Upload 10 files

model/choices.py

@@ -0,0 +1,3 @@
+mc_sim_7b_63 = [[0],[1],[2],[3],[0,0],[0,1],[0,2],[1,0],[1,1],[2,0],[2,1],[3,0]
+                ,[0,0,0],[0,0,1],[0,0,2],[0,1,0],[0,1,1],[0,2,0],[0,2,1],[1,0,0],
+                [0,0,0,0],[0,0,0,1],[0,0,0,2],[0,0,0,0,0],[0,0,0,0,1]]

model/cnets.py

@@ -0,0 +1,965 @@
+# 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.
+""" PyTorch LLaMA model."""
+import copy
+import os
+#os.environ["CUDA_VISIBLE_DEVICES"] = "5"
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+try:
+    from .configs import EConfig
+    from .utils_c import *
+    from .choices import *
+except:
+    from configs import EConfig
+    from utils_c import *
+    from choices import *
+    from utils import prepare_logits_processor
+top_k=10
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+    return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+class LlamaRotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    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.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
+
+class LlamaAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        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: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        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.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:
+            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)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        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, self.hidden_size)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def 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)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+class LlamaRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        LlamaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        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 LlamaDecoderLayer(nn.Module):
+    def __init__(self, config,index):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(config)
+        self.index=index
+        if self.index!=0:
+            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 forward(
+        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,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        if self.index != 0:
+            hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_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,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+class I(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.dummy = nn.Parameter(torch.ones(1, dtype=torch.float32))
+    def forward(self,x):
+        return x + self.dummy - self.dummy #(also tried x+self.dummy)
+
+def len_list(x,n):
+    return [i for i in x if len(i)<=n]
+
+class Model(nn.Module):
+    def __init__(self,config,load_emb=False):
+        super().__init__()
+
+
+
+
+        self.gradient_checkpointing = True
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        if load_emb:
+            from safetensors import safe_open
+            with safe_open("weights/llama2chat/13B/model-00001-of-00003.safetensors",
+                           framework="pt",
+                           device="cpu") as f:
+                tensor_slice = f.get_slice("model.embed_tokens.weight")
+                vocab_size, hidden_dim = tensor_slice.get_shape()
+                tensor = tensor_slice[:, :hidden_dim].float()
+            self.embed_tokens.weight.data = tensor
+
+        #self.init_tree()
+
+        self.layers = nn.ModuleList([LlamaDecoderLayer(config,index) for index in range(config.num_hidden_layers)])
+        self.fc=nn.Linear(2*config.hidden_size,config.hidden_size)
+        self.act=ACT2FN[config.hidden_act]
+        for param in self.embed_tokens.parameters():
+            param.requires_grad = False
+
+
+    def init_tree(self):
+        self.tree = mc_sim_7b_63
+        self.tree_buffer=generate_tree_buffers(self.tree,self.embed_tokens.weight.device)
+
+
+    def reset(self):
+        self.tree_mask=None
+
+
+    def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                #inputs_embeds.dtype,
+                torch.float32, # [MODIFIED] force to cast to float32
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(attention_mask, torch.float32, tgt_len=input_shape[-1]).to(
+                inputs_embeds.device
+            )
+            combined_attention_mask = (
+                expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+            )
+
+        # [MODIFIED] add tree mask
+        if hasattr(self, "tree_mask") and self.tree_mask is not None:
+            tree_mask = self.tree_mask
+            tree_len = tree_mask.size(-1)
+            combined_attention_mask[:, :, -tree_len:, -tree_len:][
+                tree_mask == 0
+                ] = torch.finfo(torch.float32).min
+
+
+        return combined_attention_mask
+
+    def forward(
+        self,
+        hidden_states,
+        input_ids,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        std=None
+    ):
+        batch_size, seq_length, _ = hidden_states.shape
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        with torch.no_grad():
+            inputs_embeds = self.embed_tokens(input_ids)
+            #inputs_embeds = inputs_embeds.detach()
+
+        # if std is not None:
+        #     noise = torch.randn(inputs_embeds.size(),device=inputs_embeds.device) * std
+        #     inputs_embeds=inputs_embeds+noise
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+        if position_ids is None:
+            device = hidden_states.device if hidden_states is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask, (batch_size, seq_length), hidden_states, past_key_values_length
+        )
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                use_cache = False
+
+
+        #hidden_states=self.act(self.fc(torch.cat((inputs_embeds,hidden_states),dim=-1)))
+        inputs_embeds=inputs_embeds.to(hidden_states.dtype)
+        hidden_states = self.fc(torch.cat((inputs_embeds, hidden_states), dim=-1))
+
+
+        all_hidden_states = () if output_hidden_states else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+        if use_cache:
+            return hidden_states,next_decoder_cache
+
+        return hidden_states
+
+    @torch.no_grad()
+    def generate(self,hidden_states,input_ids,head,max_length=4,use_cache=False):
+        return_input_ids=copy.deepcopy(input_ids[0].tolist())
+        input_ids=input_ids[:,1:]
+
+        #input_ids=input_ids.to(hidden_states.device)
+        if use_cache:
+            past_key_values=None
+            for i in range(max_length):
+                if past_key_values!=None:
+                    out_hidden,past_key_values = self(out_hidden[:, -1:], input_ids=torch.tensor([[token]]).to(input_ids.device),past_key_values=past_key_values,use_cache=True)
+                else:
+                    out_hidden, past_key_values = self(hidden_states, input_ids=input_ids,use_cache=True)
+                last_hidden = out_hidden[:, -1]
+                last_headout = head(last_hidden)
+                token = torch.argmax(last_headout)
+                #input_ids = torch.cat((input_ids, torch.tensor([[token]]).to(input_ids.device)), dim=1)
+                return_input_ids.append(token.item())
+                if token == 2:
+                    break
+                #hidden_states = torch.cat((hidden_states, out_hidden[:, -1:]), dim=1)
+        else:
+            for i in range(max_length):
+                out_hidden=self(hidden_states,input_ids=input_ids)
+                last_hidden = out_hidden[:, -1]
+                last_headout = head(last_hidden)
+                token = torch.argmax(last_headout)
+                return_input_ids.append(token.item())
+                input_ids = torch.cat((input_ids, torch.tensor([[token]]).to(input_ids.device)), dim=1)
+                if token==2:
+                    break
+                hidden_states = torch.cat((hidden_states, out_hidden[:, -1:]), dim=1)
+
+        return return_input_ids
+
+    @torch.no_grad()
+    def repeat_kv(self,kv,numr):
+        newkv=[]
+        for i in kv:
+            newkv.append((i[0].repeat(numr,1,1,1),i[1].repeat(numr,1,1,1)))
+        return tuple(newkv)
+
+    @torch.no_grad()
+    def reduce_kv(self,kv,numr):
+        newkv=[]
+        for i in kv:
+            newkv.append((i[0][:numr],i[1][:numr]))
+        return tuple(newkv)
+
+
+    def reset_kv(self):
+        self.stable_kv=None
+
+    @torch.no_grad()
+    def topK_genrate_batch(self,hidden_states,input_ids,head,max_length=4,use_cache=True):
+        #input_ids = torch.tensor([state[1:]])
+        input_ids = input_ids[:, 1:]
+        input_ids = input_ids.to(hidden_states.device)
+        sslogits=[]
+        self.reset()
+        if use_cache:
+
+
+
+            out_hidden, past_key_values = self(hidden_states, input_ids=input_ids,use_cache=True)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+            topk_index = torch.topk(last_headout, 3, dim=-1).indices
+
+            # hidden_states = torch.cat((hidden_states, out_hidden[:, -1:]), dim=1)
+            hidden_states = out_hidden[:, -1:]
+            hidden_states = hidden_states.repeat(3, 1, 1)
+            #input_ids = input_ids.repeat(3, 1)
+            input_ids = topk_index.t()
+            past_key_values = self.repeat_kv(past_key_values,3)
+            out_hidden,past_key_values = self(hidden_states, input_ids=input_ids,past_key_values=past_key_values,use_cache=True)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+
+            hidden_states = out_hidden[0:1, -1:]
+            #input_ids = input_ids[:1]
+            topk_index = torch.topk(last_headout[:1], 3, dim=-1).indices
+            #hidden_states = torch.cat((hidden_states, out_hidden[0:1, -1:]), dim=1)
+            hidden_states = hidden_states.repeat(3, 1, 1)
+            #input_ids = input_ids.repeat(3, 1)
+            input_ids = topk_index.t()
+            out_hidden,past_key_values = self(hidden_states, input_ids=input_ids,past_key_values=past_key_values,use_cache=True)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+
+            #hidden_states = hidden_states[:1]
+            #input_ids = input_ids[:1]
+            topk_index = torch.topk(last_headout[:1], 3, dim=-1).indices
+            hidden_states = out_hidden[0:1, -1:]
+            input_ids = topk_index[:, :1]
+            past_key_values=self.reduce_kv(past_key_values,1)
+            out_hidden,past_key_values = self(hidden_states, input_ids=input_ids,past_key_values=past_key_values,use_cache=True)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+        else:
+            out_hidden = self(hidden_states, input_ids=input_ids)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+            topk_index=torch.topk(last_headout, 3, dim=-1).indices
+
+            hidden_states = torch.cat((hidden_states, out_hidden[:, -1:]), dim=1)
+            hidden_states=hidden_states.repeat(3,1,1)
+            input_ids=input_ids.repeat(3,1)
+            input_ids=torch.cat((input_ids,topk_index.t()),dim=-1)
+            out_hidden = self(hidden_states, input_ids=input_ids)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+
