modeling_llama.py

import copy
import math
from typing import List, Optional, Tuple, Union

import torch
import torch.nn.functional as F
from torch.distributed.distributed_c10d import ProcessGroup
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
from transformers.models.llama.modeling_llama import (
    LlamaAttention,
    LlamaDecoderLayer,
    LlamaForCausalLM,
    LlamaMLP,
    LlamaModel,
    LlamaRMSNorm,
    apply_rotary_pos_emb,
    logger,
)
from transformers.utils import is_torchdynamo_compiling

from .... import distributed, parallel_state
from ....distributed.strategy import DistributedStrategy, NoOpStrategy
from ....distributed.tensorparallel import (
    reduce_from_tensor_model_parallel_region,
)
from ....distributed.tp import TPModule
from ...modeling_attn_mask_utils import (
    _gaudi_prepare_4d_causal_attention_mask,
)
from ..modeling_all_models import Matmul, apply_customized_rope_module
from .configuration_llama import LlamaConfig


try:
    from habana_frameworks.torch.hpex.kernels import RotaryPosEmbeddingHelperV2 as FusedRoPE  # noqa

    has_fused_rope = True
except ImportError:
    has_fused_rope = False
    print("Not using HPU fused kernel for apply_rotary_pos_emb")

try:
    from habana_frameworks.torch.hpex.normalization import FusedRMSNorm as FusedRMSNorm

    has_fused_rms_norm = True
except ImportError:
    has_fused_rms_norm = False
    print("Not using HPU fused kernel for RMSNorm")

try:
    from habana_frameworks.torch.hpex.kernels import FusedSDPA
except ImportError:
    print("Not using HPU fused scaled dot-product attention kernel.")
    FusedSDPA = None

import habana_frameworks.torch.core as htcore


def gaudi_llama_rmsnorm_forward(self, hidden_states):
    """
    Copied from LlamaRMSNorm.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
    The only differences are:
        - override RMSNorm with Habana fused RMSNorm
    """
    if hidden_states.device.type == "hpu" and has_fused_rms_norm:
        # mixed dtypes are not good for FusedRMSNorm, both inputs need to have same dtype
        if hidden_states.dtype != self.weight.dtype:
            orig_dtype = hidden_states.dtype
            hidden_states = FusedRMSNorm.apply(hidden_states.to(self.weight.dtype), self.weight, self.variance_epsilon)
            return hidden_states.to(orig_dtype)
        else:
            hidden_states = FusedRMSNorm.apply(hidden_states, self.weight, self.variance_epsilon)
            return hidden_states
    else:
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


class GaudiLlamaRotaryEmbedding(torch.nn.Module):
    def __init__(
        self,
        dim=None,
        max_position_embeddings=2048,
        base=10000,
        device=None,
        scaling_factor=1.0,
        rope_type="default",
        config: Optional[LlamaConfig] = None,
    ):
        super().__init__()

        # TODO (joao): remove the `if` below, only used for BC
        self.rope_kwargs = {}
        if config is None:
            logger.warning_once(
                "`LlamaRotaryEmbedding` can now be fully parameterized by passing the model config through the "
                "`config` argument. All other arguments will be removed in v4.46"
            )
            self.rope_kwargs = {
                "rope_type": rope_type,
                "factor": scaling_factor,
                "dim": dim,
                "base": base,
                "max_position_embeddings": max_position_embeddings,
            }
            self.rope_type = rope_type
            self.max_seq_len_cached = max_position_embeddings
            self.original_max_seq_len = max_position_embeddings
        else:
            # BC: "rope_type" was originally "type"
            if config.rope_scaling is not None:
                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
            else:
                self.rope_type = "default"
            self.max_seq_len_cached = config.max_position_embeddings
            self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=self.max_seq_len_cached, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

    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)

        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 _dynamic_frequency_update(self, seq_len, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        # seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(
                self.config, device, seq_len=seq_len, **self.rope_kwargs
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]

        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(seq_len, device=x.device)

        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        if self.attention_scaling == 1.0:
            return (
                self._cos_cached[:seq_len].to(dtype=x.dtype),
                self._sin_cached[:seq_len].to(dtype=x.dtype),
            )
        else:
            return (
                self._cos_cached[:seq_len].to(dtype=x.dtype) * self.attention_scaling,
                self._sin_cached[:seq_len].to(dtype=x.dtype) * self.attention_scaling,
            )


class GaudiLlamaLinearScalingRotaryEmbedding(GaudiLlamaRotaryEmbedding):
    def __init__(self, *args, **kwargs):
        logger.warning_once(
            "`LlamaLinearScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use "
            "`LlamaRotaryEmbedding`, which now also does linear scaling (simply pass the model config to __init__)."
        )
        kwargs["rope_type"] = "linear"
        super().__init__(*args, **kwargs)

    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)
        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)


class GaudiLlamaDynamicNTKScalingRotaryEmbedding(GaudiLlamaRotaryEmbedding):
    def __init__(self, *args, **kwargs):
        logger.warning_once(
            "`LlamaDynamicNTKScalingRotaryEmbedding` is deprecated an will be removed in v4.46. Please use "
            "`LlamaRotaryEmbedding`, which now also does dynamic ntk scaling (simply pass the model config to "
            "__init__)."
        )
        kwargs["rope_type"] = "dynamic"
        super().__init__(*args, **kwargs)

