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import math |
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import os |
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import random |
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import warnings |
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from dataclasses import dataclass |
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from typing import Optional, Tuple, Union |
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|
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import torch |
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import torch.nn as nn |
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import torch.nn.functional as F |
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import torch.utils.checkpoint |
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from einops import repeat |
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from torch import nn |
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from torch.cuda.amp import autocast |
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss |
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from transformers.activations import ACT2FN |
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from transformers.modeling_outputs import ( |
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BaseModelOutputWithPastAndCrossAttentions, |
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CausalLMOutputWithCrossAttentions, QuestionAnsweringModelOutput, |
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SequenceClassifierOutputWithPast, TokenClassifierOutput) |
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from transformers.modeling_utils import PreTrainedModel, SequenceSummary |
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from transformers.utils import (ModelOutput, logging) |
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from transformers.utils.model_parallel_utils import (assert_device_map, |
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get_device_map) |
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from .configuration_nano import NanoConfig |
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from transformers.models.llama.modeling_llama import LlamaRMSNorm, LlamaDynamicNTKScalingRotaryEmbedding, LlamaRotaryEmbedding, LlamaLinearScalingRotaryEmbedding |
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|
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def rotate_half(x): |
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"""Rotates half the hidden dims of the input.""" |
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x1 = x[..., : x.shape[-1] // 2] |
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x2 = x[..., x.shape[-1] // 2 :] |
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return torch.cat((-x2, x1), dim=-1) |
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|
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1): |
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cos = cos[position_ids].unsqueeze(unsqueeze_dim) |
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sin = sin[position_ids].unsqueeze(unsqueeze_dim) |
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q_embed = (q * cos) + (rotate_half(q) * sin) |
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k_embed = (k * cos) + (rotate_half(k) * sin) |
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return q_embed, k_embed |
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class NanoAttention(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.head_dim = config.hidden_size // config.num_attention_heads |
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assert ( |
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self.head_dim * config.num_attention_heads == config.hidden_size |
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), "d_model must be divisible by n_head" |
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self.use_bias = config.use_bias |
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|
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if not config.combined_qkv or config.kv_hidden_size is not None: |
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self.query = nn.Linear( |
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config.hidden_size, config.hidden_size, bias=self.use_bias |
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) |
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self.key = nn.Linear( |
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config.hidden_size |
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if not config.kv_hidden_size |
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else config.kv_hidden_size, |
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config.hidden_size, |
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bias=self.use_bias, |
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) |
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self.value = nn.Linear( |
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config.hidden_size |
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if not config.kv_hidden_size |
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else config.kv_hidden_size, |
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config.hidden_size, |
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bias=self.use_bias, |
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) |
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else: |
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self.qkv = nn.Linear( |
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config.hidden_size, config.hidden_size * 3, bias=self.use_bias |
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) |
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self.out = nn.Linear(config.hidden_size, config.hidden_size, bias=self.use_bias) |
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self._init_rope() |
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|
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def _init_rope(self): |
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if self.config.rope_scaling is None: |
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self.rotary_emb = LlamaRotaryEmbedding( |
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self.head_dim, |
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max_position_embeddings=self.config.max_position_embeddings, |
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base=self.config.rope_theta, |
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) |
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else: |