+            hidden_states=hidden_states[:1]
+            input_ids=input_ids[:1]
+            topk_index = torch.topk(last_headout[:1], 3, dim=-1).indices
+            hidden_states = torch.cat((hidden_states, out_hidden[0:1, -1:]), dim=1)
+            hidden_states = hidden_states.repeat(3, 1, 1)
+            input_ids = input_ids.repeat(3, 1)
+            input_ids = torch.cat((input_ids, topk_index.t()), dim=-1)
+            out_hidden = self(hidden_states, input_ids=input_ids)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+
+            hidden_states = hidden_states[:1]
+            input_ids = input_ids[:1]
+            topk_index = torch.topk(last_headout[:1], 3, dim=-1).indices
+            hidden_states = torch.cat((hidden_states, out_hidden[0:1, -1:]), dim=1)
+            input_ids = torch.cat((input_ids, topk_index[:,:1]), dim=-1)
+            out_hidden = self(hidden_states, input_ids=input_ids)
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+            sslogits.append(last_headout)
+
+        return torch.cat(sslogits)
+
+    @torch.no_grad()
+    def repeat_hidden(self,hidden_state,repeat_num):
+        new_hidden=[]
+        for id,i in enumerate(repeat_num):
+            new_hidden.append(hidden_state[:,id:id+1].repeat(1,i,1))
+        return torch.cat(new_hidden,dim=1)
+
+    @torch.no_grad()
+    def sample(self,tensor,k=1,replacement=True):
+        probabilities = torch.nn.functional.softmax(tensor, dim=1)
+        sampled_indices = torch.multinomial(probabilities, k,replacement=replacement)
+        sampled_probs = torch.gather(probabilities, 1, sampled_indices)
+
+        return  sampled_indices,sampled_probs
+
+    @torch.no_grad()
+    def topK_genrate(self, hidden_states, input_ids, head, logits_processor,max_length=4, use_cache=True):
+        # test_=input_ids
+        # input_ids = torch.tensor([state[1:]])
+        input_ids = input_ids[:, 1:]
+        input_ids = input_ids.to(hidden_states.device)
+        ss_token,ss_prob = [],[]
+        len_posi=input_ids.shape[1]
+        self.reset()
+        if use_cache:
+
+
+            if hasattr(self, "stable_kv") and self.stable_kv is not None:
+                kv_len=self.stable_kv[0][0].shape[2]
+                out_hidden, past_key_values = self(hidden_states[:,kv_len:], input_ids=input_ids[:,kv_len:], past_key_values=self.stable_kv,use_cache=True)
+            else:
+                out_hidden, past_key_values = self(hidden_states, input_ids=input_ids, use_cache=True)
+            self.stable_kv=past_key_values
+            last_hidden = out_hidden[:, -1]
+            last_headout = head(last_hidden)
+
+
+
+            for i in range(len(self.tree_buffer['tree_indices'])):
+                if logits_processor is not None:
+                    topk_index,topk_prob=self.sample(last_headout,top_k)
+                else:
+                    topk_index,topk_prob = torch.topk(last_headout, top_k, dim=-1).indices,torch.topk(last_headout, top_k, dim=-1).values
+
+                ss_token.append(topk_index)
+                ss_prob.append(topk_prob)
+                #topk_index = torch.topk(last_headout, top_k, dim=-1).indices
+                topk_index = topk_index.view(-1)
+                select_index=topk_index[self.tree_buffer['tree_indices'][i]]
+                #len_sq=select_index.shape[0]
+                input_ids=select_index[None,:]
+                if i==0:
+                    hidden_states = out_hidden[:, -1:]
+                else:
+                    hidden_states=out_hidden
+                hidden_states=self.repeat_hidden(hidden_states,self.tree_buffer["repeat_nums"][i])
+                #hidden_states = hidden_states.repeat(1,len_sq,1)
+                self.tree_mask=self.tree_buffer['attn_mask'][i]
+                position_ids=len_posi+self.tree_buffer["position_ids"][i]
+                out_hidden, past_key_values = self(hidden_states, input_ids=input_ids, past_key_values=past_key_values,
+                                                   position_ids=position_ids,use_cache=True)
+                len_posi += 1
+
+
+                last_headout = head(out_hidden[0])
+                #sslogits.append(last_headout)
+                #print(select_index)
+            topk_index, topk_prob = self.sample(last_headout, top_k)
+            ss_token.append(topk_index)
+            ss_prob.append(topk_prob)
+
+        else:
+            # TODO
+            pass
+
+        return (torch.cat(ss_token),torch.cat(ss_prob))
+
+
+
+
+    @torch.no_grad()
+    def acc(self,data,head,max_length=5):
+        hidden_states=data["hidden_states"]
+        input_ids=data["input_ids"]
+        #attention_mask=data["attention_mask"]
+        loss_mask=data["loss_mask"]
+        sample_mask=data["sample_mask"]
+        target=data["target"]
+        total=[0 for _ in range(max_length)]
+        correct=[0 for _ in range(max_length)]
+        bs,sl=hidden_states.shape[0],hidden_states.shape[1]
+        target_headout = head(target)
+        hidden_states_headout=head(hidden_states)
+
+        for i in range(bs):
+            for j in range(sl):
+                if loss_mask[i,j]==0:
+                    continue
+                single_hidden_states=hidden_states[i,:j]
+                single_input_ids=input_ids[i,:j]
+
+
+                single_hidden_states = single_hidden_states[None, :, :]
+                single_input_ids = single_input_ids[None, :]
+                for k in range(max_length):
+                    tmp_in_target_headout = hidden_states_headout[i,single_hidden_states.shape[1]-1]
+                    tmp_out_target_headout = target_headout[i, single_hidden_states.shape[1]-1]
+                    target_in_token = torch.argmax(tmp_in_target_headout)
+                    target_out_token = torch.argmax(tmp_out_target_headout)
+                    tmp_token=input_ids[i,single_hidden_states.shape[1]-1]
+                    tmp_sample_mask=sample_mask[i,single_hidden_states.shape[1]-1]
+                    if not (target_in_token==tmp_token):
+                        break
+                    out_hidden = self(single_hidden_states, input_ids=single_input_ids)
+                    last_hidden = out_hidden[:, -1]
+                    last_headout = head(last_hidden)
+                    token = torch.argmax(last_headout)
+                    total[k] += 1
+                    if token==target_out_token:
+                        correct[k]+=1
+                    else:
+                        for kk in range(k,max_length):
+                            total[kk]+=1
+                        break
+
+                    single_hidden_states=torch.cat((single_hidden_states,out_hidden[:,-1:]),dim=1)
+                    single_input_ids = torch.cat((single_input_ids, torch.tensor([[token]]).to(single_input_ids.device)), dim=1)
+
+
+        acc=[correct[i]/total[i] for i in range(len(correct))]
+        return acc
+
+
+
+
+
+class Vhead(nn.Module):
+    def __init__(self,ins=6566,outs=32000):
+        super().__init__()
+        self.fc = nn.Linear(ins,outs,bias=False)
+    def forward(self,x):
+        return self.fc(x)
+
+
+
+import torch
+
+def count_parameters(model):
+    return sum(p.numel() for p in model.parameters())
+
+
+
+
+if __name__=='__main__':
+    import time
+    config=EConfig.from_pretrained('config.json')
+    model=Model(config)
+    model.cuda()
+    model.init_tree()
+    #model.half()
+    model.eval()
+    total_params = count_parameters(model)
+    print(f"总参数量: {total_params}")
+    head = torch.nn.Linear(6656, 32000, bias=False)
+    head.load_state_dict(torch.load("/home/lyh/code/nlp/ess/transhead_embeding_long/headf32.ckpt"))
+    head.cuda()
+    head.eval()
+    logits_processor=prepare_logits_processor()
+    with torch.no_grad():
+        ins=torch.randn(1,499,6656)
+        input_ids=torch.tensor([[29915,385,299,365,395]*100])
+        attention_mask=torch.tensor([[1,0,1]])
+        out0 = model.generate(ins.cuda(), input_ids.cuda(), head, use_cache=True)
+        out = model.generate(ins.cuda(), input_ids.cuda(), head, use_cache=False)
+        #model(ins,input_ids=input_ids,attention_mask=attention_mask)
+        outk=model.topK_genrate_batch(ins.cuda(),input_ids.cuda(),head,use_cache=False)
+        model.reset_kv()
+        outkcache = model.topK_genrate(ins[:,:400].cuda(), input_ids[:,:401].cuda(), head, None,use_cache=True)
+        outkcache1 = model.topK_genrate(ins.cuda(), input_ids.cuda(), head, use_cache=True)
+        #s=time.time()
+        # out0 = model.generate(ins.cuda(), input_ids.cuda(), head,use_cache=True)
+        # outs,past_key_values=model(ins[:,:2],input_ids=input_ids[:,:2],use_cache=True)
+        # outs1, past_key_values1 = model(ins[:,2:], input_ids=input_ids[:,2:], use_cache=True,past_key_values=past_key_values)
+        # outs0, past_key_values0 = model(ins, input_ids=input_ids, use_cache=True)
+        #print(time.time()-s)
+        for _ in range(10):
+            s = time.time()
+            outk=model.topK_genrate_batch(ins.cuda(),input_ids.cuda(),head,use_cache=True)
+            print(time.time() - s)
+
+        print('---'*10)
+
+        for _ in range(10):
+            s = time.time()
+            outk=model.topK_genrate(ins.cuda(),input_ids.cuda(),head,use_cache=True)
+            print(time.time() - s)

model/config.json

@@ -0,0 +1,23 @@
+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 6656,
+  "initializer_range": 0.02,
+  "intermediate_size": 17920,
+  "max_sequence_length": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 52,
+  "num_key_value_heads": 13,
+  "num_hidden_layers": 1,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

model/configs.py

@@ -0,0 +1,145 @@
+from transformers.configuration_utils import PretrainedConfig
+class EConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`LlamaModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        pretraining_tp (`int`, *optional*, defaults to `1`):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-12):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
+            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+
+        Example:
+
+    ```python
+    >>> from transformers import LlamaModel, LlamaConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = LlamaConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = LlamaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    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,
+        **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
+
+        # 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_scaling = rope_scaling
+        self._rope_scaling_validation()
+
+        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,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

model/ea_model.py

@@ -0,0 +1,415 @@
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel, PretrainedConfig
+from .modeling_llama_kv import LlamaForCausalLM as KVLlamaForCausalLM
+from .utils import *
+from .kv_cache import initialize_past_key_values
+from .choices import mc_sim_7b_63
+from transformers import AutoTokenizer
+import os
+from huggingface_hub import hf_hub_download
+from .cnets import Model
+from .configs import EConfig
+
+
+
+
+
+class ResBlock(nn.Module):
+    """
+    A Residual Block module.
