         self.transformer = PhiModel(config)
+        self.vocab_size = config.vocab_size
         self.lm_head = CausalLMHead(config)
-        self.loss = CausalLMLoss()
+
+        # Initialize weights and apply final processing
         self.post_init()
 
-    def get_output_embeddings(self) -> nn.Linear:
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
+    def get_input_embeddings(self):
+        return self.transformer.embd.wte
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
+    def set_input_embeddings(self, value):
+        self.model.embd.wte = value
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
+    def get_output_embeddings(self):
         return self.lm_head.linear
 
-    def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
+    def set_output_embeddings(self, new_embeddings):
         self.lm_head.linear = new_embeddings
 
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
+    def get_decoder(self):
+        return self.model
+
     def forward(
         self,
         input_ids: torch.LongTensor = None,
-        inputs_embeds: torch.FloatTensor = None,
-        past_key_values: Optional[Union[torch.FloatTensor, InferenceParams]] = None,
-        attention_mask: Optional[torch.BoolTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.LongTensor] = None,
-        **kwargs,
-    ) -> CausalLMOutputWithPast:
-        hidden_states = self.transformer(
+        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"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, PhiForCausalLM
+
+        >>> model = PhiForCausalLM.from_pretrained("microsoft/phi-1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1")
+
+        >>> prompt = "This is an example script ."
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
+        ```"""
+
+        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
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.transformer(
             input_ids=input_ids,
-            inputs_embeds=inputs_embeds,
-            past_key_values=past_key_values,
             attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
         )
-        lm_logits = self.lm_head(hidden_states)
-        loss = self.loss(lm_logits, labels) if labels is not None else None
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
 
         return CausalLMOutputWithPast(
-            loss=loss, logits=lm_logits, past_key_values=past_key_values
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        **kwargs,
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if (
+                attention_mask is not None
+                and attention_mask.shape[1] > input_ids.shape[1]
+            ):
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        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[:, -input_ids.shape[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(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
         )
+        return model_inputs
+
+    @staticmethod
+    # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(
+                    past_state.index_select(0, beam_idx.to(past_state.device))
+                    for past_state in layer_past
+                ),
+            )
+        return reordered_past