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scaling_type = self.config.rope_scaling["type"] |
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scaling_factor = self.config.rope_scaling["factor"] |
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if scaling_type == "linear": |
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self.rotary_emb = LlamaLinearScalingRotaryEmbedding( |
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self.head_dim, |
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max_position_embeddings=self.config.max_position_embeddings, |
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scaling_factor=scaling_factor, |
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base=self.config.rope_theta, |
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) |
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elif scaling_type == "dynamic": |
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self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding( |
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self.head_dim, |
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max_position_embeddings=self.config.max_position_embeddings, |
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scaling_factor=scaling_factor, |
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base=self.config.rope_theta, |
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) |
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else: |
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raise ValueError(f"Unknown RoPE scaling type {scaling_type}") |
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|
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def forward(self, x0, x1=None, causal=False, mask=None, position_ids=None, use_cache=True, layer_past=None): |
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batch_size = x0.size(0) |
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|
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def split_heads(x): |
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return x.view( |
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batch_size, -1, self.config.num_attention_heads, self.head_dim |
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).transpose(1, 2) |
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|
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if not self.config.combined_qkv: |
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q = split_heads(self.query(x0)) |
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k = split_heads(self.key(x1) if x1 is not None else self.key(x0)) |
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v = split_heads( |
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self.value(x1 if x1 is not None else x0) |
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) |
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else: |
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q, k, v = self.qkv(x0).chunk(3,-1) |
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q = split_heads(q) |
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k = split_heads(k) |
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v = split_heads(v) |
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|
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if layer_past is not None: |
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past_key, past_value = layer_past |
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k = torch.cat((past_key, k), dim=-2) |
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v = torch.cat((past_value, v), dim=-2) |
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cos, sin = self.rotary_emb(v, seq_len=v.shape[-2]) |
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if self.config.full_adaptation_type != "no": |
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position_ids = position_ids.repeat_interleave(x0.shape[1]//position_ids.shape[-1],dim=1) |
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q, k = apply_rotary_pos_emb(q, k, cos, sin, position_ids) |
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if use_cache is True: |
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present = (k,v) |
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else: |
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present = None |
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attn_output = F.scaled_dot_product_attention( |
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q, k, v, attn_mask=None, dropout_p=0.0, is_causal=causal |
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) |
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attn_output = ( |
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attn_output.transpose(1, 2) |
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.contiguous() |
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.view(batch_size, -1, self.config.hidden_size) |
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) |
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return self.out(attn_output), present |
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class NanoGLU(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.gate_proj = nn.Linear( |
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config.hidden_size, config.intermediate_size, bias=False |
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) |
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self.up_proj = nn.Linear( |
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config.hidden_size, config.intermediate_size, bias=False |
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) |
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self.down_proj = nn.Linear( |
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config.intermediate_size, config.hidden_size, bias=False |
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) |
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self.act_fn = ACT2FN[config.activation_function] |
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|
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def forward(self, x): |
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return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x)) |
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class NanoBlock(nn.Module): |
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def __init__(self, config): |
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super().__init__() |
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self.config = config |
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self.attn = NanoAttention(config) |
