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304 lines
12 KiB
Python
304 lines
12 KiB
Python
"""
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LLaMA intermediate layer
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Based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py
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See commit history for authorship.
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"""
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import math
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from transformers.models.llama.modeling_llama import (
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LlamaAttention,
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LlamaConfig,
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LlamaDecoderLayer,
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LlamaMLP,
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LlamaModel,
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LlamaRMSNorm,
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repeat_kv,
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rotate_half,
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)
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from petals.utils.cuda_graphs import make_inference_graphed_callable
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def apply_rotary_pos_emb(q, k, cos, sin):
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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class OptimizedLlamaAttention(LlamaAttention):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self._rotary_graph = None
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def _optimized_apply_rotary(self, query_states, key_states, cos, sin):
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if self._rotary_graph is None:
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self._rotary_graph = make_inference_graphed_callable(
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apply_rotary_pos_emb, sample_args=(query_states, key_states, cos, sin)
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)
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return self._rotary_graph(query_states, key_states, cos, sin)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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assert not output_attentions
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if position_ids is None:
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past_seen_tokens = past_key_value[0].shape[2] if past_key_value is not None else 0
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position_ids = torch.arange(
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past_seen_tokens, past_seen_tokens + hidden_states.shape[1], device=hidden_states.device
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).unsqueeze(0)
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bsz, q_len, _ = hidden_states.size()
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if self.config.pretraining_tp > 1:
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key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
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query_slices = self.q_proj.weight.split(
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(self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
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)
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key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
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value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
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query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
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query_states = torch.cat(query_states, dim=-1)
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key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
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key_states = torch.cat(key_states, dim=-1)
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value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
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value_states = torch.cat(value_states, dim=-1)
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else:
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
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kv_seq_len = key_states.shape[-2]
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if past_key_value is not None:
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kv_seq_len += past_key_value[0].shape[-2]
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cos, sin = self.rotary_emb(value_states, position_ids, seq_len=kv_seq_len)
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cos, sin = cos.unsqueeze(1), sin.unsqueeze(1)
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if q_len == 1 and torch.is_inference_mode_enabled() and hidden_states.device.type == "cuda":
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query_states, key_states = self._optimized_apply_rotary(query_states, key_states, cos, sin)
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else:
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# reuse k, v, self_attention
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key_states = torch.cat([past_key_value[0], key_states], dim=2)
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value_states = torch.cat([past_key_value[1], value_states], dim=2)
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past_key_value = (key_states, value_states) if use_cache else None
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None:
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attn_weights = attn_weights + attention_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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if self.config.pretraining_tp > 1:
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attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
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o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
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attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
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else:
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attn_output = self.o_proj(attn_output)
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return attn_output, None, past_key_value
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class OptimizedLlamaDecoderLayer(LlamaDecoderLayer):
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def __init__(self, config: LlamaConfig):
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nn.Module.__init__(self)
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self.hidden_size = config.hidden_size
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self.self_attn = OptimizedLlamaAttention(config=config)
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self.mlp = LlamaMLP(config)
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self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.pre_attn_graph = None
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self.post_attn_graph = None
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def _optimized_input_layernorm(self, hidden_states):
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if self.pre_attn_graph is None:
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self.pre_attn_graph = make_inference_graphed_callable(
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self.input_layernorm.forward, sample_args=(hidden_states,)
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)
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return self.pre_attn_graph(hidden_states)
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def _optimized_output_layernorm(self, hidden_states):
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if self.post_attn_graph is None:
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self.post_attn_graph = make_inference_graphed_callable(
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self.post_attention_layernorm.forward, sample_args=(hidden_states,)
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)
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return self.post_attn_graph(hidden_states)
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Tuple[torch.Tensor]] = None,
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs,
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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"""
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Args:
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hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
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attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
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`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
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output_attentions (`bool`, *optional*):
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Whether or not to return the attentions tensors of all attention layers. See `attentions` under
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returned tensors for more detail.
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use_cache (`bool`, *optional*):
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If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
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(see `past_key_values`).
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past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
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"""
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residual = hidden_states
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if hidden_states.size(1) == 1 and torch.is_inference_mode_enabled() and hidden_states.device.type == "cuda":
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hidden_states = self._optimized_input_layernorm(hidden_states)
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else:
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hidden_states = self.input_layernorm(hidden_states)
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# Self Attention
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hidden_states, self_attn_weights, present_key_value = self.self_attn(
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hidden_states=hidden_states,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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output_attentions=output_attentions,
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use_cache=use_cache,
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cache_position=cache_position,
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**kwargs,
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)
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hidden_states = residual + hidden_states
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# Fully Connected
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residual = hidden_states
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if hidden_states.size(1) == 1 and torch.is_inference_mode_enabled() and hidden_states.device.type == "cuda":
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hidden_states = self._optimized_output_layernorm(hidden_states)
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else:
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hidden_states = self.post_attention_layernorm(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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outputs = (hidden_states,)
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if output_attentions:
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outputs += (self_attn_weights,)
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if use_cache:
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outputs += (present_key_value,)
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return outputs
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class WrappedLlamaBlock(OptimizedLlamaDecoderLayer):
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def forward(
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self,
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hidden_states: torch.Tensor,
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*args,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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layer_past: Optional[Tuple[torch.Tensor]] = None,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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batch_size, seq_length, _ = hidden_states.shape
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seq_length_with_past = seq_length
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past_key_values_length = 0
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past_key_value = layer_past
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if past_key_value is not None:
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past_key_values_length = past_key_value[0].shape[2]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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past_key_value = self._reorder_cache_from_bloom_to_llama(past_key_value, batch_size, past_key_values_length)
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assert position_ids is None
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# embed positions
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if attention_mask is None:
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attention_mask = torch.ones(
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(batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device
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)
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask=attention_mask,
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input_shape=(batch_size, seq_length),
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inputs_embeds=hidden_states,
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past_key_values_length=past_key_values_length,
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)
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outputs = super().forward(
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hidden_states,
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*args,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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use_cache=use_cache,
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**kwargs,
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)
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if use_cache:
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present_key_value = outputs[-1]
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present_key_value = self._reorder_cache_from_llama_to_bloom(
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present_key_value, batch_size, seq_length_with_past
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)
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outputs = outputs[:-1] + (present_key_value,)
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return outputs
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def _reorder_cache_from_bloom_to_llama(
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self, key_value: Tuple[torch.Tensor], batch_size: int, seq_length: int
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) -> Tuple[torch.Tensor]:
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key_states, value_states = key_value
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key_states = key_states.permute(0, 2, 1)
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key_states = key_states.view(
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batch_size, self.self_attn.num_key_value_heads, seq_length, self.self_attn.head_dim
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)
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value_states = value_states.view(*key_states.shape)
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return (key_states, value_states)
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def _reorder_cache_from_llama_to_bloom(
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self, key_value: Tuple[torch.Tensor], batch_size: int, seq_length: int
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) -> Tuple[torch.Tensor]:
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key_states, value_states = key_value
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value_states = value_states.view(
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batch_size * self.self_attn.num_key_value_heads, seq_length, self.self_attn.head_dim
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)
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key_states = key_states.view(*value_states.shape)
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key_states = key_states.permute(0, 2, 1)
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return (key_states, value_states)
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