    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, 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)


class GaudiLlamaMLP(LlamaMLP):
    def __init__(self, config):
        super(LlamaMLP, self).__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.gate_proj = torch.nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.up_proj = torch.nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
        self.down_proj = torch.nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
        self.act_fn = ACT2FN[config.hidden_act]

    def pre_mlp_forward(self, x):
        if self.config.pretraining_tp > 1:
            slice = self.intermediate_size // self.config.pretraining_tp
            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
            down_proj_slices = self.down_proj.weight.split(slice, dim=1)

            gate_proj = torch.cat(
                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
            )
            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)

            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
            down_proj = [
                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
            ]
            output = sum(down_proj)
        else:
            input = self.act_fn(self.gate_proj(x)) * self.up_proj(x)
            output = self.down_proj(input)
        return output

    def mlp_all_reduce(self, x):
        if hasattr(self.down_proj, "all_reduce"):
            self.down_proj.all_reduce(x)

    def post_mlp_forward(self, x):
        if self.config.pretraining_tp > 1:
            return x
        if hasattr(self.down_proj, "post_all_reduce"):
            return self.down_proj.post_all_reduce(x)
        return x


class TPGaudiLlamaMLP(GaudiLlamaMLP, TPModule):
    def __init__(
        self,
        config,
        group: Optional[ProcessGroup] = None,
    ):
        assert torch.distributed.is_initialized()
        rank, world_size = distributed.rank_and_world(group)
        hidden_dim = int(config.hidden_grow_factor * config.hidden_size)
        assert hidden_dim % world_size == 0, "Hidden dim must be divisible by world size"

        self.config = copy.deepcopy(config)
        self.config.intermediate_size = int((config.hidden_grow_factor / world_size) * config.hidden_size)
        GaudiLlamaMLP.__init__(self, self.config)
        self.setup_tp(rank, world_size)

    def colwise_param_names(self) -> List[str]:
        return ["up_proj", "gate_proj"]

    def rowwise_param_names(self) -> List[str]:
        return ["down_proj"]

    @staticmethod
    def import_module(glu: GaudiLlamaMLP, group: ProcessGroup) -> "TPGaudiLlamaMLP":
        config = copy.deepcopy(glu.config)
        config.hidden_grow_factor = glu.config.intermediate_size / glu.config.hidden_size
        tp_glu = TPGaudiLlamaMLP(config=config, group=group)
        return tp_glu

    def pre_mlp_forward(self, x):
        out_par = GaudiLlamaMLP.pre_mlp_forward(self, x)
        return reduce_from_tensor_model_parallel_region(out_par)


def gaudi_llama_repeat_kv(
    query_states: torch.Tensor,
    key_states: torch.Tensor,
    value_states: torch.Tensor,
    attention_mask: torch.Tensor,
    n_rep: int,
):
    """
    Copied from repeat_kv: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
    The only differences are:
        - Append num_key_value_heads == 1 check as kv states can be broadcasted during matmuls so need to expand and reshape them.
        - Add new args query_states, key_states, value_states and attention_mask and update the logic for expansion.
    The query states go from (batch, num_heads, seqlen, head_dim) to (batch, num_key_value_heads, n_rep, seqlen, head_dim)
    The key/value states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_key_value_heads, 1, seqlen, head_dim)
    """
    batch, num_key_value_heads, kv_len, head_dim = key_states.shape
    if n_rep == 1 or num_key_value_heads == 1:
        return query_states, key_states, value_states, attention_mask

    new_kv_shape = (batch, num_key_value_heads, 1, kv_len, head_dim)
    key_states = key_states.reshape(new_kv_shape)
    value_states = value_states.reshape(new_kv_shape)

    batch, _, q_len, head_dim = query_states.shape
    new_q_shape = (batch, num_key_value_heads, n_rep, q_len, head_dim)
    query_states = query_states.reshape(new_q_shape)

    if attention_mask is not None:
        # Add groups dim and set to 1
        attention_mask = attention_mask.unsqueeze(1)

    return query_states, key_states, value_states, attention_mask


# FusedScaledDotProductAttention
class ModuleFusedSDPA(torch.nn.Module):
    def __init__(self, fusedSDPA, scale, attention_dropout, enable_recompute, flash_attention_fp8):
        super().__init__()
        self._hpu_kernel_fsdpa = fusedSDPA
        self.scale = scale
        self.attention_dropout = attention_dropout
        self.enable_recompute = enable_recompute
        self.flash_attention_fp8 = flash_attention_fp8

    def forward(
        self,
        query,
        key,
        value,
        attn_mask,
        dropout_p,
        is_causal,
        scale,
        softmax_mode,
        recompute_mode,
        valid_sequence_lengths,
        padding_side="left",
    ):
        return self._hpu_kernel_fsdpa.apply(
            query,
            key,
            value,
            attn_mask,
            dropout_p,
            is_causal,
            scale,
            softmax_mode,
            recompute_mode,
            valid_sequence_lengths,
            padding_side,
        )


class KVCache(torch.nn.Module):
    def __init__(self):
        super(KVCache, self).__init__()
        self.cache = None
        self.inp_seq_len = -1

    def allocate(self, inp_seq_len, dtype, device, shape):
        if self.cache is None or self.cache.shape != shape:
            self.inp_seq_len = inp_seq_len
            self.cache = torch.zeros(shape, dtype=dtype, device=device)
        else:
            assert (
                self.inp_seq_len == inp_seq_len
            ), f"inp_seq_len must be the same. self.inp_seq_len:{self.inp_seq_len} inp_seq_len:{inp_seq_len}"
            self.cache.fill_(0)