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self.ffn = NanoGLU(config) |
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ln_class = LlamaRMSNorm if config.layernorm=="llamarmsnorm" else nn.LayerNorm |
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self.ln1 = ln_class(config.hidden_size, eps=config.layer_norm_epsilon) |
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self.ln2 = ln_class(config.hidden_size, eps=config.layer_norm_epsilon) |
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if config.residual_alpha: |
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self.ffn_a = nn.Parameter(torch.tensor(0.)) |
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self.attn_a = nn.Parameter(torch.tensor(0.)) |
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else: |
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self.ffn_a = 1 |
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self.attn_a = 1 |
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def forward(self, x, mask=None, position_ids=None, use_cache=True, layer_past=None): |
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if self.config.ffn == "llamalike": |
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residual = x |
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x = self.ln1(x) |
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attn_out, attn_outs = self.attn(x, causal=True, mask=mask, position_ids=position_ids, use_cache=use_cache, layer_past=layer_past) |
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x = residual + attn_out * self.attn_a |
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residual = x |
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x = self.ln2(x) |
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x = self.ffn(x) |
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x = residual + x * self.ffn_a |
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else: |
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attn_in = self.ln1(x) |
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ffn_in = self.ln2(x) |
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attn_out, attn_outs = self.attn(attn_in, causal=True, mask=mask, position_ids=position_ids, use_cache=use_cache, layer_past=layer_past) |
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ffn_out = self.ffn(ffn_in) |
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x = x + attn_out * self.attn_a + ffn_out * self.ffn_a |
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if not use_cache: attn_outs = None |
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return (x, attn_outs) |
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class NanoPreTrainedModel(PreTrainedModel): |
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config_class = NanoConfig |
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base_model_prefix = "transformer" |
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is_parallelizable = False |
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supports_gradient_checkpointing = True |
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_no_split_modules = ["NanoBlock"] |
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_skip_keys_device_placement = "past_key_values" |
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|
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def __init__(self, *inputs, **kwargs): |
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super().__init__(*inputs, **kwargs) |
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|
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def _init_weights(self, module): |
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"""Initialize the weights.""" |
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if isinstance(module, (nn.Linear)): |
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.bias is not None: |
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module.bias.data.zero_() |
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elif isinstance(module, nn.Embedding): |
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module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) |
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if module.padding_idx is not None: |
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module.weight.data[module.padding_idx].zero_() |
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elif isinstance(module, nn.LayerNorm): |
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module.bias.data.zero_() |
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module.weight.data.fill_(1.0) |
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|
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def _set_gradient_checkpointing(self, module, value=False): |
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if isinstance(module, NanoModel): |
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module.gradient_checkpointing = value |
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class Split(nn.Module): |
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def __init__(self, splits): |
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super().__init__() |
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self.splits=splits |
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def forward(self, x): |
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bs, tokens, _ = x.shape |
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x = x.view(bs, tokens, self.splits, -1) |
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x = x.permute(0, 1, 2, 3).reshape(bs, tokens * self.splits, -1) |
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return x |
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class Recombine(nn.Module): |
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def __init__(self, splits): |
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super().__init__() |
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self.splits = splits |
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def forward(self, x): |
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bs, _, _ = x.shape |
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tokens = x.shape[1] // self.splits |
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x = x.view(bs, tokens, -1) |
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return x |
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class Residual(nn.Module): |
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def __init__(self, module, a=None): |
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super().__init__() |
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self.module = module |