+
+    This module performs a linear transformation followed by a SiLU activation,
+    and then adds the result to the original input, creating a residual connection.
+
+    Args:
+        hidden_size (int): The size of the hidden layers in the block.
+    """
+
+    def __init__(self, hidden_size):
+        super().__init__()
+        self.linear = nn.Linear(hidden_size, hidden_size)
+        # Initialize as an identity mapping
+        torch.nn.init.zeros_(self.linear.weight)
+        # Use SiLU activation to keep consistent with the Llama model
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        """
+        Forward pass of the ResBlock.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Output after the residual connection and activation.
+        """
+        return x + self.act(self.linear(x))
+
+
+class EaModel(nn.Module):
+
+
+    def __init__(
+        self,
+        base_model,
+        base_model_name_or_path,
+        ea_model_path,
+    ):
+
+        super().__init__()
+        self.base_model = base_model
+        self.config = base_model.config
+        self.hidden_size = base_model.lm_head.weight.shape[-1]
+        self.vocab_size = base_model.lm_head.weight.shape[0]
+        self.base_model_name_or_path = base_model_name_or_path
+        self.tokenizer = AutoTokenizer.from_pretrained(self.base_model_name_or_path)
+        config = EConfig.from_pretrained(ea_model_path)
+        self.ea_layer = Model(config)
+
+
+        device = base_model.model.layers[-1].self_attn.q_proj.weight.device
+        self.ea_layer.to(self.base_model.dtype).to(device)
+        self.ea_layer.init_tree()
+
+
+
+    def get_tokenizer(self):
+        """Get the tokenizer of the base model.
+
+        Returns:
+            Tokenizer: The tokenizer of the base model.
+        """
+        return self.tokenizer
+
+    @classmethod
+    def from_pretrained(
+        cls,
+        base_model_path=None,
+        ea_model_path=None,
+        **kwargs,
+    ):
+
+
+            
+        base_model = KVLlamaForCausalLM.from_pretrained(
+            base_model_path, **kwargs
+        )
+
+        model = cls(
+            base_model,
+            base_model_path,
+            ea_model_path
+        )
+
+        ea_layer_state_dict = torch.load(os.path.join(ea_model_path,"pytorch_model.bin"), map_location=base_model.device)
+        model.ea_layer.load_state_dict(ea_layer_state_dict, strict=False)
+
+        return model
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        labels=None,
+        past_key_values=None,
+        output_orig=False,
+        position_ids=None,
+        init=True,
+        logits_processor=None
+    ):
+
+
+        with torch.inference_mode():
+            # Pass input through the base model
+            outputs = self.base_model.model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                position_ids=position_ids,
+            )
+            if output_orig:
+                orig = self.base_model.lm_head(outputs[0])
+            hidden_states = outputs[0].clone()
+        if init:
+            if logits_processor is not None:
+                logits=orig[:, -1]
+                logits=logits_processor(None,logits)
+                probabilities = torch.nn.functional.softmax(logits, dim=1)
+                token=torch.multinomial(probabilities, 1)
+            else:
+                token = torch.argmax(orig[:,-1])
+                token=token[None,None]
+            input_ids=torch.cat((input_ids,token.to(input_ids.device)),dim=1)
+            # Clone the output hidden states
+
+            ea_logits = self.ea_layer.topK_genrate(hidden_states,input_ids,self.base_model.lm_head,logits_processor)
+            if output_orig:
+                return ea_logits, outputs, orig,hidden_states,token
+            return ea_logits,hidden_states,token
+        else:
+            if output_orig:
+                return outputs,orig,hidden_states
+
+    @torch.no_grad()
+    def eagenerate(
+        self,
+        input_ids,
+        temperature=0.0,
+        top_p=0.0,
+        top_k=0.0,
+        max_new_tokens=512,
+        max_length=2048,
+        tree_choices=mc_sim_7b_63,
+
+    ):
+        if temperature>1e-5:
+            logits_processor=prepare_logits_processor(temperature=temperature,top_p=top_p,top_k=top_k)
+        else:
+            logits_processor=None
+        assert input_ids.shape[0] == 1, "Only support batch size 1 for now!!"
+        # Avoid modifying the input_ids in-place
+        input_ids = input_ids.clone()
+        self.ea_layer.reset_kv()
+
+        if hasattr(self, "tree_choices") and self.tree_choices == tree_choices:
+            tree_buffers = self.tree_buffers
+        else:
+            tree_buffers = generate_tree_buffers(
+                tree_choices, device=self.base_model.model.layers[-1].self_attn.q_proj.weight.device
+            )
+        self.tree_buffers = tree_buffers
+        self.tree_choices = tree_choices
+
+        # Initialize the past key and value states
+        if hasattr(self, "past_key_values"):
+            past_key_values = self.past_key_values
+            past_key_values_data = self.past_key_values_data
+            current_length_data = self.current_length_data
+            # Reset the past key and value states
+            current_length_data.zero_()
+        else:
+            (
+                past_key_values,
+                past_key_values_data,
+                current_length_data,
+            ) = initialize_past_key_values(self.base_model)
+            self.past_key_values = past_key_values
+            self.past_key_values_data = past_key_values_data
+            self.current_length_data = current_length_data
+
+        input_len = input_ids.shape[1]
+        reset_tree_mode(self)
+        tree_logits, logits, hidden_state, sample_token = initialize_tree(
+            input_ids, self, tree_buffers["tree_attn_mask"], past_key_values, logits_processor
+        )
+        new_token = 0
+
+        for idx in range(max_length):
+            candidates, cart_candidates_prob, tree_candidates = generate_candidates(
+                tree_logits,
+                tree_buffers["tree_indices"],
+                tree_buffers["retrieve_indices"],
+                sample_token,
+                logits_processor
+            )
+            logits, hidden_state_new, outputs = tree_decoding(
+                self,
+                tree_candidates,
+                past_key_values,
+                tree_buffers["tree_position_ids"],
+                input_ids,
+                tree_buffers["retrieve_indices"],
+            )
+            best_candidate, accept_length, sample_p = evaluate_posterior(
+                logits, candidates, logits_processor, cart_candidates_prob
+            )
+            input_ids, tree_logits, new_token, hidden_state, sample_token = update_inference_inputs(
+                input_ids,
+                candidates,
+                best_candidate,
+                accept_length,
+                tree_buffers["retrieve_indices"],
+                logits_processor,
+                logits,
+                tree_logits,
+                new_token,
+                past_key_values_data,
+                current_length_data,
+                self,
+                hidden_state,
+                hidden_state_new,
+                sample_p
+            )
+
+
+            if self.tokenizer.eos_token_id in input_ids[0, input_len:].tolist():
+                return input_ids
+            if new_token > max_new_tokens:
+                return input_ids
+            if input_ids.shape[1] > max_length:
+                return input_ids
+
+    @torch.no_grad()
+    def ea_generate(
+            self,
+            input_ids,
+            temperature=0.0,
+            top_p=0.0,
+            top_k=0.0,
+            max_steps=512,
+            tree_choices=mc_sim_7b_63,
+
+    ):
+        if temperature > 1e-5:
+            logits_processor = prepare_logits_processor(temperature=temperature, top_p=top_p, top_k=top_k)
+        else:
+            logits_processor = None
+        assert input_ids.shape[0] == 1, "Only support batch size 1 for now!!"