    @staticmethod
    def update(prev, cur, dim, idx, inp_seq_len):
        orig_cur = cur
        if prev.shape == cur.shape:
            prev.copy_(cur)
            return orig_cur
        if idx is not None and cur.shape[2] > 1 and cur.shape[2] <= prev.shape[2]:
            # Initialize
            prev[:, :, :inp_seq_len, :].copy_(cur)
            return orig_cur
        if idx is not None:
            prev.index_copy_(dim, idx - 1, cur)
            return prev
        else:
            return torch.cat((prev, cur), dim=dim)

    def get_shape(self):
        if self.cache is None:
            return None
        return self.cache.shape

    def forward(self, cur, dim, idx):
        return self.update(self.cache, cur, dim, idx, self.inp_seq_len)


def GaudiDistributedAttention(fused_scaled_dot_product_attention, fused_scaled_dot_product_attention_distributed):
    if parallel_state.sequence_parallel_is_initialized() and parallel_state.get_sequence_parallel_world_size() > 1:
        return fused_scaled_dot_product_attention_distributed
    else:
        return fused_scaled_dot_product_attention


class GaudiLlamaAttention(LlamaAttention):
    def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
        super().__init__(config, layer_idx)

        self.matmul_qk = Matmul()
        self.matmul_av = Matmul()
        self.k_cache = KVCache()
        self.v_cache = KVCache()

        if hasattr(config, "fused_qkv") and config.fused_qkv:
            self.num_heads = config.num_attention_heads
            self.head_dim = config.hidden_size // self.num_heads
            self.dim1 = self.num_heads * self.head_dim
            self.dim2 = config.num_key_value_heads * self.head_dim
            self.qkv_proj = torch.nn.Linear(
                self.hidden_size,
                self.dim1 + 2 * self.dim2,
                bias=config.attention_bias,
            )
            self.q_proj = None
            self.k_proj = None
            self.v_proj = None
        self.inp_seq_len = -1
        self.norm_factor = 1.0 / math.sqrt(self.head_dim)
        self.fused_scaled_dot_product_attention = (
            ModuleFusedSDPA(
                FusedSDPA,
                scale=self.norm_factor,
                attention_dropout=self.attention_dropout,
                enable_recompute=False,
                flash_attention_fp8=getattr(config, "flash_attention_fp8", False),
            )
            if FusedSDPA
            else None
        )
        # https://github.com/microsoft/DeepSpeed/issues/4359
        # for all2all comm, Distributed Attention cares about sequence (s) and number of heads (h) dimensions. In HPU, they are at 1 and 2 indices
        self.fused_scaled_dot_product_attention_distributed = None
        if parallel_state.sequence_parallel_is_initialized() and parallel_state.get_sequence_parallel_world_size() > 1:
            from deepspeed.sequence.layer import DistributedAttention

            self.fused_scaled_dot_product_attention_distributed = DistributedAttention(
                self.fused_scaled_dot_product_attention, parallel_state.get_sequence_parallel_group(), 1, 2
            )

    def get_k_proj_weight(self):
        """4bit quantization in GPTQ replaces the k_proj.weight with qweight."""
        if hasattr(self.k_proj, "qweight"):
            return self.k_proj.qweight
        return self.k_proj.weight

    def get_k_proj_weight_dtype(self):
        """4bit quantization in GPTQ replaces the k_proj.weight with qweight.
        Scales tensor gets the weight dtype."""
        if hasattr(self.k_proj, "qweight"):
            return self.k_proj.scales.dtype
        return self.k_proj.weight.dtype

    def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        cache_shape = (batch_size, self.num_key_value_heads, max_seq_len, self.head_dim)
        device = self.get_k_proj_weight().device
        dtype = self.config.torch_dtype
        self.k_cache.allocate(inp_seq_len, dtype, device, cache_shape)
        self.v_cache.allocate(inp_seq_len, dtype, device, cache_shape)

    def update_sincos_cache(self, seq_len):
        # Call rotary emb forward() to update cos/sin cache when infering more than self.max_position_embeddings
        # This helps in avoiding creation of these caches during actual model forward pass and
        # reduce memory consumption and improve performance.
        if seq_len > self.max_position_embeddings:
            self.max_position_embeddings = seq_len
            _, _ = self.rotary_emb(self.get_k_proj_weight(), seq_len=seq_len)

    def reorder(self, tensor, beam_idx, dim_a, dim_b):
        updated = tensor.index_select(0, beam_idx)
        tensor.copy_(updated)

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        if self.k_cache.cache is None:
            return (None, None)

        head_dim = self.k_cache.cache.size(-1)
        seq_length = self.k_cache.cache.size(-2)
        self.reorder(self.k_cache.cache, beam_idx, seq_length, head_dim)
        self.reorder(self.v_cache.cache, beam_idx, seq_length, head_dim)
        return (self.k_cache.cache.shape, self.v_cache.cache.shape)

    def pre_attn_forward(
        self,
        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,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
        token_idx: Optional[torch.Tensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        reuse_cache: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: Optional[torch.Tensor] = None,
        cache_idx: int = None,
        num_virtual_tokens: int = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """
        Copied from LlamaAttention.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
        The only differences are:
        - add new args token_idx
        - optimize KV cache
        - add new args attn_softmax_bf16
        - add new args reuse_cache
        - add new args use_flash_attention
        - add new arg flash_attention_recompute
        - add new arg flash_attention_causal_mask
        - add new arg flash_attention_fast_softmax
        - add new arg num_virtual_tokens
        """
        bsz, q_len, _ = hidden_states.size()

        if self.config.pretraining_tp > 1:
            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
            query_slices = self.q_proj.weight.split(
                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
            )
            key_slices = self.get_k_proj_weight().split(key_value_slicing, dim=0)
            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)

