content: # coding=utf-8
  # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
  #
  # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
  # and OPT implementations in this library. It has been modified from its
  # original forms to accommodate minor architectural differences compared
  # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from typing import List, Optional, Tuple
  
  # Flash attention imports
  import dropout_layer_norm
  import torch
  import torch.distributed
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.configuration_utils import PretrainedConfig
  
  from lorax_server.adapters import AdapterBatchData
  from lorax_server.utils import flash_attn, paged_attention
  from lorax_server.utils.attention.common import Seqlen
  from lorax_server.utils.flash_attn import HAS_FLASH_ATTN_V2_CUDA
  from lorax_server.utils.layers import (
      MultiAdapterHead,
      PositionRotaryEmbedding,
      TensorParallelAdapterRowLinear,
      TensorParallelColumnLinear,
      TensorParallelEmbedding,
      TensorParallelHead,
      TensorParallelMultiAdapterLinear,
      TensorParallelRowLinear,
      get_linear,
  )
  from lorax_server.utils.lora import (
      DOWN_PROJ,
      GATE_PROJ,
      K_PROJ,
      LM_HEAD,
      O_PROJ,
      Q_PROJ,
      UP_PROJ,
      V_PROJ,
  )
  from lorax_server.utils.torch_utils import is_fp8_kv, is_quantized
  
  if not HAS_FLASH_ATTN_V2_CUDA:
      raise ImportError("Solar model requires flash attn v2")
  
  
  class SolarConfig(PretrainedConfig):
      def __init__(
          self,
          vocab_size=32000,
          hidden_size=4096,
          intermediate_size=11008,
          num_hidden_layers=32,
          num_attention_heads=32,
          num_key_value_heads=None,
          hidden_act="silu",
          max_position_embeddings=2048,
          initializer_range=0.02,
          rms_norm_eps=1e-6,
          use_cache=True,
          pad_token_id=None,
          bos_token_id=1,
          eos_token_id=2,
          pretraining_tp=1,
          tie_word_embeddings=False,
          rope_scaling=None,
          rope_theta=10000.0,
          attention_bias=False,
          attention_dropout=0.0,
          mlp_bias=False,
          sliding_window=2047,
          bskcn_1=[12, 20, 32, 44],
          bskcn_2=[20, 32],
          bskcn_3=[16, 24, 36, 48],
          bskcn_4=[28, 40],
          bskcn_tv=[0.9, 0.8],
          **kwargs,
      ):
          self.vocab_size = vocab_size
          self.max_position_embeddings = max_position_embeddings
          self.hidden_size = hidden_size
          self.intermediate_size = intermediate_size
          self.num_hidden_layers = num_hidden_layers
          self.num_attention_heads = num_attention_heads
          self.sliding_window = sliding_window
  
          # for backward compatibility
          if num_key_value_heads is None:
              num_key_value_heads = num_attention_heads
  
          self.num_key_value_heads = num_key_value_heads
          self.hidden_act = hidden_act
          self.initializer_range = initializer_range
          self.rms_norm_eps = rms_norm_eps
          self.pretraining_tp = pretraining_tp
          self.use_cache = use_cache
          self.rope_theta = rope_theta
          self.rope_scaling = rope_scaling
          self.attention_bias = attention_bias
          self.attention_dropout = attention_dropout
          self.mlp_bias = mlp_bias
          self.bskcn_1 = bskcn_1
          self.bskcn_2 = bskcn_2
          self.bskcn_3 = bskcn_3
          self.bskcn_4 = bskcn_4
          self.bskcn_tv = bskcn_tv
  
          super().__init__(
              pad_token_id=pad_token_id,
              bos_token_id=bos_token_id,
              eos_token_id=eos_token_id,
              tie_word_embeddings=tie_word_embeddings,
              **kwargs,
          )
  
  
  class SolarRMSNorm(nn.Module):
      def __init__(self, prefix, weights, eps=1e-6):
          """
          SolarRMSNorm is equivalent to T5LayerNorm
          """
          super().__init__()
  
          weight = weights.get_tensor(f"{prefix}.weight")
          self.weight = nn.Parameter(weight)
          self.variance_epsilon = eps
  
      def forward(self, hidden_states, residual=None):
          if hidden_states.shape[-1] > 8192:
              if residual is not None:
                  hidden_states += residual
              residual = hidden_states
  
