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# Copyright (c) 2024, SliceX AI, Inc.
import copy
import inspect
import math
import numpy as np
import os
from dataclasses import dataclass, field
from typing import List, Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from elm.utils import *
from elm.positional_embeddings import *
def get_elm_model_map(model_name):
"""Map the model type to corresponding class."""
elm_model_map = {
"rambutan": RambutanSlice,
}
return elm_model_map.get(model_name, RambutanSlice)
@dataclass
class ModelArgs:
"""ELM Model Args"""
model_name_or_path: str = "ELM"
compile_model: bool = False
elm_model_class: Optional[str] = "rambutan"
hidden_size: Optional[int] = 2048
max_inp_len: Optional[int] = 2048
attn_window_size: Optional[int] = max_inp_len
num_attention_heads: Optional[int] = 32
layernorm_eps: float = 1e-5
attention_dropout: float = 0.1
hidden_dropout: float = 0.1
num_layers: Optional[int] = 16
bits: Optional[int] = 256
vocab_size: Optional[int] = 50304
dropout: Optional[int] = 0.1
use_rotary_embeddings: Optional[bool] = True
tokenizer: Optional[str] = None
class ELM(torch.nn.Module):
"""ELM (SliceX GPT) model."""
def __init__(self,
model_args: ModelArgs):
"""Initialize an ELM model instance."""
super().__init__()
self.model_args = model_args
elm_model_class = model_args.elm_model_class
hidden_size = model_args.hidden_size
max_inp_len = model_args.max_inp_len
num_attention_heads = model_args.num_attention_heads
layernorm_eps = model_args.layernorm_eps
attention_dropout = model_args.attention_dropout
hidden_dropout = model_args.hidden_dropout
num_layers = model_args.num_layers
bits = model_args.bits
vocab_size = model_args.vocab_size
use_rotary_embeddings = model_args.use_rotary_embeddings
layer_class = get_elm_model_map(elm_model_class)
self.slice_transformer = torch.nn.ModuleDict(dict(
temb = torch.nn.Embedding(vocab_size, hidden_size),
pemb = torch.nn.Embedding(max_inp_len, hidden_size) if not use_rotary_embeddings else None,
drop = torch.nn.Dropout(hidden_dropout),
h = torch.nn.ModuleList([ layer_class(model_args=model_args) for _ in range(num_layers) ]),
ln_f = torch.nn.LayerNorm(hidden_size, eps=layernorm_eps),
))
self.lm_head = torch.nn.Linear(hidden_size, vocab_size, bias=False)
print("Number of model parameters: %.2fM" % (self.get_num_params(False)/1e6,))
def forward(self,
x: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
targets: Optional[torch.Tensor] = None):
device = x.device
batch, seqlen = x.size()
tok_emb = self.slice_transformer.temb(x)
if not self.model_args.use_rotary_embeddings:
pos = torch.arange(0, seqlen, dtype=torch.long, device=device)
pos_emb = self.slice_transformer.pemb(pos)
x = self.slice_transformer.drop(tok_emb + pos_emb)
else:
x = self.slice_transformer.drop(tok_emb)
ignore_index_id = -100
loss = torch.zeros(1).to(device)
loss_denom = 0
for tlayer in self.slice_transformer.h:
x = tlayer(x, attention_mask=attention_mask)
x = self.slice_transformer.ln_f(x)
if targets is not None:
logits = self.lm_head(x)
shift_logits = logits[..., :-1, :].contiguous()
shift_targets = targets[..., 1:].contiguous()
curr_loss = F.cross_entropy(shift_logits.view(-1, shift_logits.size(-1)),
shift_targets.view(-1),
ignore_index=ignore_index_id)
loss += curr_loss.float()
loss_denom += 1
else:
logits = self.lm_head(x[:, [-1], :])
loss = loss / loss_denom
return logits, loss
def get_num_params(self, non_embedding=True):
"""
Return the number of parameters in the model.
For non-embedding count (default), subtract position embeddings if parameter tying applies.
If there is no parameter sharing, set the flag to False to include parameters for both input/output layers.
"""
n_params = sum(p.numel() for p in self.parameters())
if non_embedding and not self.model_args.use_rotary_embeddings:
n_params -= self.slice_transformer.pemb.weight.numel()
return n_params
@torch.no_grad()
def generate(self, x, max_new_tokens, temperature=0.8, top_k=200, top_p=0.9,
return_gen_only=False):
max_inp_len = self.model_args.max_inp_len
for _ in range(max_new_tokens):
x_ctxt = x if x.size(1) <= max_inp_len else x[:, -max_inp_len:]
logits, _ = self(x_ctxt)
next_id = None
if temperature <= 0:
next_id = torch.argmax(logits, dim=-1)
else:
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, k = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
probs = F.softmax(logits, dim=-1)
if top_p is None:
next_id = torch.multinomial(probs, num_samples=1)
else:
next_id = sample_top_p(probs, top_p)
x = torch.cat((x, next_id), dim=1)
if return_gen_only:
return x[:,-max_new_tokens:]
return x
class RambutanMLP(torch.nn.Module):
"""RambutanMLP version of MLP module used in the ELM (SliceX GPT) Transformer block."""