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self.a = nn.Parameter(torch.tensor(a, dtype=torch.bfloat16)) if a is not None else None |
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def forward(self, x): |
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return self.module(x) * (self.a if self.a is not None else 1) + x |
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|
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class LoRA(nn.Module): |
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def __init__(self, d, r, a=1): |
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super().__init__() |
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self.fn_i = nn.Linear(d, r) |
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self.fn_o = nn.Linear(r, d) |
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self.a = nn.Parameter(torch.tensor(a, dtype=self.fn_i.weight.dtype)) |
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def forward(self, x): |
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return self.fn_o(self.fn_i(x)) * self.a + x |
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def get_delta_w(self): |
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return torch.mm(self.fn_o.weight, self.fn_i.weight) * self.a |
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|
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class NanoModel(NanoPreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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ln_class = LlamaRMSNorm if config.layernorm=="llamarmsnorm" else nn.LayerNorm |
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|
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if config.full_adaptation_type == "no": |
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if config.expanded_wte_size is not None: |
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self.wte = nn.Sequential( |
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nn.Embedding(config.vocab_size, config.expanded_wte_size), |
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nn.Linear(config.expanded_wte_size, config.hidden_size), |
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) |
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else: |
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self.wte = nn.Embedding(config.vocab_size, config.hidden_size) |
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else: |
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assert config.expanded_wte_size is not None, "experimental full adaptation of token embeddings requires expanded_wte_size to be set" |
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self.d_0 = config.expanded_wte_size if (config.full_adaptation_has_pre_proj == False) else config.pre_proj_dim |
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|
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self.wte = nn.Sequential( |
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nn.Embedding(config.vocab_size, config.expanded_wte_size), |
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( |
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nn.Linear(config.expanded_wte_size, config.pre_proj_dim) if config.full_adaptation_has_pre_proj else nn.Identity() |
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), |
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( |
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LoRA(self.d_0, config.experimental_full_adaption_rank) |
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if config.full_adaptation_type == "lora" else |
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nn.Linear(self.d_0, self.d_0) |
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if config.full_adaptation_type == "linear" else |
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Residual( |
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nn.Linear(self.d_0, self.d_0) |
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) |
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if config.full_adaptation_type == "linear-r" else |
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Residual( |
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nn.Linear(self.d_0, self.d_0), 1 |
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) |
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if config.full_adaptation_type == "linear-ra" else |
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nn.Identity() |
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), |
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Split(self.d_0//config.hidden_size) |
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) |
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self.h = nn.ModuleList( |
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[NanoBlock(config) for i in range(config.num_hidden_layers)] |
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) |
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self.ln_f = ln_class(config.hidden_size, eps=config.layer_norm_epsilon) |
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self.model_parallel = False |
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self.device_map = None |
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self.gradient_checkpointing = False |
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self.post_init() |
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|
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def get_input_embeddings(self): |
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return self.wte[0] if self.config.expanded_wte_size is not None else self.wte |
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|
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def set_input_embeddings(self, new_embeddings): |
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if self.config.expanded_wte_size is not None: |
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self.wte[0] = new_embeddings |
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else: |
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self.wte = new_embeddings |
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|
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
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attention_mask: Optional[torch.FloatTensor] = None, |
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token_type_ids: Optional[torch.LongTensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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head_mask: Optional[torch.FloatTensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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encoder_hidden_states: Optional[torch.Tensor] = None, |