            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
            query_states = torch.cat(query_states, dim=-1)

            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
            key_states = torch.cat(key_states, dim=-1)

            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
            value_states = torch.cat(value_states, dim=-1)

        else:
            if hasattr(self.config, "fused_qkv") and self.config.fused_qkv:
                qkv_states = self.qkv_proj(hidden_states)
                query_states, key_states, value_states = torch.split(
                    qkv_states, [self.dim1, self.dim2, self.dim2], dim=-1
                )
            else:
                query_states = self.q_proj(hidden_states)
                key_states = self.k_proj(hidden_states)
                value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        # TODO: update when auto mp params is enabled in DeepSpeed (cf. https://github.com/HabanaAI/DeepSpeed/blob/94309c7b5dfc1a69858f5c9f25737b2f81a332a5/deepspeed/module_inject/replace_module.py#L440)
        key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            if token_idx is None:
                if hasattr(past_key_value, "get_usable_length"):
                    kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
                else:
                    kv_seq_len += past_key_value[0].shape[-2]
            else:
                if reuse_cache and not isinstance(past_key_value[0], torch.Tensor):
                    kv_seq_len = past_key_value[0][-2]
                else:
                    if num_virtual_tokens is not None and num_virtual_tokens == past_key_value[0].shape[-2]:
                        kv_seq_len = past_key_value[0].shape[-2] + kv_seq_len
                    else:
                        kv_seq_len = past_key_value[0].shape[-2]

        # TODO: the following section cause torch.compile performance issue with graph recompilation
        # as we are not using position_embeddings, disable it for now
        # if position_embeddings is None:
        # logger.warning_once(
        # "The attention layers in this model are transitioning from computing the RoPE embeddings internally "
        # "through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
        # "`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
        # "removed and `position_embeddings` will be mandatory."
        # )
        # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        # else:
        # cos, sin = position_embeddings
        seq_len = kv_seq_len
        if parallel_state.sequence_parallel_is_initialized():
            seq_len = kv_seq_len * parallel_state.get_sequence_parallel_world_size()

        cos, sin = self.rotary_emb(value_states, seq_len=seq_len)
        # If sequence parallel in enabled, position_ids should be based on which part of the sequence is present in the rank
        # As we divide the inputs based on ranks, position_ids are generated to suit that part of the sequence
        if parallel_state.sequence_parallel_is_initialized() and parallel_state.get_sequence_parallel_rank() > 0:
            position_ids = torch.arange(
                kv_seq_len * parallel_state.get_sequence_parallel_rank(),
                kv_seq_len * (parallel_state.get_sequence_parallel_rank() + 1),
                dtype=torch.long,
                device=query_states.device,
            )
            position_ids = position_ids.unsqueeze(0)

        query_states, key_states = apply_customized_rope(query_states, key_states, cos, sin, position_ids)

        if use_cache:
            # reuse k, v, self_attention
            if reuse_cache:
                if past_key_value is not None and isinstance(past_key_value[0], torch.Tensor):
                    # prefix tuning case. attach past_key_value to generate first token.
                    key_states = torch.cat((past_key_value[0], key_states), -2)
                    value_states = torch.cat((past_key_value[1], value_states), -2)
                key_states = self.k_cache(key_states, 2, token_idx)
                value_states = self.v_cache(value_states, 2, token_idx)
                past_key_value = (self.k_cache.get_shape(), self.v_cache.get_shape())
            else:
                if past_key_value is None:
                    past_key = torch.zeros(
                        key_states.shape, dtype=self.get_k_proj_weight_dtype(), device=key_states.device
                    )
                    past_value = torch.zeros(
                        key_states.shape, dtype=self.get_k_proj_weight_dtype(), device=key_states.device
                    )
                    # Return list instead of tuple
                    past_key_value = [past_key, past_value]
                if (
                    token_idx is not None
                    and num_virtual_tokens is not None
                    and num_virtual_tokens == past_key_value[0].shape[-2]
                ):
                    # prefix tuning case. attach past_key_value to generate first token.
                    key_states = torch.cat((past_key_value[0], key_states), -2)
                    value_states = torch.cat((past_key_value[1], value_states), -2)
                    past_key_value = (key_states, value_states)
                else:
                    key_states = self.k_cache.update(past_key_value[0], key_states, 2, token_idx, self.inp_seq_len)
                    value_states = self.v_cache.update(past_key_value[1], value_states, 2, token_idx, self.inp_seq_len)

                if token_idx is None:
                    past_key_value = (key_states, value_states)