+        # Avoid modifying the input_ids in-place
+        input_ids = input_ids.clone()
+        self.ea_layer.reset_kv()
+
+        if hasattr(self, "tree_choices") and self.tree_choices == tree_choices:
+            tree_buffers = self.tree_buffers
+        else:
+            tree_buffers = generate_tree_buffers(
+                tree_choices, device=self.base_model.model.layers[-1].self_attn.q_proj.weight.device
+            )
+        self.tree_buffers = tree_buffers
+        self.tree_choices = tree_choices
+
+        # Initialize the past key and value states
+        if hasattr(self, "past_key_values"):
+            past_key_values = self.past_key_values
+            past_key_values_data = self.past_key_values_data
+            current_length_data = self.current_length_data
+            # Reset the past key and value states
+            current_length_data.zero_()
+        else:
+            (
+                past_key_values,
+                past_key_values_data,
+                current_length_data,
+            ) = initialize_past_key_values(self.base_model)
+            self.past_key_values = past_key_values
+            self.past_key_values_data = past_key_values_data
+            self.current_length_data = current_length_data
+
+        input_len = input_ids.shape[1]
+        reset_tree_mode(self)
+        tree_logits, logits, hidden_state, sample_token = initialize_tree(
+            input_ids, self, tree_buffers["tree_attn_mask"], past_key_values, logits_processor
+        )
+        new_token = 0
+
+        for idx in range(max_steps):
+            candidates, cart_candidates_prob, tree_candidates = generate_candidates(
+                tree_logits,
+                tree_buffers["tree_indices"],
+                tree_buffers["retrieve_indices"],
+                sample_token,
+                logits_processor
+            )
+            logits, hidden_state_new, outputs = tree_decoding(
+                self,
+                tree_candidates,
+                past_key_values,
+                tree_buffers["tree_position_ids"],
+                input_ids,
+                tree_buffers["retrieve_indices"],
+            )
+            best_candidate, accept_length, sample_p = evaluate_posterior(
+                logits, candidates, logits_processor, cart_candidates_prob
+            )
+            input_ids, tree_logits, new_token, hidden_state, sample_token = update_inference_inputs(
+                input_ids,
+                candidates,
+                best_candidate,
+                accept_length,
+                tree_buffers["retrieve_indices"],
+                logits_processor,
+                logits,
+                tree_logits,
+                new_token,
+                past_key_values_data,
+                current_length_data,
+                self,
+                hidden_state,
+                hidden_state_new,
+                sample_p
+            )
+
+            yield input_ids
+
+            if self.tokenizer.eos_token_id in input_ids[0, input_len:].tolist():
+                break
+            if new_token > 1024:
+                break
+            if input_ids.shape[1] > 1960:
+                break
+
+    @torch.no_grad()
+    def naive_generate(
+            self,
+            input_ids,
+            temperature=0.0,
+            top_p=0.0,
+            top_k=0.0,
+            max_steps=512,
+            tree_choices=mc_sim_7b_63,
+
+    ):
+        if temperature > 1e-5:
+            logits_processor = prepare_logits_processor(temperature=temperature, top_p=top_p, top_k=top_k)
+        else:
+            logits_processor = None
+        assert input_ids.shape[0] == 1, "Only support batch size 1 for now!!"
+        # Avoid modifying the input_ids in-place
+        input_ids = input_ids.clone()
+        self.ea_layer.reset_kv()
+
+        if hasattr(self, "tree_choices") and self.tree_choices == tree_choices:
+            tree_buffers = self.tree_buffers
+        else:
+            tree_buffers = generate_tree_buffers(
+                tree_choices, device=self.base_model.model.layers[-1].self_attn.q_proj.weight.device
+            )
+        self.tree_buffers = tree_buffers
+        self.tree_choices = tree_choices
+
+        # Initialize the past key and value states
+        if hasattr(self, "past_key_values"):
+            past_key_values = self.past_key_values
+            past_key_values_data = self.past_key_values_data
+            current_length_data = self.current_length_data
+            # Reset the past key and value states
+            current_length_data.zero_()
+        else:
+            (
+                past_key_values,
+                past_key_values_data,
+                current_length_data,
+            ) = initialize_past_key_values(self.base_model)
+            self.past_key_values = past_key_values
+            self.past_key_values_data = past_key_values_data
+            self.current_length_data = current_length_data
+
+        input_len = input_ids.shape[1]
+        reset_tree_mode(self)
+        outputs = self.base_model(input_ids, past_key_values=past_key_values, use_cache=True)
+        new_token = 0
+
+        for idx in range(max_steps):
+            input_id = outputs.logits[:, -1:].argmax(dim=-1)
+            outputs = self.base_model(input_id, use_cache=True, past_key_values=past_key_values)
+            input_ids = torch.cat([input_ids, input_id], dim=-1)
+
+            yield input_ids
+
+            if self.tokenizer.eos_token_id in input_ids[0, input_len:].tolist():
+                break
+            if new_token > 1024:
+                break
+            if input_ids.shape[1] > 1960:
+                break

model/kv_cache.py

@@ -0,0 +1,148 @@
+import torch
+
+
+class KVCache:
+    """
+    A key-value cache for the model.
+
+    This class provides a mechanism to maintain a growing cache of keys and values,
+    particularly useful for models that benefit from caching previous states,
+    like transformers during autoregressive decoding.
+
+    Attributes:
+        data (torch.Tensor): The tensor storing keys and values.
+        current_length (int): Current length of the data being stored.
+    """
+
+    def __init__(self, data, current_length):
+        """
+        Initialize the KVCache.
+
+        Args:
+            data (torch.Tensor): Initial tensor to store the keys and values.
+            current_length (int): Initial length of the data.
+        """
+        self.data = data
+        self.current_length = current_length
+
+    @property
+    def shape(self):
+        """Return the shape of the data tensor with updated length."""
+        return (
+            self.data.shape[0],
+            self.data.shape[1],
+            self.current_length.item(),
+            self.data.shape[3],
+        )
+
+    def copy(self, indices: torch.Tensor, prev_length: int, dim: int = 2):
+        """
+        Copy values from the current data at specified indices to a new location.
+
+        Args:
+            indices (torch.Tensor): Indices of the data tensor to be copied.
+            prev_length (int): Previous length before adding new data.
+            dim (int, optional): Dimension along which copying should be performed. Default is 2.
+        """
+        tgt = self.data.index_select(dim, indices)
+        dst = self.data.narrow(dim, prev_length, tgt.shape[dim])
+        dst.copy_(tgt, non_blocking=True)
+        self.current_length.fill_(prev_length + tgt.shape[dim])
+
+    def cat(self, tensor: torch.Tensor, dim: int = 2):
+        """
+        Concatenate the given tensor with the current data.
+
+        Args:
+            tensor (torch.Tensor): The tensor to be concatenated.
+            dim (int, optional): The dimension along which concatenation should be done. Default is 2.
+
+        Returns:
+            torch.Tensor: The data tensor after concatenation up to the current length.
+        """
+        dst = self.data.narrow(dim, self.current_length, tensor.shape[dim])
+        dst.copy_(tensor)
+        self.current_length.add_(tensor.shape[dim])
+        return torch.narrow(self.data, 2, 0, self.current_length)
+
+
+def initialize_past_key_values(model):
+    """
+    Initialize past key and value states for a given transformer model.
+
+    This function prepares key-value cache structures for the model, allowing it to store and reuse
+    past key and value states during autoregressive decoding, which can improve efficiency.
+
+    Args:
+        model (nn.Module): The transformer model for which past key-value states need to be initialized.
+
+    Returns:
+        tuple:
+            - past_key_values (list): A list of KVCache objects for each layer in the model.
+            - past_key_values_data (torch.Tensor): The tensor that will store all keys and values.
+            - current_length_data (torch.Tensor): A tensor tracking the current length of keys/values in the cache.
+    """
+    # Extracting configuration from the model
+    config = model.config
+    # Initializing the batch size to 1, this can be modified if different batch sizes are required
+    batch_size = 1
+    # Initializing a tensor to store past keys and values for all layers
+
+    devices=[]
+    for i in range(config.num_hidden_layers):
+        try:
+            device = model.model.layers[i].self_attn.q_proj.weight.device
+        except:
+            device=model.layers[i].self_attn.q_proj.weight.device
+        devices.append(device)
+    past_key_values_data_list=[]
+    startnum=0
+    startdevice=devices[0]
+    for id,i in enumerate(devices):
+        if startdevice!=i:
+            past_key_values_data = torch.zeros(
+                startnum * 2,
+                batch_size,
+                config.num_key_value_heads,
+                config.max_position_embeddings,
+                config.hidden_size // config.num_attention_heads,
+                device=startdevice,
+                dtype=model.dtype,
+            )
+            past_key_values_data_list.append(past_key_values_data)
+            startdevice = i
+            startnum=0
+        startnum += 1
+    past_key_values_data = torch.zeros(
+        startnum * 2,
+        batch_size,
+        config.num_key_value_heads,
+        config.max_position_embeddings,
+        config.hidden_size // config.num_attention_heads,
+        device=startdevice,
+        dtype=model.dtype,
+    )
+    past_key_values_data_list.append(past_key_values_data)
+    # Initialize tensor to store the current length of the cached data for all layers.
+    # [IMPORTANT] It needs to be kept on CPU for quick access and updates.
