# 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 .choices import * except: from configs import EConfig from choices import * from utils import prepare_logits_processor import time class Timer: def __init__(self, name): self.name = name def __enter__(self): torch.cuda.synchronize() self.start = time.perf_counter() def __exit__(self, exc_type, exc_value, traceback): torch.cuda.synchronize() elapsed = time.perf_counter() - self.start print(f'{self.name} took {elapsed} seconds') # 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: if hasattr(self.config, "rope_theta"): self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.config.rope_theta) else: 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, path=None, bias=True, total_tokens=63, depth=5, top_k=8, threshold=1.0): 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 import json try: with open(os.path.join(path, "model.safetensors.index.json"), "r") as f: index_json = json.loads(f.read()) emb_path = index_json["weight_map"]["model.embed_tokens.weight"] with safe_open(os.path.join(path, emb_path), 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() except: with open(os.path.join(path, "pytorch_model.bin.index.json"), "r") as f: index_json = json.loads(f.read()) emb_path = index_json["weight_map"]["model.embed_tokens.weight"] weights = torch.load(os.path.join(path, emb_path)) tensor = weights["model.embed_tokens.weight"].float() self.embed_tokens.weight.data = tensor self.top_k = top_k self.total_tokens = total_tokens - 1 self.depth = depth self.threshold = math.log(threshold) # print("total_tokens",total_tokens) # print("depth",depth) # print("top_k",top_k) # print("threshold",threshold) 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, bias=bias) self.act = ACT2FN[config.hidden_act] self.logsoftmax = nn.LogSoftmax(dim=-1) for param in self.embed_tokens.parameters(): param.requires_grad = False def init_tree(self): self.tree_mask_init = torch.eye(self.top_k, device=self.embed_tokens.weight.device)[None, None] self.position_ids = torch.zeros(self.top_k, device=self.embed_tokens.weight.device, dtype=torch.long) self.tree_mask_init = self.tree_mask_init.to(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_shape0, tree_shape1 = tree_mask.shape combined_attention_mask[:, :, -tree_shape0:, -tree_shape1:][ 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 def reset_kv(self): self.stable_kv = None @torch.no_grad() def topK_genrate(self, hidden_states, input_ids, head, logits_processor): input_ids = input_ids.to(hidden_states.device) total_tokens = self.total_tokens depth = self.depth top_k = self.top_k sample_token = input_ids[:, -1] scores_list = [] parents_list = [] ss_token = [] input_ids = input_ids[:, 1:] input_ids = input_ids.to(hidden_states.device) len_posi = input_ids.shape[1] self.reset() # with Timer("draft many"): 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, 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) last_p = self.logsoftmax(last_headout) top = torch.topk(last_p, top_k, dim=-1) topk_index, topk_p = top.indices, top.values scores = topk_p[0] scores_list.append(scores[None]) parents_list.append(torch.zeros(1, dtype=torch.long, device=scores.device)) ss_token.append(topk_index) input_ids = topk_index input_hidden = last_hidden[None].repeat(1, top_k, 1) tree_mask = self.tree_mask_init topk_cs_index = torch.arange(top_k, device=self.embed_tokens.weight.device) # 4 for i in range(depth): self.tree_mask = tree_mask position_ids = len_posi + self.position_ids # with Timer("draft one"): out_hidden, past_key_values = self(input_hidden, input_ids=input_ids, past_key_values=past_key_values, position_ids=position_ids, use_cache=True) len_posi += 1 # with Timer("sort1"): bias1 = top_k if i > 0 else 0 bias2 = max(0, i - 1) bias = 1 + top_k ** 2 * bias2 + bias1 parents = (topk_cs_index + bias) parents_list.append(parents) last_headout = head(out_hidden[0]) last_p = self.logsoftmax(last_headout) top = torch.topk(last_p, top_k, dim=-1) topk_index, topk_p = top.indices, top.values cu_scores = topk_p + scores[:, None] topk_cs = torch.topk(cu_scores.view(-1), top_k, dim=-1) topk_cs_index, topk_cs_p = topk_cs.indices, topk_cs.values scores = topk_cs_p out_ids = topk_cs_index // top_k input_hidden = out_hidden[:, out_ids] # with Timer("2index"): # in_ids = topk_cs_index % top_k # input_ids = topk_index[out_ids, in_ids][None] # with Timer("1index"): input_ids = topk_index.view(-1)[topk_cs_index][None] # print(input_ids.equal(input_ids0)) ss_token.append(topk_index) scores_list.append(cu_scores) tree_mask = torch.cat((tree_mask[:, :, out_ids], self.tree_mask_init), dim=3) # if self.threshold < 0 and cu_scores.max() < self.threshold: # break # del parents_list,scores_list,ss_token # return draft_tokens, mask_index,tree_mask,tree_position_ids # with Timer("post"): scores_list = torch.cat(scores_list, dim=0).view(-1) ss_token_list = torch.cat(ss_token, dim=0).view(-1) top_scores = torch.topk(scores_list, total_tokens, dim=-1) top_scores_index = top_scores.indices top_scores_index = torch.sort(top_scores_index).values draft_tokens = ss_token_list[top_scores_index] draft_tokens = torch.cat((sample_token, draft_tokens), dim=0) draft_parents = torch.cat(parents_list, dim=0)[top_scores_index // top_k].long() mask_index = torch.searchsorted(top_scores_index, draft_parents - 1, right=False) # mask_index[(top_scores_index[mask_index]!=draft_parents - 1)]=-1 mask_index[draft_parents == 0] = -1 mask_index = mask_index + 1 mask_index_list = mask_index.tolist() # with Timer("mask"): tree_mask = torch.eye(total_tokens + 1).bool() tree_mask[:, 0] = True for i in range(total_tokens): tree_mask[i + 1].add_(tree_mask[mask_index_list[i]]) # with Timer("mask1"): # tree_mask0 = [[False for _ in range(total_tokens + 1)] for _ in range(total_tokens + 1)] # tree_mask0[0][0] = True # for i in range(total_tokens): # #tree_mask0[i + 1][0]=True # tree_mask0[i + 1][i + 1] = True # p=mask_index_list[i] # tree_mask0[i + 1][p] = True # while p: # p=mask_index_list[p-1] # tree_mask0[i + 1][p] = True # tree_mask0 = torch.tensor(tree_mask0, dtype=torch.bool) # # print(tree_mask0.equal(tree_mask)) tree_position_ids = torch.sum(tree_mask, dim=1) - 1 tree_mask = tree_mask.float()[None, None] draft_tokens = draft_tokens[None] del parents_list, scores_list, ss_token, ss_token_list, draft_parents # with Timer("retrieve"): max_depth = torch.max(tree_position_ids) + 1 noleaf_index = torch.unique(mask_index).tolist() noleaf_num = len(noleaf_index) - 1 leaf_num = total_tokens - noleaf_num retrieve_indices = torch.zeros(leaf_num, max_depth.item(), dtype=torch.long) - 1 retrieve_indices = retrieve_indices.tolist() rid = 0 position_ids_list = tree_position_ids.tolist() for i in range(total_tokens + 1): if i not in noleaf_index: cid = i depth = position_ids_list[i] for j in reversed(range(depth + 1)): retrieve_indices[rid][j] = cid cid = mask_index_list[cid - 1] rid += 1 if logits_processor is not None: maxitem = total_tokens + 5 def custom_sort(lst): # sort_keys=[len(list)] sort_keys = [] for i in range(len(lst)): sort_keys.append(lst[i] if lst[i] >= 0 else maxitem) return sort_keys retrieve_indices = sorted(retrieve_indices, key=custom_sort) retrieve_indices = torch.tensor(retrieve_indices, dtype=torch.long) del mask_index, mask_index_list, noleaf_index, noleaf_num, leaf_num, max_depth, rid tree_position_ids = tree_position_ids.to(hidden_states.device) return draft_tokens, retrieve_indices, tree_mask, tree_position_ids @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__": config = EConfig.from_pretrained('config.json') model = Model(config, load_emb=True, path="/home/lyh/weights/hf/vicuna_v13/7B/") print(model)