            if cache_idx is not None and q_len == 1:
                key_states = key_states[:, :, :cache_idx, :]
                value_states = value_states[:, :, :cache_idx, :]
                if attention_mask is not None:
                    attention_mask = attention_mask[:, :, :, :cache_idx]
                kv_seq_len = key_states.shape[-2]
        else:
            past_key_value = None
        fused_scaled_dot_product_attention = GaudiDistributedAttention(
            self.fused_scaled_dot_product_attention, self.fused_scaled_dot_product_attention_distributed
        )
        if use_flash_attention and FusedSDPA is not None:
            if q_len == 1:
                # next token
                attn_output = fused_scaled_dot_product_attention(
                    query_states,
                    key_states,
                    value_states,
                    attention_mask,
                    0.0,
                    False,
                    None,
                    "None",
                    False,
                    None,
                    "None",
                )
            else:
                # first token
                softmax_mode = "fast" if flash_attention_fast_softmax else "None"
                if flash_attention_causal_mask:
                    # causal masking on first token requires inputs to be of the same length
                    attn_output = fused_scaled_dot_product_attention(
                        query_states,
                        key_states,
                        value_states,
                        None,
                        0.0,
                        True,
                        None,
                        softmax_mode,
                        flash_attention_recompute,
                        valid_sequence_lengths,
                        "left",
                    )
                else:
                    attn_output = fused_scaled_dot_product_attention(
                        query_states,
                        key_states,
                        value_states,
                        attention_mask,
                        0.0,
                        False,
                        None,
                        softmax_mode,
                        flash_attention_recompute,
                        None,
                        "None",
                    )

        else:
            query_states, key_states, value_states, attention_mask = gaudi_llama_repeat_kv(
                query_states, key_states, value_states, attention_mask, self.num_key_value_groups
            )

            attn_weights = self.matmul_qk(query_states, key_states.transpose(-2, -1)) * self.norm_factor

            if attention_mask is not None:  # no matter the length, we just slice it
                causal_mask = attention_mask
                if cache_position is not None:
                    causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
                attn_weights = attn_weights + causal_mask

            if attn_softmax_bf16:
                attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=query_states.dtype)
            else:
                # upcast attention to fp32
                attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
                    query_states.dtype
                )
            attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
            attn_output = self.matmul_av(attn_weights, value_states)
            attn_output = attn_output.reshape(bsz, -1, q_len, self.head_dim)

        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()}"
            )

        attn_output = attn_output.transpose(1, 2).contiguous()

        attn_output = attn_output.reshape(bsz, q_len, -1)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        if not reuse_cache and token_idx is not None and cache_idx is not None and q_len == 1:
            # Return only past key value shapes and not the tensors during decode phase (q len is 1)
            # to avoid making past key values as persistent output tensors of HPU graphs.
            past_key_value = (past_key_value[0].shape, past_key_value[1].shape)

        return attn_output, attn_weights, past_key_value

    def attention_all_reduce(self, attn_output):
        if hasattr(self.o_proj, "all_reduce"):
            self.o_proj.all_reduce(attn_output)

    def post_attn_forward(self, attn_output):
        if hasattr(self.o_proj, "post_all_reduce"):
            return self.o_proj.post_all_reduce(attn_output)
        return attn_output


class TPGaudiLlamaAttention(GaudiLlamaAttention, TPModule):
    def __init__(
        self,
        config: LlamaConfig,
        layer_idx: Optional[int] = None,
        group: Optional[ProcessGroup] = None,
    ):
        super().__init__(config, layer_idx)

        assert torch.distributed.is_initialized()
        rank, world_size = distributed.rank_and_world(group)
        assert config.num_attention_heads % world_size == 0, "The number of heads must be divisible by world size"
        self.config = copy.deepcopy(config)

        self.pre_tp_kvheads = config.num_key_value_heads
        GaudiLlamaAttention.__init__(self, self.config, layer_idx)
        self.config.num_attention_heads = self.config.num_attention_heads // world_size
        self.config.num_key_value_heads = (
            (self.config.num_key_value_heads // world_size)
            if self.config.num_key_value_heads > 1
            else self.config.num_key_value_heads
        )
        self.head_dim = config.hidden_size // config.num_attention_heads
        self.hidden_size = self.config.hidden_size // world_size
        self.num_heads = self.config.num_attention_heads

        self.q_proj = torch.nn.Linear(
            config.hidden_size, self.config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = torch.nn.Linear(
            config.hidden_size, self.config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = torch.nn.Linear(
            config.hidden_size, self.config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = torch.nn.Linear(
            self.config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )
        self.norm_factor = 1.0 / math.sqrt(self.head_dim)
        self.setup_tp(rank, world_size)

    def colwise_param_names(self) -> List[str]:
        colwise_weights = ["q_proj"]
        if self.pre_tp_kvheads != 1:
            colwise_weights.append("k_proj")
            colwise_weights.append("v_proj")
        return colwise_weights

    def rowwise_param_names(self) -> List[str]:
        return ["o_proj"]