+    current_length_data = torch.zeros(
+        config.num_hidden_layers * 2, dtype=torch.long, device="cpu"
+    )
+    # Creating a KVCache for each pair of key and value in all layers
+    past_key_values = [] * config.num_hidden_layers
+
+    bias=0
+    start_data_m=devices[0].index
+    for i in range(config.num_hidden_layers):
+        data_m=devices[i].index
+        if data_m!=start_data_m:
+            bias=0
+            start_data_m=data_m
+        past_key_values.append(
+            [
+                KVCache(past_key_values_data_list[data_m-devices[0].index][2*bias + j], current_length_data[i * 2 + j])
+                for j in range(2)
+            ]
+        )
+        bias+=1
+    return past_key_values, past_key_values_data_list, current_length_data

model/modeling_llama_kv.py

@@ -0,0 +1,1398 @@
+# Source: https://github.com/huggingface/transformers/blob/v4.31-release/src/transformers/models/llama/modeling_llama.py
+# Modifications are denoted by the symbol: [MODIFIED]
+
+
+""" PyTorch LLaMA model."""
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+# [MODIFIED] Import from transformer library
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers import LlamaConfig
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "LlamaConfig"
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+        input_ids_shape: torch.Size,
+        dtype: torch.dtype,
+        device: torch.device,
+        past_key_values_length: int = 0,
+):
+    """
+    Create a causal mask for bi-directional self-attention.
+
+    Args:
+        input_ids_shape (torch.Size): The shape of input_ids tensor, typically (batch_size, tgt_len).
+        dtype (torch.dtype): The data type of the mask.
+        device (torch.device): The device on which the mask will be placed.
+        past_key_values_length (int, optional): The length of past key values. Default is 0.
+
+    Returns:
+        torch.Tensor: The causal mask tensor.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    tgt_len, past_key_values_length, dtype=dtype, device=device
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    return mask[None, None, :, :].expand(
+        bsz, 1, tgt_len, tgt_len + past_key_values_length
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expand attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+
+    Args:
+        mask (torch.Tensor): The attention mask tensor of shape `[bsz, seq_len]`.
+        dtype (torch.dtype): The data type of the mask.
+        tgt_len (Optional[int], optional): The target sequence length. If None, it defaults to the source sequence length.
+
+    Returns:
+        torch.Tensor: The expanded mask tensor.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+import torch.nn as nn
+import torch
+
+
+class LlamaRMSNorm(nn.Module):
+    """
+    LlamaRMSNorm is equivalent to T5LayerNorm.
+
+    Args:
+        hidden_size (int): The size of the hidden states.
+        eps (float, optional): A small value to prevent division by zero. Default is 1e-6.
+    """
+
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        """
+        Apply LlamaRMSNorm to the input hidden states.
+
+        Args:
+            hidden_states (torch.Tensor): Input hidden states.
+
+        Returns:
+            torch.Tensor: The normalized and scaled hidden states.
+        """
+        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 LlamaRotaryEmbedding(nn.Module):
+    """
+    Llama Rotary Positional Embedding Module.
+
+    Args:
+        dim (int): The dimension of the embedding.
+        max_position_embeddings (int, optional): The maximum position for embeddings. Default is 2048.
+        base (int, optional): The base value for rotational encoding. Default is 10000.
+        device (str, optional): The device on which the computation will be performed. Default is None.
+    """
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (
+                self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+        )
+        self.register_buffer("inv_freq", inv_freq)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings,
+            device=self.inv_freq.device,
+            dtype=torch.get_default_dtype(),
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        """
+        Set the cosine and sine cache for positional embeddings.
+
+        Args:
+            seq_len (int): The sequence length.
+            device (str): The device on which the cache tensors will be stored.
+            dtype: The data type of the cache tensors.
+        """
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
+        )
+
+    def forward(self, x, seq_len=None):
+        """
+        Forward pass of the LlamaRotaryEmbedding module.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape [bs, num_attention_heads, seq_len, head_size].
+            seq_len (int): The sequence length. If greater than the cached length, the cache will be updated.
+
+        Returns:
+            tuple: A tuple containing two tensors, the cosine and sine embeddings, both of shape [1, 1, seq_len, dim].
+        """
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+            self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+        )
+
+
+class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """
+    LlamaRotaryEmbedding extended with linear scaling.
+
+    This class adds linear scaling to LlamaRotaryEmbedding. Credits to the Reddit user /u/kaiokendev.
+
+    Args:
+        dim (int): The dimension of the embedding.
+        max_position_embeddings (int, optional): The maximum number of position embeddings. Default is 2048.
+        base (int, optional): The base value for the rotational embeddings. Default is 10000.
+        device (str or torch.device, optional): The device where the embeddings should be stored. Default is None.
+        scaling_factor (float, optional): The scaling factor for the embeddings. Default is 1.0.
+    """
+
+    def __init__(
+            self,
+            dim,
+            max_position_embeddings=2048,
+            base=10000,
+            device=None,
+            scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        """
+        Set the cosine and sine cache for the rotary embeddings.
+
+        Args:
+            seq_len (int): The sequence length.
+            device (str or torch.device): The device where the cache should be stored.
+            dtype: The data type for the cache.
+        """
+        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.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
+        )
+
+
+class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
+    """
+    LlamaRotaryEmbedding extended with Dynamic NTK scaling.
+
+    Credits to the Reddit users /u/bloc97 and /u/emozilla.
+    """
+
+    def __init__(
+            self,
+            dim,
+            max_position_embeddings=2048,
+            base=10000,
+            device=None,
+            scaling_factor=1.0,
+    ):
+        """
+        Initialize the LlamaDynamicNTKScalingRotaryEmbedding.
+
+        Args:
+            dim (int): The dimensionality of the embedding.
+            max_position_embeddings (int, optional): Maximum number of position embeddings. Default is 2048.
+            base (int, optional): Base value for scaling calculations. Default is 10000.
+            device: The device to place tensors on. If None, uses the default device.
+            scaling_factor (float, optional): Scaling factor for NTK scaling. Default is 1.0.
+        """
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        """
+        Set the cached values for cosine and sine.
+
+        Args:
+            seq_len (int): The sequence length.
+            device: The device to place tensors on.
+            dtype: The data type of tensors.
+        """
+        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)
+
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+
+        freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer(
+            "cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
+        )
+
+
+def rotate_half(x):
+    """
+    Rotates half the hidden dimensions of the input.
+
+    Args:
+        x (torch.Tensor): Input tensor.
+
+    Returns:
+        torch.Tensor: Tensor with half of its hidden dimensions rotated.
+    """
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    """
+    Apply rotary position embeddings to query and key tensors.
+
+    Args:
+        q (torch.Tensor): Query tensor.
+        k (torch.Tensor): Key tensor.
+        cos (torch.Tensor): Cosine values.
+        sin (torch.Tensor): Sine values.
+        position_ids (torch.Tensor): Position IDs.
+
+    Returns:
+        torch.Tensor: Query and key tensors with rotary position embeddings applied.
+    """
+    cos = cos.squeeze(1).squeeze(0)
+    sin = sin.squeeze(1).squeeze(0)
+    cos = cos[position_ids].unsqueeze(1)
+    sin = sin[position_ids].unsqueeze(1)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class LlamaMLP(nn.Module):
+    """
+    LlamaMLP is a multi-layer perceptron module used in the Llama model.
+
+    Args:
+        config: The configuration for the MLP.
+
+    Attributes:
+        pretraining_tp (int): The pretraining time periods.
+        hidden_size (int): The size of the hidden layer.
+        intermediate_size (int): The size of the intermediate layer.
+        gate_proj (nn.Linear): The linear projection for gating.
+        up_proj (nn.Linear): The linear projection for the up projection.
+        down_proj (nn.Linear): The linear projection for the down projection.
+        act_fn: The activation function.
+
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.pretraining_tp = config.pretraining_tp
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        """
+        Forward pass of the MLP.
+
+        Args:
+            x: Input tensor.
+
+        Returns:
+            torch.Tensor: Output tensor.
+        """
+        if self.pretraining_tp > 1:
+            slice = self.intermediate_size // self.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.pretraining_tp)],
+                dim=-1,
+            )
+            up_proj = torch.cat(
+                [F.linear(x, up_proj_slices[i]) for i in range(self.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.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    Repeat key and value tensors n times along the specified dimension.
+
+    Args:
+        hidden_states (torch.Tensor): Input tensor with shape (batch, num_key_value_heads, seqlen, head_dim).
+        n_rep (int): Number of times to repeat.
+
+    Returns:
+        torch.Tensor: Repeated tensor with shape (batch, num_key_value_heads * n_rep, seqlen, head_dim).
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(
+        batch, num_key_value_heads, n_rep, slen, head_dim
+    )
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class LlamaAttention(nn.Module):
+    """
+    LlamaAttention is a multi-headed attention module based on the 'Attention Is All You Need' paper.
+
+    Args:
+        config (LlamaConfig): Configuration for the attention module.
+
+    Attributes:
+        config (LlamaConfig): Configuration for the attention module.
+        hidden_size (int): The size of the hidden layer.
+        num_heads (int): The number of attention heads.
+        head_dim (int): The dimension of each attention head.
+        num_key_value_heads (int): The number of key-value attention heads.
+        num_key_value_groups (int): The number of key-value groups.
+        pretraining_tp (int): The pretraining time periods.
+        max_position_embeddings (int): The maximum position embeddings.