              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)
  
              # convert into half-precision if necessary
              if self.weight.dtype in [torch.float16, torch.bfloat16]:
                  hidden_states = hidden_states.to(self.weight.dtype)
  
              return self.weight * hidden_states, residual
          else:
              # faster post attention rms norm
              normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
                  hidden_states,
                  residual,
                  self.weight,
                  None,
                  None,
                  None,
                  None,
                  None,
                  0.0,
                  self.variance_epsilon,
                  1.0,
                  0,
                  None,
                  False,
                  True,  # Activate RMSNorm
              )
              if res is None:
                  res = hidden_states
  
              return normed_hidden_states, res
  
  
  def load_attention(config, prefix, weights, layer_id, head_size):
      base_layer = load_attention_multi(config, prefix, weights, head_size)
      return TensorParallelMultiAdapterLinear.load(
          base_layer,
          layer_id,
          [Q_PROJ, K_PROJ, V_PROJ],
          sizes=[
              head_size * config.num_attention_heads,
              head_size * config.num_key_value_heads,
              head_size * config.num_key_value_heads,
          ],
          process_group=weights.process_group,
      )
  
  
  def load_attention_multi(config, prefix, weights, head_size):
      if config.num_attention_heads != config.num_key_value_heads:
          return _load_gqa(config, prefix, weights, head_size)
      else:
          return TensorParallelColumnLinear.load_multi(
              config,
              prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
              dim=0,
              weights=weights,
              bias=False,
          )
  
  
  def _load_gqa(config, prefix: str, weights, head_size):
      assert config.hidden_size % config.num_attention_heads == 0
      assert config.num_attention_heads % weights.process_group.size() == 0
  
      weight = weights.get_multi_weights_col(
          prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
          quantize=config.quantize,
          dim=0,
      )
  
      input_scale, weight_scale = None, None
      if isinstance(weight, tuple):
          weight, input_scale, weight_scale = weight
  
      if not is_quantized(config.quantize):
          weight = weight.to(dtype=weights.dtype).to(device=weights.device)
  
          num_heads = config.num_attention_heads // weights.process_group.size()
          num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
          assert list(weight.shape) == [
              (num_heads + 2 * num_key_value_heads) * head_size,
              config.hidden_size,
          ], f"{list(weight.shape)} != {[(num_heads + 2 * num_key_value_heads) * head_size, config.hidden_size]}"
  
      return TensorParallelColumnLinear(
          get_linear(
              weight,
              bias=None,
              quantize=config.quantize,
              weight_scale=weight_scale,
              input_scale=input_scale,
          )
      )
  
  
  class SolarAttention(torch.nn.Module):
      def __init__(
          self,
          prefix: str,
          config,
          weights,
          layer_id: int,
      ):
          super().__init__()
  
          self.max_past = config.sliding_window if config.sliding_window is not None else -1
          self.num_heads = config.num_attention_heads
          self.hidden_size = config.hidden_size
  
          if hasattr(config, "head_dim"):
              self.head_size = config.head_dim
          else:
              self.head_size = self.hidden_size // self.num_heads
  
          # TODO(Arnav): Solar can also support linear and dynamic ntk rotary embeddings
          self.rotary_emb = PositionRotaryEmbedding.static(
              config=config,
              dim=self.head_size,
              base=config.rope_theta,
              device=weights.device,
              dtype=weights.dtype,
          )
  
          self.softmax_scale = self.head_size**-0.5
  
          if self.num_heads % weights.process_group.size() != 0:
              raise ValueError(
                  f"`num_heads` must be divisible by `num_shards` (got `num_heads`: {self.num_heads} "
                  f"and `num_shards`: {weights.process_group.size()}"
              )
  
          self.num_heads = self.num_heads // weights.process_group.size()
          self.num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
  