    @staticmethod
    def import_module(mha: GaudiLlamaAttention, layer_idx, group: ProcessGroup) -> "TPGaudiLlamaAttention":
        tp_mha = TPGaudiLlamaAttention(config=mha.config, layer_idx=layer_idx, group=group)
        return tp_mha

    def pre_attn_forward(
        self,
        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,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.45
        token_idx: Optional[torch.Tensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        reuse_cache: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        cache_idx: int = None,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        hidden_states, attn_weights, present_key_value = GaudiLlamaAttention.pre_attn_forward(
            self,
            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,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            token_idx=token_idx,
            attn_softmax_bf16=attn_softmax_bf16,
            reuse_cache=reuse_cache,
            use_flash_attention=use_flash_attention,
            flash_attention_recompute=flash_attention_recompute,
            flash_attention_causal_mask=flash_attention_causal_mask,
            flash_attention_fast_softmax=flash_attention_fast_softmax,
            cache_idx=cache_idx,
            **kwargs,
        )

        hidden_states = reduce_from_tensor_model_parallel_region(hidden_states)
        return hidden_states, attn_weights, present_key_value


class GaudiLlamaDecoderLayer(LlamaDecoderLayer):
    def __init__(self, config: LlamaConfig, layer_idx: int):
        super(LlamaDecoderLayer, self).__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = GaudiLlamaAttention(config=config, layer_idx=layer_idx)

        self.mlp = GaudiLlamaMLP(config)
        self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        self.self_attn.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        return self.self_attn.reorder_kv_cache(beam_idx)

    def update_sincos_cache(self, seq_len):
        self.self_attn.update_sincos_cache(seq_len)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
        token_idx: Optional[torch.Tensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        reuse_cache: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: Optional[torch.Tensor] = None,
        cache_idx: int = None,
        num_virtual_tokens: int = None,
        **kwargs,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """
        Copied from LlamaDecoderLayer.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
        The only differences are:
        - add new args token_idx
        - add new args attn_softmax_bf16
        - add new args reuse_cache
        - add new args use_flash_attention
        - add new arg flash_attention_recompute
        - add new arg flash_attention_causal_mask
        - add new arg flash_attention_fast_softmax
        """
        residual = hidden_states

        hidden_states, self_attn_weights, present_key_value = self.pre_attn(
            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,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            token_idx=token_idx,
            attn_softmax_bf16=attn_softmax_bf16,
            reuse_cache=reuse_cache,
            use_flash_attention=use_flash_attention,
            flash_attention_recompute=flash_attention_recompute,
            flash_attention_causal_mask=flash_attention_causal_mask,
            flash_attention_fast_softmax=flash_attention_fast_softmax,
            valid_sequence_lengths=valid_sequence_lengths,
            cache_idx=cache_idx,
            num_virtual_tokens=num_virtual_tokens,
            **kwargs,
        )
        self.self_attn.attention_all_reduce(hidden_states)
        hidden_states, residual = self.post_attn_pre_mlp(hidden_states, residual)
        self.mlp.mlp_all_reduce(hidden_states)
        hidden_states = self.post_mlp(hidden_states, residual)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)
        if use_cache:
            outputs += (present_key_value,)

        return outputs

    def pre_attn(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.45
        token_idx: Optional[torch.Tensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        reuse_cache: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: Optional[torch.Tensor] = None,
        cache_idx: int = None,
        num_virtual_tokens: int = None,
    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states, attn_weights, present_key_value = self.self_attn.pre_attn_forward(
            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,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            token_idx=token_idx,
            attn_softmax_bf16=attn_softmax_bf16,
            reuse_cache=reuse_cache,
            use_flash_attention=use_flash_attention,
            flash_attention_recompute=flash_attention_recompute,
            flash_attention_causal_mask=flash_attention_causal_mask,
            flash_attention_fast_softmax=flash_attention_fast_softmax,
            valid_sequence_lengths=valid_sequence_lengths,
            cache_idx=cache_idx,
            num_virtual_tokens=num_virtual_tokens,
        )
        return hidden_states, attn_weights, present_key_value

    def post_attn_pre_mlp(self, hidden_states, residual):
        hidden_states = self.self_attn.post_attn_forward(hidden_states)

        if self.training:
            hidden_states = hidden_states + residual
            residual = hidden_states
        else:
            residual.add_(hidden_states)
            hidden_states = residual

        hidden_states = self.post_attention_layernorm(hidden_states)

        hidden_states = self.mlp.pre_mlp_forward(hidden_states)
        return hidden_states, residual

    def post_mlp(self, hidden_states, residual):
        hidden_states = self.mlp.post_mlp_forward(hidden_states)

        if self.training:
            hidden_states = hidden_states + residual
        else:
            residual.add_(hidden_states)
            hidden_states = residual

        return hidden_states


class GaudiLlamaModel(LlamaModel):
    """
    Copied from https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/models/llama/modeling_llama.py#L909
    """

    def __init__(self, config: LlamaConfig):
        """
        Copied from https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/models/llama/modeling_llama.py#L917
        1. set fill_value to 1 instead of True
        2. add device=self.device
        """
        super(LlamaModel, self).__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = torch.nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        layers = []
        for layer_idx in range(config.num_hidden_layers):
            layer = GaudiLlamaDecoderLayer(config, layer_idx)
            if config.parallel_strategy is not None:
                layer = config.parallel_strategy.distribute_layer(layer, layer_idx)
            layers.append(layer)
        self.layers = torch.nn.ModuleList(layers)
        # parallel_strategy is not JSON serializable
        config.parallel_strategy = None

        self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.gradient_checkpointing = False

        # Initialize weights and apply final processing
        self.post_init()

    def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        for layer in self.layers:
            layer.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        return tuple(layer.reorder_kv_cache(beam_idx) for layer in self.layers)

    def update_sincos_cache(self, seq_len):
        for layer in self.layers:
            layer.update_sincos_cache(seq_len)