+
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.pretraining_tp = config.pretraining_tp
+        self.max_position_embeddings = config.max_position_embeddings
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+        self.q_proj = nn.Linear(
+            self.hidden_size, self.num_heads * self.head_dim, bias=False
+        )
+        self.k_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.o_proj = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=False
+        )
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = LlamaRotaryEmbedding(
+                self.head_dim, max_position_embeddings=self.max_position_embeddings
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
+            .transpose(1, 2)
+            .contiguous()
+        )
+
+    def forward(
+            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: bool = False,
+            use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.pretraining_tp > 1:
+            key_value_slicing = (
+                                        self.num_key_value_heads * self.head_dim
+                                ) // self.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.pretraining_tp, dim=0
+            )
+            key_slices = self.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.pretraining_tp)
+            ]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [
+                F.linear(hidden_states, key_slices[i])
+                for i in range(self.pretraining_tp)
+            ]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [
+                F.linear(hidden_states, value_slices[i])
+                for i in range(self.pretraining_tp)
+            ]
+            value_states = torch.cat(value_states, 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)
+        key_states = key_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+        value_states = value_states.view(
+            bsz, q_len, self.num_key_value_heads, self.head_dim
+        ).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value[0].shape[-2]
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(
+            query_states, key_states, cos, sin, position_ids
+        )
+
+        # [MODIFIED] Using KVCache mechanism for preallocated GPU memory optimization
+        # past_key_value is utilized to leverage previously computed key and value states.
+        # If past_key_value is available, reuse the states for k, v, and self_attention.
+        if past_key_value is not None:
+            key_states = past_key_value[0].cat(key_states, dim=2)
+            value_states = past_key_value[1].cat(value_states, dim=2)
+        # Reset past_key_value to avoid return past_key_value.
+        past_key_value = None
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(
+            query_states, key_states.transpose(2, 3)
+        ) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(
+            attn_weights, dim=-1, dtype=torch.float32
+        ).to(query_states.dtype)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        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, self.hidden_size)
+
+        if self.pretraining_tp > 1:
+            attn_output = attn_output.split(
+                self.hidden_size // self.pretraining_tp, dim=2
+            )
+            o_proj_slices = self.o_proj.weight.split(
+                self.hidden_size // self.pretraining_tp, dim=1
+            )
+            attn_output = sum(
+                [
+                    F.linear(attn_output[i], o_proj_slices[i])
+                    for i in range(self.pretraining_tp)
+                ]
+            )
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class LlamaDecoderLayer(nn.Module):
+    """
+    LlamaDecoderLayer represents a single layer of the Llama decoder.
+
+    Args:
+        config (LlamaConfig): Configuration for the decoder layer.
+
+    Attributes:
+        hidden_size (int): The size of the hidden layer.
+        self_attn (LlamaAttention): Multi-headed self-attention module.
+        mlp (LlamaMLP): Multi-layer perceptron module.
+        input_layernorm (LlamaRMSNorm): Layer normalization for input.
+        post_attention_layernorm (LlamaRMSNorm): Layer normalization after self-attention.
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = LlamaAttention(config=config)
+        self.mlp = LlamaMLP(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 forward(
+            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,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Forward pass for the LlamaDecoderLayer.
+
+        Args:
+            hidden_states (torch.FloatTensor): Input tensor of shape `(batch, seq_len, embed_dim)`.
+            attention_mask (torch.FloatTensor, optional): Attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            position_ids (torch.LongTensor, optional): Positional IDs tensor.
+            past_key_value (Tuple[torch.FloatTensor], optional): Cached past key and value projection states.
+            output_attentions (bool, optional): Whether or not to return the attentions tensors of all attention layers.
+            use_cache (bool, optional): If set to `True`, `past_key_values` key-value states are returned and can be
+                used to speed up decoding.
+
+        Returns:
+            Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: Tuple containing:
+                - hidden_states (torch.FloatTensor): Output tensor.
+                - self_attn_weights (Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]): Self-attention weights if
+                  `output_attentions` is `True`.
+                - present_key_value (Optional[Tuple[torch.FloatTensor]]): Cached key and value projection states if
+                  `use_cache` is `True`.
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_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,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+LLAMA_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`LlamaConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaPreTrainedModel(PreTrainedModel):
+    config_class = LlamaConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, LlamaModel):
+            module.gradient_checkpointing = value
+
+
+LLAMA_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
+            `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
+            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
+
+            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
+    LLAMA_START_DOCSTRING,
+)
+class LlamaModel(LlamaPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = nn.ModuleList(
+            [LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        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 get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+            self, attention_mask, input_shape, inputs_embeds, past_key_values_length
+    ):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                # inputs_embeds.dtype,
+                torch.float32,  # [MODIFIED] force to cast to float32
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = (
+                expanded_attn_mask
+                if combined_attention_mask is None
+                else expanded_attn_mask + combined_attention_mask
+            )
+
+
+        if hasattr(self, "tree_mask") and self.tree_mask is not None:
+            tree_mask = self.tree_mask
+            tree_len = tree_mask.size(-1)
+            combined_attention_mask[:, :, -tree_len:, -tree_len:][
+                tree_mask == 0
+                ] = combined_attention_mask.min()
+
+        return combined_attention_mask
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values=None,  # [MODIFIED] past_key_value is KVCache class
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        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
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_length
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length,
+                seq_length + past_key_values_length,
+                dtype=torch.long,
+                device=device,
+            )
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            # if idx==16:
+            #     print(idx)
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_value,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            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 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 LlamaForCausalLM(LlamaPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = LlamaModel(config)
+        self.pretraining_tp = config.pretraining_tp
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    @replace_return_docstrings(
+        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
+    )
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values=None,  # [MODIFIED] past_key_value is KVCache class
+            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,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, LlamaForCausalLM
+
+        >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.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,
+        )
+
+        hidden_states = outputs[0]
+        if self.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(
+                self.vocab_size // self.pretraining_tp, dim=0
+            )
+            logits = [
+                F.linear(hidden_states, lm_head_slices[i])
+                for i in range(self.pretraining_tp)
+            ]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids,
+            past_key_values=None,
+            attention_mask=None,
+            inputs_embeds=None,
+            **kwargs,
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    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
+
+
+@add_start_docstrings(
+    """
+    The LLaMa Model transformer with a sequence classification head on top (linear layer).
+
+    [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    LLAMA_START_DOCSTRING,
+)
+class LlamaForSequenceClassification(LlamaPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = LlamaModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
+    def forward(
+            self,
+            input_ids: torch.LongTensor = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            past_key_values: Optional[List[torch.FloatTensor]] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        transformer_outputs = self.model(
+            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,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError(
+                "Cannot handle batch sizes > 1 if no padding token is defined."
+            )
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (
+                        torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1
+                ).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[
+            torch.arange(batch_size, device=logits.device), sequence_lengths
+        ]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                        labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
+                )
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

model/utils.py

@@ -0,0 +1,412 @@
+import random
+
+import torch
+
+# TODO
+# from transformers import LlamaTokenizer
+# tokenizer=LlamaTokenizer.from_pretrained("/home/lyh/weights/hf/vicuna_v13/7B/")
+
+TOPK = 10  # topk for sparse tree
+
+
+
+from transformers.generation.logits_process import (
+    LogitsProcessorList,
+    RepetitionPenaltyLogitsProcessor,
+    TemperatureLogitsWarper,
+    TopKLogitsWarper,
+    TopPLogitsWarper,
+)
+def prepare_logits_processor(
+        temperature=0.0, repetition_penalty=0.0, top_p=0.0, top_k=0
+) -> LogitsProcessorList:
+    processor_list = LogitsProcessorList()
+    if temperature >= 1e-5 and temperature != 1.0:
+        processor_list.append(TemperatureLogitsWarper(temperature))
+    if repetition_penalty > 1.0:
+        processor_list.append(RepetitionPenaltyLogitsProcessor(repetition_penalty))
+    if 1e-8 <= top_p < 1.0:
+        processor_list.append(TopPLogitsWarper(top_p))
+    if top_k > 0:
+        processor_list.append(TopKLogitsWarper(top_k))
+    return processor_list
+
+
+
+# test_processor = prepare_logits_processor(
+#         0.0, 0.0, -1, 1
+#     )
+
+
+def pad_path(path, length, pad_value=-2):
+    """
+    Pad the given path list with a specific value up to a specified length.
+
+    Parameters:
+    - path (list): The original list that needs padding.
+    - length (int): The desired length of the padded list.
+    - pad_value (optional, default=-2): The value to use for padding.
+
+    Returns:
+    - list: A new list based on the original path but padded to the desired length.
+
+    Example:
+    >>> pad_path([1,2,3], 5)
+    [1, 2, 3, -2, -2]
+
+    Note:
+    If the given path is already longer than the specified length,
+    then no padding occurs, and the original path is returned.
+    """
+
+    # Calculate the number of padding values needed by subtracting the length
+    # of the path from the desired length.
+    # Append the padding values to the original path and return the new list.