          if is_fp8_kv(config.quantize):
              self.k_scale = weights.get_tensor(f"{prefix}.k_scale", use_self_dtype=False).item()
              self.v_scale = weights.get_tensor(f"{prefix}.v_scale", use_self_dtype=False).item()
              self.fp8_kv = True
          else:
              self.k_scale = 1.0
              self.v_scale = 1.0
              self.fp8_kv = False
  
          self.query_key_value = load_attention(config, prefix, weights, layer_id, self.head_size)
  
          self.o_proj = TensorParallelAdapterRowLinear.load(
              TensorParallelRowLinear.load(
                  config,
                  prefix=f"{prefix}.o_proj",
                  weights=weights,
                  bias=False,
              ),
              layer_id,
              O_PROJ,
              process_group=weights.process_group,
          )
  
          self.num_groups = self.num_heads // self.num_key_value_heads
          self.kv_head_mapping = torch.arange(
              0, self.num_key_value_heads, dtype=torch.int32, device=weights.device
          ).repeat_interleave(self.num_groups)
  
      def get_query_key_value_weights(self, clone=True):
          """Gets the query, key, and value weights from the attention layer.
  
          If `clone`, then the weights are cloned before being returned.
  
          NOTE: if not `clone`, then the weights are returned as views, meaning
          that changes to the weights will be reflected in the attention layer.
          """
          query, key, value = self.query_key_value.base_layer.linear.weight.split(
              [
                  self.head_size * self.num_heads,
                  self.head_size * self.num_key_value_heads,
                  self.head_size * self.num_key_value_heads,
              ],
              dim=0,
          )
  
          if clone:
              return query.clone(), key.clone(), value.clone()
          return query, key, value
  
      def forward(
          self,
          hidden_states,
          cos,
          sin,
          cu_seqlen_prefill,
          kv_cache,
          block_tables,
          slots,
          seqlen,
          max_s,
          adapter_data,
          prefill_cache_indices,
      ):
          qkv = self.query_key_value(hidden_states, adapter_data)
          query, kv = qkv.split(
              [
                  self.head_size * self.num_heads,
                  2 * self.head_size * self.num_key_value_heads,
              ],
              dim=1,
          )
          query = query.view(-1, self.num_heads, self.head_size)
          kv = kv.view(-1, 2, self.num_key_value_heads, self.head_size)
  
          self.rotary_emb(query, cos, sin)
          self.rotary_emb(torch.select(kv, dim=1, index=0), cos, sin)
  
          if prefill_cache_indices is not None:
              kv_to_cache = kv[prefill_cache_indices]
          else:
              kv_to_cache = kv
  
          paged_attention.reshape_and_cache(
              kv_to_cache[:, 0],
              kv_to_cache[:, 1],
              kv_cache[0],
              kv_cache[1],
              slots,
              self.k_scale,
              self.v_scale,
              self.fp8_kv,
          )
  
          # Prefill
          if cu_seqlen_prefill is not None:
              # flash attention
              attn_output = flash_attn.attention(
                  query,
                  torch.select(kv, dim=1, index=0),
                  torch.select(kv, dim=1, index=1),
                  kv_cache[0],
                  kv_cache[1],
                  cu_seqlen_prefill,
                  max_s,
                  self.softmax_scale,
                  window_size_left=self.max_past,
                  k_scale=self.k_scale,
                  v_scale=self.v_scale,
                  fp8_kv=self.fp8_kv,
              )
          # Decode
          else:
              attn_output = paged_attention.attention(
                  query,
                  kv_cache[0],
                  kv_cache[1],
                  self.num_key_value_heads,
                  self.kv_head_mapping,
                  self.softmax_scale,
                  block_tables,
                  seqlen,
                  max_s,
                  k_scale=self.k_scale,
                  v_scale=self.v_scale,
              )
  
          return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size), adapter_data)
  
  
  class SolarMLP(nn.Module):
      def __init__(self, prefix, config, weights, layer_id):
          super().__init__()
          act = config.hidden_act
          self.act = (
              ACT2FN[act]
              if "gelu" not in act
              else lambda x: torch.nn.functional.gelu(
                  x,
                  approximate="tanh" if act in ["gelu_fast", "gelu_pytorch_tanh"] else "none",
              )
          )
          # Fuse gate and up proj
          gate_up_proj = TensorParallelColumnLinear.load_multi(
              config,
              prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
              weights=weights,
              dim=0,
              bias=False,
          )
          self.gate_up_proj = TensorParallelMultiAdapterLinear.load(
              gate_up_proj,
              layer_id,
              [GATE_PROJ, UP_PROJ],
              sizes=[
                  config.intermediate_size,
                  config.intermediate_size,
              ],
              process_group=weights.process_group,
          )
  