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Union[Cache, 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,
        cache_position: Optional[torch.LongTensor] = None,
        token_idx: Optional[torch.Tensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        reuse_cache: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: torch.Tensor = None,
        cache_idx: int = None,
        lazy_mode: Optional[bool] = True,
        num_virtual_tokens: int = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        """
        Copied from LlamaModel.forward: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
        The only differences are:
        - add new args token_idx
        - add new args attn_softmax_bf16
        - add new args reuse_cache
        - add new args use_flash_attention
        - add new arg flash_attention_recompute
        - add new arg flash_attention_causal_mask
        - add new arg flash_attention_fast_softmax
        - add new arg lazy_mode
        """
        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
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError(
                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
            )
        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:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if hasattr(self.config, "use_fused_rope") and self.config.use_fused_rope is False:
            global has_fused_rope
            has_fused_rope = False
        if hasattr(self.config, "use_fused_rms_norm") and self.config.use_fused_rms_norm is False:
            global has_fused_rms_norm
            has_fused_rms_norm = False

        if self.gradient_checkpointing and self.training and use_cache:
            logger.warning_once(
                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
            )
            use_cache = False

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        ignore_cache_position = True  # Ignoring cache position for HPU
        use_new_cache = False  # Ignoring new Cache path for HPU

        past_seen_tokens = 0

        if past_key_values is not None and use_cache:  # kept for BC (cache positions)
            if reuse_cache:
                if isinstance(past_key_values[0][0], torch.Tensor):
                    past_seen_tokens = past_key_values[0][0].shape[2]
                else:
                    past_seen_tokens = past_key_values[0][0][2]
            else:
                if use_new_cache:
                    if not isinstance(past_key_values, StaticCache):
                        past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                    past_seen_tokens = past_key_values.get_seq_length()
                else:
                    past_seen_tokens = past_key_values[0][0].shape[2]

        if ignore_cache_position is False:
            if cache_position is None:
                past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
                cache_position = torch.arange(
                    past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
                )
            if position_ids is None and cache_position:
                position_ids = cache_position.unsqueeze(0)
        else:
            if position_ids is None:
                position_ids = torch.arange(
                    past_seen_tokens, seq_length + past_seen_tokens, dtype=torch.long, device=inputs_embeds.device
                )
                position_ids = position_ids.unsqueeze(0)
            cache_position = None

        # HPU specific mask generation
        if ignore_cache_position:
            causal_mask = _gaudi_prepare_4d_causal_attention_mask(
                attention_mask,
                input_ids.shape if input_ids is not None else (batch_size, seq_length),
                inputs_embeds,
                past_seen_tokens,
            )
        else:
            causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens)

        # embed positions
        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = None  # self.rotary_emb(hidden_states, position_ids)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if not use_new_cache else None

        if lazy_mode:
            htcore.mark_step()

        for layer_idx, decoder_layer in enumerate(self.layers):
            if (
                lazy_mode
                and not self.training
                and (torch.distributed.is_initialized() is False or torch.distributed.get_world_size() == 1)
            ):
                htcore.mark_step()

            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,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                    None,
                    attn_softmax_bf16,
                    False,
                    use_flash_attention,
                    flash_attention_recompute,
                    flash_attention_causal_mask,
                    flash_attention_fast_softmax,
                    valid_sequence_lengths,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=None if past_key_values is None else past_key_values[layer_idx],
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                    token_idx=token_idx,
                    attn_softmax_bf16=attn_softmax_bf16,
                    reuse_cache=reuse_cache,
                    use_flash_attention=use_flash_attention,
                    flash_attention_recompute=flash_attention_recompute,
                    flash_attention_causal_mask=flash_attention_causal_mask,
                    flash_attention_fast_softmax=flash_attention_fast_softmax,
                    valid_sequence_lengths=valid_sequence_lengths,
                    cache_idx=cache_idx,
                    num_virtual_tokens=num_virtual_tokens,
                )
            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],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if not use_new_cache and isinstance(next_cache, Cache):
            next_cache = next_cache.to_legacy_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,
        )


class GaudiLlamaForCausalLM(LlamaForCausalLM):
    """
    Inherits from LlamaForCausalLM: https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
    The only differences are:
    - add new args token_idx
    - add token_idx into model_inputs
    - from step2 when enable KV cache, slice next_input_ids from input_ids base on the token_idx
    - from step2 when enable KV cache, slice next_position_ids from position_ids base on the token_idx
    - add new args attn_softmax_bf16
    - add new args reuse_cache
    """

    def __init__(self, config, parallel_strategy: DistributedStrategy = NoOpStrategy):
        config.parallel_strategy = parallel_strategy
        super().__init__(config)

    def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        self.model.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        return self.model.reorder_kv_cache(beam_idx)

    def update_sincos_cache(self, seq_len):
        self.model.update_sincos_cache(seq_len)

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        num_logits_to_keep: int = 0,
        token_idx: Optional[torch.Tensor] = None,
        trim_logits: Optional[bool] = False,
        attn_softmax_bf16: Optional[bool] = False,
        reuse_cache: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: torch.Tensor = None,
        cache_idx: int = None,
        lazy_mode: Optional[bool] = True,
        num_virtual_tokens: int = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        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
        if self.generation_config.use_fused_rope is False:
            global has_fused_rope
            has_fused_rope = False