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encoder_attention_mask: Optional[torch.FloatTensor] = None, |
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use_cache: Optional[bool] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple, BaseModelOutputWithPastAndCrossAttentions]: |
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output_attentions = ( |
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output_attentions |
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if output_attentions is not None |
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else self.config.output_attentions |
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) |
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output_hidden_states = ( |
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output_hidden_states |
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if output_hidden_states is not None |
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else self.config.output_hidden_states |
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) |
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use_cache = use_cache if use_cache is not None else self.config.use_cache |
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return_dict = ( |
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return_dict if return_dict is not None else self.config.use_return_dict |
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) |
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if input_ids is not None and inputs_embeds is not None: |
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raise ValueError( |
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"You cannot specify both input_ids and inputs_embeds at the same time" |
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) |
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elif input_ids is not None: |
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self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) |
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input_shape = input_ids.size() |
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input_ids = input_ids.view(-1, input_shape[-1]) |
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batch_size = input_ids.shape[0] |
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elif inputs_embeds is not None: |
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input_shape = inputs_embeds.size()[:-1] |
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batch_size = inputs_embeds.shape[0] |
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else: |
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raise ValueError("You have to specify either input_ids or inputs_embeds") |
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|
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device = input_ids.device if input_ids is not None else inputs_embeds.device |
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|
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if token_type_ids is not None: |
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token_type_ids = token_type_ids.view(-1, input_shape[-1]) |
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if position_ids is not None: |
|
position_ids = position_ids.view(-1, input_shape[-1]) |
|
|
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if past_key_values is None: |
|
past_length = 0 |
|
past_key_values = tuple([None] * len(self.h)) |
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else: |
|
past_length = past_key_values[0][0].size(-2) |
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if position_ids is None: |
|
position_ids = torch.arange( |
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past_length, |
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input_shape[-1] + past_length, |
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dtype=torch.long, |
|
device=device, |
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) |
|
position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1]) |
|
|
|
if attention_mask is not None: |
|
if batch_size <= 0: |
|
raise ValueError("batch_size has to be defined and > 0") |
|
attention_mask = attention_mask.view(batch_size, -1) |
|
attention_mask = attention_mask[:, None, None, :] |
|
attention_mask = attention_mask.to(dtype=self.dtype) |
|
attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min |
|
|
|
if self.config.add_cross_attention and encoder_hidden_states is not None: |
|
( |
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encoder_batch_size, |
|
encoder_sequence_length, |
|
_, |
|
) = encoder_hidden_states.size() |
|
encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) |
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if encoder_attention_mask is None: |
|
encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) |
|
encoder_attention_mask = self.invert_attention_mask(encoder_attention_mask) |
|
else: |
|
encoder_attention_mask = None |
|
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) |
|
|
|
if inputs_embeds is None: |
|
inputs_embeds = self.wte(input_ids) |
|
|
|
|
|
hidden_states = inputs_embeds |
|
|
|
if token_type_ids is not None: |
|
token_type_embeds = self.wte(token_type_ids) |
|
hidden_states = hidden_states + token_type_embeds |
|
|
|
|
|
output_shape = (-1,) + (hidden_states.shape[1],) + (hidden_states.size(-1),) |
|
|
|
|
|
if self.gradient_checkpointing and self.training: |
|
if use_cache: |
|
|
|
|
|
|
|
use_cache = False |
|
|
|
presents = () if use_cache else None |
|
all_self_attentions = () if output_attentions else None |
|
all_cross_attentions = ( |
|
() if output_attentions and self.config.add_cross_attention else None |
|
) |
|
all_hidden_states = () if output_hidden_states else None |
|
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)): |
|
if self.model_parallel: |
|
torch.cuda.set_device(hidden_states.device) |
|
if layer_past is not None: |
|
layer_past = tuple( |
|
past_state.to(hidden_states.device) |
|
for past_state in layer_past |
|
) |
|
if attention_mask is not None: |
|
attention_mask = attention_mask.to(hidden_states.device) |
|
if isinstance(head_mask, torch.Tensor): |
|
head_mask = head_mask.to(hidden_states.device) |
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
outputs = block(hidden_states, mask=attention_mask, position_ids=position_ids, use_cache=use_cache, layer_past=layer_past) |
|
hidden_states = outputs[0] |
|
if use_cache == True: |
|
presents = presents + (outputs[1],) |
|
|
|
hidden_states = self.ln_f(hidden_states) |
|
hidden_states = hidden_states.view(output_shape) |
|