          self.down_proj = TensorParallelAdapterRowLinear.load(
              TensorParallelRowLinear.load(
                  config,
                  prefix=f"{prefix}.down_proj",
                  weights=weights,
                  bias=False,
              ),
              layer_id,
              DOWN_PROJ,
              process_group=weights.process_group,
          )
          self.intermediate_size = config.intermediate_size // weights.process_group.size()
  
      def forward(self, hidden_states, adapter_data):
          gate_up_states = self.gate_up_proj(hidden_states, adapter_data)
          gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
          return self.down_proj(self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], adapter_data)
  
  
  class SolarLayer(nn.Module):
      def __init__(self, prefix, layer_id, config, weights):
          super().__init__()
          self.self_attn = SolarAttention(
              prefix=f"{prefix}.self_attn",
              config=config,
              weights=weights,
              layer_id=layer_id,
          )
          self.mlp = SolarMLP(prefix=f"{prefix}.mlp", config=config, weights=weights, layer_id=layer_id)
  
          self.input_layernorm = SolarRMSNorm(
              prefix=f"{prefix}.input_layernorm", weights=weights, eps=config.rms_norm_eps
          )
          self.post_attention_layernorm = SolarRMSNorm(
              prefix=f"{prefix}.post_attention_layernorm",
              weights=weights,
              eps=config.rms_norm_eps,
          )
  
      def forward(
          self,
          hidden_states,
          residual,
          cos,
          sin,
          cu_seqlen_prefill,
          kv_cache,
          block_tables,
          slots,
          seqlen,
          max_s,
          adapter_data,
          prefill_cache_indices,
      ):
          normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
  
          # Self Attention
          attn_output = self.self_attn(
              normed_hidden_states,
              cos,
              sin,
              cu_seqlen_prefill,
              kv_cache,
              block_tables,
              slots,
              seqlen,
              max_s,
              adapter_data,
              prefill_cache_indices,
          )
  
          # faster post attention rms norm
          normed_attn_res_output, attn_res = self.post_attention_layernorm(attn_output, res)
  
          mlp_output = self.mlp(normed_attn_res_output, adapter_data)
  
          return mlp_output, attn_res
  
  
  class SolarModel(nn.Module):
      def __init__(self, prefix: str, config, weights):
          super().__init__()
          self.config = config
  
          process_group = weights.process_group
          self.tp_rank = process_group.rank()
          self.tp_world_size = process_group.size()
  
          self.layers = nn.ModuleList(
              [
                  SolarLayer(
                      f"model.layers.{layer_id}" if not prefix else f"{prefix}.model.layers.{layer_id}",
                      layer_id,
                      config,
                      weights,
                  )
                  for layer_id in range(config.num_hidden_layers)
              ]
          )
  
          self.norm = SolarRMSNorm(
              prefix="model.norm" if not prefix else f"{prefix}.model.norm",
              weights=weights,
              eps=config.rms_norm_eps,
          )
  
          self.gradient_checkpointing = False
  
          self.head_size = self.layers[0].self_attn.head_size
          self.num_heads = self.layers[0].self_attn.num_heads
          self.num_key_value_heads = self.layers[0].self_attn.num_key_value_heads
  
      def forward(
          self,
          inputs_embeds: torch.Tensor,
          position_ids: torch.Tensor,
          cu_seqlen_prefill: Optional[torch.Tensor],
          kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
          block_tables: torch.Tensor,
          slots: torch.Tensor,
          seqlen: Seqlen,
          max_s: int,
          adapter_data: AdapterBatchData,
          prefill_cache_indices: Optional[torch.Tensor],
      ) -> torch.Tensor:
          hidden_states = inputs_embeds
  