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            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,
            cache_position=cache_position,
            token_idx=token_idx,
            attn_softmax_bf16=attn_softmax_bf16,
            reuse_cache=reuse_cache,
            use_flash_attention=use_flash_attention,
            flash_attention_recompute=flash_attention_recompute,
            flash_attention_causal_mask=flash_attention_causal_mask,
            flash_attention_fast_softmax=flash_attention_fast_softmax,
            valid_sequence_lengths=valid_sequence_lengths,
            cache_idx=cache_idx,
            lazy_mode=lazy_mode,
            num_virtual_tokens=num_virtual_tokens,
        )
        hidden_states = outputs[0]
        _, seq_len, _ = hidden_states.shape
        if seq_len > 1 and trim_logits and not self.training:
            if token_idx is not None:
                hidden_states = hidden_states.index_select(1, token_idx - 1)
            else:
                hidden_states = hidden_states[:, -1, :]

        if self.config.pretraining_tp > 1:
            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
            logits = torch.cat(logits, dim=-1)
        else:
            if labels is None and not is_torchdynamo_compiling():
                logger.warning_once(
                    "Starting from v4.46, the `logits` model output will have the same type as the model (except at train time, where it will always be FP32)"
                )
            # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
            # TODO: remove the float() operation in v4.46
            logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :]).float()

        loss = None
        if labels is not None:
            # Upcast to float if we need to compute the loss to avoid potential precision issues
            logits = logits.float()
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = torch.nn.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)
            # Collect losses from context parallel group
            # Each rank in group calculates loss on partial outputs
            if (
                parallel_state.sequence_parallel_is_initialized()
                and parallel_state.get_sequence_parallel_world_size() > 1
            ):
                from optimum.habana.distributed.contextparallel import _get_loss_from_context_parallel

                loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
                loss_all = _get_loss_from_context_parallel(loss_fct(shift_logits, shift_labels))
                loss = torch.mean(loss_all)
            else:
                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=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    @staticmethod
    def _reorder_cache(
        past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.

        Output shares the same memory storage as `past`.
        """
        return tuple(
            (
                layer_past[0].index_select(0, beam_idx.to(layer_past[0].device)),
                layer_past[1].index_select(0, beam_idx.to(layer_past[1].device)),
            )
            for layer_past in past
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past_key_values=None,
        attention_mask=None,
        inputs_embeds=None,
        cache_position=None,
        position_ids=None,
        use_cache=True,
        num_logits_to_keep=None,
        token_idx=None,
        **kwargs,
    ):
        reuse_cache = kwargs.get("reuse_cache")
        bucket_internal = kwargs.get("bucket_internal")
        if past_key_values is not None:
            if token_idx is not None:
                idx = token_idx + kwargs.get("inputs_embeds_offset", 0) - 1
                input_ids = torch.index_select(input_ids, 1, idx)
            else:
                if inputs_embeds is not None:  # Exception 1
                    input_ids = input_ids[:, -cache_position.shape[0] :]
                elif (
                    input_ids.shape[1] != cache_position.shape[0]
                ):  # Default case (the "else", a no op, is Exception 2)
                    input_ids = input_ids[:, cache_position]
        elif (reuse_cache or bucket_internal) and token_idx is not None:
            # KV cache is pre allocated with reuse cache or will be padded with bucket internal
            # hence for the 1st token we can slice the inputs till token idx for the fwd pass.
            input_ids = input_ids[:, :token_idx]
            attention_mask = attention_mask[:, :token_idx]

        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:
                if token_idx is not None:
                    position_ids = torch.index_select(position_ids, 1, token_idx - 1)
                else:
                    position_ids = position_ids[:, -input_ids.shape[1] :]
                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
                position_ids = position_ids.clone(memory_format=torch.contiguous_format)

        # keep cache_position implementation as None for HPU
        cache_position = 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.clone(memory_format=torch.contiguous_format)}

        if num_logits_to_keep is not None:
            model_inputs["num_logits_to_keep"] = num_logits_to_keep

        model_inputs.update(
            {
                "position_ids": position_ids,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": use_cache,
                "attention_mask": attention_mask,
                "token_idx": token_idx,
                "trim_logits": kwargs.get("trim_logits"),
                "attn_softmax_bf16": kwargs.get("attn_softmax_bf16"),
                "reuse_cache": reuse_cache,
                "use_flash_attention": kwargs.get("use_flash_attention"),
                "flash_attention_recompute": kwargs.get("flash_attention_recompute"),
                "flash_attention_causal_mask": kwargs.get("flash_attention_causal_mask"),
                "flash_attention_fast_softmax": kwargs.get("flash_attention_fast_softmax"),
                "valid_sequence_lengths": kwargs.get("valid_sequence_lengths"),
                "cache_idx": kwargs.get("cache_idx"),
                "lazy_mode": kwargs.get("lazy_mode"),
                "num_virtual_tokens": kwargs.get("num_virtual_tokens"),
            }
        )
        return model_inputs

    # Transformer4.43 use new Cache mechanism while Gaudi is not.
    # Adding _reorder_cache back to support HPU.
    @staticmethod
    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


def apply_customized_rope(q, k, cos, sin, position_ids, training=True):
    if q.device.type == "hpu" and has_fused_rope:
        return apply_customized_rope_module(q, k, cos, sin, position_ids, training)
    else:
        return apply_rotary_pos_emb(q, k, cos[position_ids], sin[position_ids])