if output_hidden_states: |
|
all_hidden_states = all_hidden_states + (hidden_states,) |
|
|
|
if not return_dict: |
|
return tuple( |
|
v |
|
for v in [hidden_states, None, all_hidden_states, None, None] |
|
if v is not None |
|
) |
|
|
|
return BaseModelOutputWithPastAndCrossAttentions( |
|
last_hidden_state=hidden_states, |
|
past_key_values=presents, |
|
hidden_states=all_hidden_states, |
|
attentions=None, |
|
cross_attentions=None, |
|
) |
|
|
|
class NanoModelForCausalLM(NanoPreTrainedModel): |
|
_tied_weights_keys = ["lm_head.weight"] |
|
def __init__(self, config): |
|
super().__init__(config) |
|
self.transformer = NanoModel(config) |
|
if config.full_adaptation_type == "no": |
|
if (config.expanded_lm_head_size is not None): |
|
self.lm_head = nn.Sequential( |
|
nn.Linear( |
|
config.hidden_size, config.expanded_lm_head_size, bias=config.lm_head_projection_bias |
|
), |
|
nn.Linear( |
|
config.expanded_lm_head_size, config.vocab_size, bias=config.lm_head_bias |
|
), |
|
) |
|
else: |
|
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size) |
|
else: |
|
d_0 = config.expanded_lm_head_size if (not config.full_adaptation_has_pre_proj) else config.pre_proj_dim |
|
self.lm_head = nn.Sequential( |
|
Recombine(d_0//config.hidden_size), |
|
nn.Identity() if not config.full_adaptation_has_pre_proj else nn.Linear(d_0, config.expanded_lm_head_size), |
|
( |
|
LoRA(config.expanded_lm_head_size, config.experimental_full_adaption_rank) |
|
if config.full_adaptation_type == "lora" else |
|
nn.Linear(config.expanded_lm_head_size, config.expanded_lm_head_size) |
|
if config.full_adaptation_type == "linear" else |
|
Residual( |
|
nn.Linear(config.expanded_lm_head_size, config.expanded_lm_head_size) |
|
) |
|
if config.full_adaptation_type == "linear-r" else |
|
Residual( |
|
nn.Linear(config.expanded_lm_head_size, config.expanded_lm_head_size), 1 |
|
) |
|
if config.full_adaptation_type == "linear-ra" else |
|
nn.Identity() |
|
), |
|
|
|
nn.Linear(config.expanded_lm_head_size, config.vocab_size) |
|
) |
|
self.model_parallel = False |
|
self.device_map = None |
|
self.post_init() |
|
|
|
def get_output_embeddings(self): |
|
return self.lm_head if (self.config.experimental_full_adaption_rank is None and self.config.expanded_lm_head_size is None) else self.lm_head[-1] |
|
|
|
def set_output_embeddings(self, new_embeddings): |
|
self.lm_head = new_embeddings |
|
|
|
def prepare_inputs_for_generation( |
|
self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs |
|
): |
|
token_type_ids = kwargs.get("token_type_ids", None) |
|
|
|
if past_key_values: |
|
input_ids = input_ids[:, -1].unsqueeze(-1) |
|
if token_type_ids is not None: |
|
token_type_ids = token_type_ids[:, -1].unsqueeze(-1) |
|
|
|
attention_mask = kwargs.get("attention_mask", None) |
|
position_ids = kwargs.get("position_ids", None) |
|
|
|
if attention_mask is not None and position_ids is None: |
|
|
|
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) |
|
else: |
|
position_ids = None |
|
|
|
|
|
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( |
|
{ |
|
"past_key_values": past_key_values, |
|
"use_cache": kwargs.get("use_cache"), |
|
"position_ids": position_ids, |
|
"attention_mask": attention_mask, |
|
"token_type_ids": token_type_ids, |
|
} |
|
) |
|
return model_inputs |
|
|
|
def forward( |
|
self, |
|
input_ids: Optional[torch.LongTensor] = None, |
|
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
|
attention_mask: Optional[torch.FloatTensor] = None, |
|
token_type_ids: Optional[torch.LongTensor] = None, |
|
position_ids: Optional[torch.LongTensor] = None, |
|
head_mask: Optional[torch.FloatTensor] = None, |
|
inputs_embeds: Optional[torch.FloatTensor] = None, |
|
encoder_hidden_states: Optional[torch.Tensor] = None, |
|
encoder_attention_mask: 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, CausalLMOutputWithCrossAttentions]: |
|
r""" |
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
|
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set |
|
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` |
|
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` |
|
""" |
|
return_dict = ( |
|
return_dict if return_dict is not None else self.config.use_return_dict |
|
) |
|
|
|
transformer_outputs = self.transformer( |
|
input_ids, |
|
past_key_values=past_key_values, |
|
attention_mask=attention_mask, |
|
token_type_ids=token_type_ids, |
|
position_ids=position_ids, |
|
head_mask=head_mask, |
|
inputs_embeds=inputs_embeds, |
|
encoder_hidden_states=encoder_hidden_states, |
|
encoder_attention_mask=encoder_attention_mask, |
|
use_cache=use_cache, |
|
output_attentions=output_attentions, |
|
output_hidden_states=output_hidden_states, |
|
return_dict=return_dict, |
|
) |
|
hidden_states = transformer_outputs[0] |
|
|
|
if self.model_parallel: |
|
torch.cuda.set_device(self.transformer.first_device) |
|
hidden_states = hidden_states.to(self.lm_head.weight.device) |
|
|
|
lm_logits = self.lm_head(hidden_states) |
|
|
|
loss = None |
|
if labels is not None: |
|
|
|
labels = labels.to(lm_logits.device) |
|
|
|
shift_logits = lm_logits[..., :-1, :].contiguous() |
|
shift_labels = labels[..., 1:].contiguous() |
|
|
|
loss_fct = CrossEntropyLoss() |
|
loss = loss_fct( |
|
shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1) |
|
) |
|
|
|
if not return_dict: |
|
output = (lm_logits,) + transformer_outputs[1:] |
|
return ((loss,) + output) if loss is not None else output |
|
|
|
return CausalLMOutputWithCrossAttentions( |
|
loss=loss, |
|
logits=lm_logits, |
|
past_key_values=transformer_outputs.past_key_values, |
|
hidden_states=transformer_outputs.hidden_states, |
|
attentions=transformer_outputs.attentions, |
|
cross_attentions=transformer_outputs.cross_attentions, |
|
) |
|
|
|
@staticmethod |
|
def _reorder_cache( |
|
past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor |
|
) -> Tuple[Tuple[torch.Tensor]]: |
|
""" |
|
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or |
|
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct |
|
beam_idx at every generation step. |
|
""" |
|
return tuple( |
|
tuple( |
|
past_state.index_select(0, beam_idx.to(past_state.device)) |
|
for past_state in layer_past |
|
) |
|
for layer_past in past_key_values |
|
) |
|
|
|
|
|
class VTMModelForCausalLM(NanoModelForCausalLM): |
|
_tied_weights_keys = ["lm_head.3.weight"] |
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
class VTMPreProjModelForCausalLM(NanoModelForCausalLM): |
|
_tied_weights_keys = ["lm_head.3.weight"] |
|
def __init__(self, config): |
|
super().__init__(config) |
|
|
|
class PlusModelForCausalLM(NanoModelForCausalLM): |
|
_tied_weights_keys = ["lm_head.1.weight"] |
|
def __init__(self, config): |
|
super().__init__(config) |