          # Get rotary cos and sin for this forward
          # Avoid to index in each layer
          cos, sin = self.layers[0].self_attn.rotary_emb.get_cos_sin(position_ids, max_s, hidden_states.dtype)
  
          residual = None
  
          bskcn_1 = None
          bskcn_2 = None
          # Note, we use index 1 instead of index 0 since index 0 is used when training is enabled
          bskcn_tv = self.config.bskcn_tv[1]
          for i, layer in enumerate(self.layers):
              # Add residual to hidden states explicitly. We have to do this because the cross-layer
              # residuals assume the output hidden state already have the residuals added to it, but the
              # LoRAX implementation only adds the residual to the hidden states in the next layer's input_layernorm.
              if residual is not None:
                  hidden_states = hidden_states + residual
  
              if i in self.config.bskcn_1:
                  bskcn_1 = hidden_states
              if i in self.config.bskcn_2:
                  bskcn_2 = hidden_states
              if i in self.config.bskcn_3:
                  hidden_states = (bskcn_1 * bskcn_tv).to(hidden_states.device) + hidden_states * (1 - bskcn_tv)
              if i in self.config.bskcn_4:
                  hidden_states = (bskcn_2 * bskcn_tv).to(hidden_states.device) + hidden_states * (1 - bskcn_tv)
  
              # Note, we explicitly set residual to None here to skip adding it to the hidden states
              # in the input_layernorm layer because we do this explicitly above.
              hidden_states, residual = layer(
                  hidden_states,
                  None,  # residual
                  cos,
                  sin,
                  cu_seqlen_prefill,
                  kv_cache[i],
                  block_tables,
                  slots,
                  seqlen,
                  max_s,
                  adapter_data,
                  prefill_cache_indices,
              )
  
          hidden_states, _ = self.norm(hidden_states, residual)
  
          return hidden_states
  
  
  class FlashSolarForCausalLM(torch.nn.Module):
      def __init__(self, prefix: str, config, weights):
          super().__init__()
          self.config = config
  
          self.embed_tokens = TensorParallelEmbedding(
              prefix=("model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"),
              weights=weights,
          )
  
          self.model = SolarModel(
              prefix=prefix,
              config=config,
              weights=weights,
          )
  
          if config.tie_word_embeddings:
              suffix = "model.embed_tokens"
          else:
              suffix = "lm_head"
  
          self.lm_head = MultiAdapterHead.load(
              TensorParallelHead.load(
                  config,
                  prefix=suffix if not prefix else f"{prefix}.{suffix}",
                  weights=weights,
              ),
              0,
              LM_HEAD,
              process_group=weights.process_group,
          )
  
          self.max_past = config.sliding_window
  
      def forward(
          self,
          input_ids: torch.Tensor,
          position_ids: torch.Tensor,
          cu_seqlen_prefill: Optional[torch.Tensor],
          kv_cache: List[Tuple[torch.Tensor, torch.Tensor]],
          block_tables: torch.Tensor,
          slots: torch.Tensor,
          seqlen: Seqlen,
          max_s: int,
          adapter_data: AdapterBatchData,
          prefill_cache_indices: Optional[torch.Tensor] = None,
          lm_head_indices: Optional[torch.Tensor] = None,
          skip_lm_head: bool = False,
      ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
          if prefill_cache_indices is not None:
              # Slots also need to be sliced as it has the same size as the whole kv tensor
              slots = slots[prefill_cache_indices]
          elif self.max_past is not None:
              # Clamp in decode mode as paged attention requires clamped values whereas the flash attention
              # kernel requires the true values
              max_s = min(self.max_past, max_s)
              seqlen = seqlen.clamp(max=self.max_past)
  
          inputs_embeds = self.embed_tokens(input_ids)
          hidden_states = self.model(
              inputs_embeds,
              position_ids,
              cu_seqlen_prefill,
              kv_cache,
              block_tables,
              slots,
              seqlen,
              max_s,
              adapter_data,
              prefill_cache_indices,
          )
  
          if lm_head_indices is not None:
              hidden_states = hidden_states[lm_head_indices]
  
          if skip_lm_head:
              return hidden_states, None
  
          logits, speculative_logits = self.lm_head(hidden_states, adapter_data)
          return logits, speculative_logits
  