+    return path + [pad_value] * (length - len(path))
+
+
+def generate_tree_buffers(tree_choices, device="cuda"):
+
+    sorted_tree_choices = sorted(tree_choices, key=lambda x: (len(x), x))
+    tree_len = len(sorted_tree_choices) + 1
+
+    # Initialize depth_counts to keep track of how many choices have a particular depth
+    depth_counts = []
+    prev_depth = 0
+    for path in sorted_tree_choices:
+        depth = len(path)
+        if depth != prev_depth:
+            depth_counts.append(0)
+        depth_counts[depth - 1] += 1
+        prev_depth = depth
+
+
+    tree_attn_mask = torch.eye(tree_len, tree_len)
+    tree_attn_mask[:, 0] = 1
+    start = 0
+    for i in range(len(depth_counts)):
+        for j in range(depth_counts[i]):
+            cur_tree_choice = sorted_tree_choices[start + j]
+            # retrieve ancestor position
+            if len(cur_tree_choice) == 1:
+                continue
+            ancestor_idx = []
+            for c in range(len(cur_tree_choice) - 1):
+                ancestor_idx.append(sorted_tree_choices.index(cur_tree_choice[:c + 1]) + 1)
+            tree_attn_mask[j + start + 1, ancestor_idx] = 1
+        start += depth_counts[i]
+
+
+    tree_indices = torch.zeros(tree_len, dtype=torch.long)
+    tree_indices[0] = 0
+    start = 0
+    bias = 0
+    for i in range(len(depth_counts)):
+        for j in range(depth_counts[i]):
+            cur_tree_choice = sorted_tree_choices[start + j]
+            cur_parent = cur_tree_choice[:-1]
+            if j!=0:
+                if cur_parent!=parent:
+                    bias+=1
+                    parent=cur_parent
+            else:
+                parent=cur_parent
+            tree_indices[start + j + 1] = cur_tree_choice[-1] + TOPK * (i+bias) + 1
+        start += depth_counts[i]
+
+
+    tree_position_ids = torch.zeros(tree_len, dtype=torch.long)
+    start = 0
+    for i in range(len(depth_counts)):
+        tree_position_ids[start + 1: start + depth_counts[i] + 1] = i + 1
+        start += depth_counts[i]
+
+
+    retrieve_indices_nest = []
+    retrieve_paths = []
+    for i in range(len(sorted_tree_choices)):
+        cur_tree_choice = sorted_tree_choices[-i - 1]
+        retrieve_indice = []
+        if cur_tree_choice in retrieve_paths:
+            continue
+        else:
+            for c in range(len(cur_tree_choice)):
+                retrieve_indice.append(sorted_tree_choices.index(cur_tree_choice[:c + 1]))
+                retrieve_paths.append(cur_tree_choice[:c + 1])
+        retrieve_indices_nest.append(retrieve_indice)
+    max_length = max([len(x) for x in retrieve_indices_nest])
+    retrieve_indices = [pad_path(path, max_length) for path in retrieve_indices_nest]
+    retrieve_indices = torch.tensor(retrieve_indices, dtype=torch.long)
+    retrieve_indices = retrieve_indices + 1
+    retrieve_indices = torch.cat([torch.zeros((retrieve_indices.shape[0], 1), dtype=torch.long), retrieve_indices],
+                                 dim=1)
+
+    # Aggregate the generated buffers into a dictionary
+    tree_buffers = {
+        "tree_attn_mask": tree_attn_mask.unsqueeze(0).unsqueeze(0),
+        "tree_indices": tree_indices,
+        "tree_position_ids": tree_position_ids,
+        "retrieve_indices": retrieve_indices,
+    }
+
+    # Move the tensors in the dictionary to the specified device
+    tree_buffers = {
+        k: v.clone().to(device)
+        if isinstance(v, torch.Tensor)
+        else torch.tensor(v, device=device)
+        for k, v in tree_buffers.items()
+    }
+    return tree_buffers
+
+
+def initialize_tree(input_ids, model, tree_attn_mask, past_key_values,logits_processor):
+
+    tree_logits, outputs, logits,hidden_state,sample_token = model(
+        input_ids, past_key_values=past_key_values, output_orig=True,logits_processor=logits_processor
+    )
+    model.base_model.model.tree_mask = tree_attn_mask
+    return tree_logits, logits,hidden_state,sample_token
+
+
+def reset_tree_mode(
+        model,
+):
+
+    model.base_model.model.tree_mask = None
+    model.base_model.model.tree_mode = None
+
+
+def reset_past_key_values(passed_key_values):
+    """
+    Resets the current lengths in the passed key-values to zero.
+
+    This function is designed to be used during the evaluation of a baseline model.
+    It iterates through each layer's key-values and sets their current lengths to zero,
+    effectively resetting their state.
+
+    Args:
+    - passed_key_values (list of torch.Tensor): Contains past hidden states and past attention values for each layer.
+
+    Returns:
+    - passed_key_values (list of torch.Tensor): Updated past hidden states and past attention values with reset lengths.
+    """
+    for i in range(len(passed_key_values)):
+        for j in range(2):
+            passed_key_values[i][j].current_length.fill_(0)
+    return passed_key_values
+
+
+def generate_candidates(tree_logits, tree_indices, retrieve_indices,sample_token,logits_processor):
+
+
+    candidates_logit = sample_token[0]
+
+
+    candidates_tree_logits = tree_logits[0]
+
+
+
+    candidates = torch.cat([candidates_logit, candidates_tree_logits.view(-1)], dim=-1)
+
+
+
+    tree_candidates = candidates[tree_indices]
+
+
+
+    tree_candidates_ext = torch.cat(
+        [tree_candidates, torch.zeros((1), dtype=torch.long, device=tree_candidates.device)], dim=0)
+
+
+
+    cart_candidates = tree_candidates_ext[retrieve_indices]
+
+    if logits_processor is not None:
+        candidates_tree_prob = tree_logits[1]
+        candidates_prob = torch.cat(
+            [torch.ones(1, device=candidates_tree_prob.device, dtype=torch.float32), candidates_tree_prob.view(-1)],
+            dim=-1)
+
+        tree_candidates_prob = candidates_prob[tree_indices]
+        tree_candidates_prob_ext = torch.cat(
+            [tree_candidates_prob, torch.ones((1), dtype=torch.float32, device=tree_candidates_prob.device)], dim=0)
+        cart_candidates_prob = tree_candidates_prob_ext[retrieve_indices]
+    else:
+        cart_candidates_prob=None
+    # Unsqueeze the tree candidates for dimension consistency.
+    tree_candidates = tree_candidates.unsqueeze(0)
+    return cart_candidates,cart_candidates_prob, tree_candidates
+
+
+def tree_decoding(
+        model,
+        tree_candidates,
+        past_key_values,
+        tree_position_ids,
+        input_ids,
+        retrieve_indices,
+):
+
+    position_ids = tree_position_ids + input_ids.shape[1]
+
+
+    outputs,tree_logits,hidden_state = model(
+        tree_candidates,
+        output_orig=True,
+        past_key_values=past_key_values,
+        position_ids=position_ids,
+        init=False,
+    )
+
+
+    logits = tree_logits[0, retrieve_indices]
+    return logits, hidden_state,outputs
+
+
+def evaluate_posterior(
+        logits, candidates, logits_processor,cart_candidates_prob
+):
+    """
+    Evaluate the posterior probabilities of the candidates based on the provided logits and choose the best candidate.
+
+    Depending on the temperature value, the function either uses greedy decoding or evaluates posterior
+    probabilities to select the best candidate.
+
+    Args:
+    - logits (torch.Tensor): Predicted logits of shape (batch_size, sequence_length, vocab_size).
+    - candidates (torch.Tensor): Candidate token sequences.
+    - temperature (float): Softmax temperature for probability scaling. A value of 0 indicates greedy decoding.
+    - posterior_threshold (float): Threshold for posterior probability.
+    - posterior_alpha (float): Scaling factor for the threshold.
+
+    Returns:
+    - best_candidate (torch.Tensor): Index of the chosen best candidate.
+    - accept_length (int): Length of the accepted candidate sequence.
+    """
+    # Greedy decoding based on temperature value
+    if logits_processor is None:
+        # Find the tokens that match the maximum logits for each position in the sequence
+        posterior_mask = (
+                candidates[:, 1:] == torch.argmax(logits[:, :-1], dim=-1)
+        ).int()
+        candidates_accept_length = (torch.cumprod(posterior_mask, dim=1)).sum(dim=1)
+        accept_length = candidates_accept_length.max()
+        # Choose the best candidate
+        if accept_length == 0:
+            # Default to the first candidate if none are accepted
+            best_candidate = torch.tensor(0, dtype=torch.long, device=candidates.device)
+        else:
+            best_candidate = torch.argmax(candidates_accept_length).to(torch.long)
+        return best_candidate, accept_length,logits[best_candidate, accept_length]
+
+    else:
+        accept_length=1
+        accept_cand=candidates[0][:1]
+        best_candidate=0
+        #breakflag=False
+        for i in range(1,candidates.shape[1]):
+            is_eq=(candidates[:,:accept_length]==accept_cand).all(dim=1)
+            if i!=accept_length:
+                #breakflag=True
+                break
+            fi=torch.nonzero(is_eq, as_tuple=True)[0][0]
+            gt_logits=logits[fi,i-1][None]
+            gt_logits=logits_processor(None,gt_logits)[0]
+            gtp=torch.softmax(gt_logits,dim=0)
+            adjustflag=False
+            for j in range(candidates.shape[0]):
+                if is_eq[j]:
+                    r=random.random()
+                    x=candidates[j,i]
+                    if x==0:
+                        continue
+                    px=gtp[x]
+                    qx=cart_candidates_prob[j,i]
+                    acp=px/qx
+                    if r<=acp:
+                        accept_cand=torch.cat((accept_cand,x[None]),dim=0)
+                        accept_length+=1
+                        best_candidate=j
+                        break
+                    else:
+                        gtp[x]=max(px-qx,0)
+                        gtp=gtp/gtp.sum()
+                        adjustflag=True
+        if adjustflag:
+            sample_p=gtp
+        else:
+            gt_logits = logits[best_candidate, accept_length-1]
+            sample_p=torch.softmax(gt_logits,dim=0)
+        return torch.tensor(best_candidate), accept_length-1,sample_p
+
+
+
+
+
+
+
+@torch.no_grad()
+def update_inference_inputs(
+    input_ids,
+    candidates,
+    best_candidate,
+    accept_length,
+    retrieve_indices,
+    logits_processor,
+    logits,
+    tree_logits,
+    new_token,
+    past_key_values_data_list,
+    current_length_data,
+    model,
+    hidden_state,
+    hidden_state_new,
+    sample_p
+):
+
+    prev_input_len = input_ids.shape[1]
+    # Map the best candidate indices to the original indices in the sequence
+    select_indices = (
+        retrieve_indices[best_candidate, : accept_length + 1] + prev_input_len
+    )
+    # Append the tokens from the best candidate to the input sequence
+    input_ids = torch.cat(
+        [input_ids, candidates[None, best_candidate, : accept_length + 1].to(input_ids.device)], dim=-1
+    )
+    # Update the past key values based on the selected tokens
+    # Source tensor that contains relevant past information based on the selected candidate
+    for past_key_values_data in past_key_values_data_list:
+        tgt = past_key_values_data[..., select_indices.to(past_key_values_data.device), :]
+        # Destination tensor where the relevant past information will be stored
+        dst = past_key_values_data[..., prev_input_len : prev_input_len + tgt.shape[-2], :]
+        # Copy relevant past information from the source to the destination
+        dst.copy_(tgt, non_blocking=True)
+
+    # Update the current length tensor (currently only support batch size is 1)
+    current_length_data.fill_(prev_input_len + tgt.shape[-2])
+
+
+    retrieve_hidden_state_new=hidden_state_new[:,retrieve_indices]
+    accept_hidden_state_new=retrieve_hidden_state_new[:,best_candidate, : accept_length + 1]
+    #token=model.base_model.lm_head(accept_hidden_state_new[:,-1]).argmax()
+    #token=token[None,None]
+    prob = sample_p
+    if logits_processor is not None:
+        token = torch.multinomial(prob, 1)
+        token=token[None]
+    else:
+        token=torch.argmax(prob)
+        token=token[None,None]
+    hidden_state=torch.cat((hidden_state,accept_hidden_state_new),dim=1)
+    tree_logits=model.ea_layer.topK_genrate(hidden_state,input_ids=torch.cat((input_ids,token.to(input_ids.device)),dim=1),head=model.base_model.lm_head,logits_processor=logits_processor)
+
+    new_token += accept_length + 1
+
+    return input_ids, tree_logits, new_token,hidden_state,token
+
+if __name__=="__main__":
+    logits=torch.randn(1,5)
+    tp = prepare_logits_processor(0.9, 0, 0.9, 0)
+    l=tp(None,logits)
+    if tp is None:
+        print(tp)

model/utils_c.py

@@ -0,0 +1,203 @@
+import torch
+
+TOPK = 10  # topk for sparse tree
+
+
+def pad_path(path, length, pad_value=-2):
+    """
+    Pad the given path list with a specific value up to a specified length.
+
+    Parameters:
+    - path (list): The original list that needs padding.
+    - length (int): The desired length of the padded list.
+    - pad_value (optional, default=-2): The value to use for padding.
+
+    Returns:
+    - list: A new list based on the original path but padded to the desired length.
+
+    Example:
+    >>> pad_path([1,2,3], 5)
+    [1, 2, 3, -2, -2]
+
+    Note:
+    If the given path is already longer than the specified length,
+    then no padding occurs, and the original path is returned.
+    """
+
+    # Calculate the number of padding values needed by subtracting the length
+    # of the path from the desired length.
+    # Append the padding values to the original path and return the new list.
+    return path + [pad_value] * (length - len(path))
+
+class node:
+    def __init__(self,parent=None,value=None,dict_key=None):
+        self.parent=parent
+        self.value=value
+        if parent:
+            self.depth=parent.depth+1
+            parent.children.append(self)
+        else:
+            self.depth=0
+        self.children=[]
+        self.dict_key=dict_key
+    def is_leaf(self):
+        return len(self.children)==0
+
+    def all_index(self):
+        if not self.parent.parent:
+            return [self.index]
+        else:
+            return self.parent.all_index()+[self.index]
+
+
+
+class Tree:
+    def __init__(self,tree_list):
+        sorted_tree_list = sorted(tree_list, key=lambda x: (len(x), x))
+        self.root=node()
+        self.node_dic={}
+        for tree_node in sorted_tree_list:
+            cur_value=tree_node[-1]
+            if len(tree_node)==1:
+                cur_node=node(parent=self.root,value=cur_value,dict_key=tuple(tree_node))
+            else:
+                cur_parent=self.node_dic[tuple(tree_node[:-1])]
+                cur_node = node(parent=cur_parent, value=cur_value,dict_key=tuple(tree_node))
+            self.node_dic[tuple(tree_node)] = cur_node
+        self.indexnode()
+
+    def max_depth(self):
+        return max([item.depth for item in self.node_dic.values()])
+
+    def num_node_wchild(self):
+        num_c=0
+        for item in self.node_dic.values():
+            if not item.is_leaf():
+                num_c+=1
+        return num_c
+
+    def get_node_wchild(self):
+        ns=[]
+        for item in self.node_dic.values():
+            if not item.is_leaf():
+                ns.append(item)
+        return ns
+
+    def indexnode(self):
+        cur_index=0
+        for key in self.node_dic:
+            cur_node=self.node_dic[key]
+            if not cur_node.is_leaf():
+                cur_node.index=cur_index
+                cur_index+=1
+
+
+
+
+def generate_tree_buffers(tree_choices, device="cuda"):
+    tree=Tree(tree_choices)
+    sorted_tree_choices = sorted(tree_choices, key=lambda x: (len(x), x))
+    tree_len = tree.num_node_wchild()
+
+
+    max_depth=tree.max_depth()
+    nodes_wc=tree.get_node_wchild()
+
+    depth_counts=[0 for _ in range(max_depth-1)]
+    for x in nodes_wc:
+        depth_counts[x.depth-1]+=1
+    depth_counts_sum = [sum(depth_counts[:i + 1]) for i in range(len(depth_counts))]
+
+
+    tree_attn_mask = torch.eye(tree_len, tree_len)
+
+    for id,x in enumerate(nodes_wc):
+        tree_attn_mask[id,x.all_index()]=1
+
+
+
+
+    tree_attn_mask_list0=[tree_attn_mask[:ml,:ml] for ml in depth_counts_sum]
+    tree_attn_mask_list=[]
+    for id,x in enumerate(tree_attn_mask_list0):
+        x=x[-depth_counts[id]:]
+        tree_attn_mask_list.append(x)
+
+
+
+    tree_indices_list = [torch.zeros(ml, dtype=torch.long) for ml in depth_counts]
+    repeat_nums=[[] for _ in depth_counts]
+    start = 0
+    bias = 0
+    for i in range(len(depth_counts)):
+        bias = 0
+        repeat_j=0
+        for j in range(depth_counts[i]):
+            cur_node = nodes_wc[start + j]
+            cur_parent = cur_node.parent
+
+            if j != 0:
+                if cur_parent != parent:
+                    bias += 1
+                    parent = cur_parent
+                    repeat_nums[i].append(j-repeat_j)
+                    repeat_j=j
+            else:
+                parent = cur_parent
+            tree_indices_list[i][j] = cur_node.value + TOPK * (bias)
+        repeat_nums[i].append(j - repeat_j+1)
+        start += depth_counts[i]
+
+    position_ids = [torch.zeros(ml, dtype=torch.long) for ml in depth_counts]
+
+    # start = 0
+    # for i in range(len(depth_counts)):
+    #     position_ids[start: start + depth_counts[i]] = i
+    #     start += depth_counts[i]
+
+    tree_buffers = {
+        "attn_mask": [i.unsqueeze(0).unsqueeze(0) for i in tree_attn_mask_list],
+        "tree_indices": tree_indices_list,
+        "position_ids":position_ids,
+        "repeat_nums":repeat_nums
+    }
+
+    # Move the tensors in the dictionary to the specified device
+    tree_buffers = {
+        k: [i.clone().to(device) for i in v]
+        if isinstance(v[0], torch.Tensor)
+        else (
+            torch.tensor(v, device=device)
+            if isinstance(v, torch.Tensor)
+            else v
+        )
+        for k, v in tree_buffers.items()
+    }
+    return tree_buffers
+
+
+def reset_past_key_values(passed_key_values):
+    """
+    Resets the current lengths in the passed key-values to zero.
+
+    This function is designed to be used during the evaluation of a baseline model.
+    It iterates through each layer's key-values and sets their current lengths to zero,
+    effectively resetting their state.
+
+    Args:
+    - passed_key_values (list of torch.Tensor): Contains past hidden states and past attention values for each layer.
+
+    Returns:
+    - passed_key_values (list of torch.Tensor): Updated past hidden states and past attention values with reset lengths.
+    """
+    for i in range(len(passed_key_values)):
+        for j in range(2):
+            passed_key_values[i][j].current_length.fill_(0)
+    return passed_key_values
+
+
+
+if __name__=="__main__":
+    from choices import mc_sim_7b_63
+    a=generate_tree_buffers(mc_sim_7b_63)
+    print(a)