def __init__(self, dim=768, bits=32, dropout = 0.0):
super(RambutanMLP, self).__init__()
self.dim = dim
self.bits = bits
self.dropout = torch.nn.Dropout(dropout)
self.A1_c_w = torch.nn.Linear(self.dim, self.bits, bias=True)
self.Hexperts = 4
self.Hexpertemb = torch.nn.Embedding(self.bits, self.dim)
self.expert_aggr = torch.nn.Linear(self.Hexperts, 1)
def forward(self, x):
h_c = torch.nn.functional.softmax(self.A1_c_w(x), dim=-1)
v, i = torch.topk(h_c, self.Hexperts)
if len(x.size()) < 3:
p = v.unsqueeze(-1).expand(-1,-1,self.dim)
else:
p = v.unsqueeze(-1).expand(-1,-1,-1,self.dim)
h_emb = p * self.Hexpertemb(i)
if len(x.size()) < 3:
out = self.expert_aggr(h_emb.transpose(1,2)).reshape(h_emb.size(0), -1)
else:
out = self.expert_aggr(h_emb.transpose(-2,-1)).reshape(x.size())
out = x * out
out = self.dropout(out)
return out
class RambutanSlice(torch.nn.Module):
"""Rambutan version of ELM (SliceX GPT) Transformer block."""
def __init__(self,
model_args: ModelArgs):
super().__init__()
self.model_args = model_args
self.num_attention_heads = model_args.num_attention_heads
self.kv_channels = model_args.hidden_size // model_args.num_attention_heads
self.ln1 = torch.nn.LayerNorm(model_args.hidden_size, eps=model_args.layernorm_eps)
self.ln2 = torch.nn.LayerNorm(model_args.hidden_size, eps=model_args.layernorm_eps)
self.mlp = RambutanMLP(dim=model_args.hidden_size, bits=model_args.bits)
self.cattn = RambutanCausalSelfAttention(model_args=model_args)
def forward(self,
x: torch.Tensor,
attention_mask: torch.Tensor = None):
res = x
x = self.ln1(x)
x = self.cattn(x, attention_mask=attention_mask)
x = res + x
res = x
x = self.ln2(x)
x = self.mlp(x)
return x + res
class RambutanCausalSelfAttention(torch.nn.Module):
"""Rambutan version of self-attention module used in the ELM (SliceX GPT) transformer block."""
def __init__(self,
model_args: ModelArgs):
super().__init__()
self.model_args = model_args
n_embd = model_args.hidden_size
n_head = model_args.num_attention_heads
bias = False
dropout = model_args.attention_dropout
assert n_embd % n_head == 0
self.c_attn = torch.nn.Linear(n_embd, 3 * n_embd, bias=bias)
self.c_proj = torch.nn.Linear(n_embd, n_embd, bias=bias)
self.attn_dropout = torch.nn.Dropout(dropout)
self.resid_dropout = torch.nn.Dropout(dropout)
self.n_head = n_head
self.n_embd = n_embd
self.dropout = dropout
self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if not self.flash:
print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
self.rotary_embeddings = (
RotaryEmbedding(n_embd // n_head) if model_args.use_rotary_embeddings else None
)
def forward(self, x, attention_mask: torch.Tensor = None):
B, T, C = x.size()
device = x.device
q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
if self.rotary_embeddings:
q, k = self.rotary_embeddings(q=q, k=k)
is_causal = True
attn_mask = None
if attention_mask is not None:
att_mask_input = attention_mask
att_mask_input = att_mask_input.unsqueeze(-1).expand(B, T, T)
if is_causal:
att_mask_causal = torch.tril(torch.ones(T, T)).view(1,T,T).expand(B,T,T).to(device)
attn_mask = (att_mask_causal * att_mask_input)
else:
attn_mask = att_mask_input
attn_mask = attn_mask.unsqueeze(1).expand(B, self.n_head, T, T)
attn_mask.float().to(device)
if self.flash:
y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=self.dropout if self.training else 0, is_causal=True)
else:
att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
if is_causal and attn_mask is None:
attn_mask = torch.tril(torch.ones(T, T)).view(1,T,T).expand(B,T,T).to(device)
attn_mask = attn_mask.unsqueeze(1).expand(B, self.n_head, T, T)
if attn_mask is not None:
att = att.masked_fill(attn_mask == 0, torch.finfo(att.dtype).min)
att = F.softmax(att, dim=-1)
att = self.attn_dropout(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.resid_dropout(self.c_proj(y))
return y
def init_elm_model(model_args=ModelArgs(), device="cuda", model_config_dict=None):
"""Initialize ELM model using default or model_config parameters."""
if model_config_dict:
model_args = ModelArgs(**model_config_dict)
dtype = torch.bfloat16 if device=="cuda" and torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float16
if not torch.cuda.is_available():
dtype = torch.bfloat16
model = ELM(model_args=model_args).to(dtype=dtype)
return model
def get_h_layers_in_ckpt(ckpt_state_dict,
layer_name_template = 'slice_transformer.h.{layer_num}.'):
num_layers_in_ckpt = 0
from collections import defaultdict
layer_wise_dict = defaultdict(lambda: defaultdict(list))
layer_num_found = True
while layer_num_found:
layer_num_found = False
for layer_name in ckpt_state_dict.keys():
if layer_name_template.format(layer_num=num_layers_in_ckpt) in layer_name:
layer_wise_dict[num_layers_in_ckpt][layer_name] = ckpt_state_dict[layer_name]
layer_num_found = True
num_layers_in_ckpt += 1
return layer_wise_dict
def load_elm_model_from_ckpt(ckpt_dir, device='cuda', load_partial=False, model_args=ModelArgs(), get_num_layers_from_ckpt=True):
"""Load ELM model from local checkpoint."""
print(f"Loading ELM checkpoint from {ckpt_dir}")
ckpt_path = os.path.join(ckpt_dir, 'ckpt.pt')
checkpoint = torch.load(ckpt_path, map_location=device)
if get_num_layers_from_ckpt:
layer_name_template = 'slice_transformer.h.{layer_num}.'
ckpt_layer_wise_dict = get_h_layers_in_ckpt(checkpoint['model'],
layer_name_template = layer_name_template)
model_args.num_layers = len(ckpt_layer_wise_dict)
model = init_elm_model(model_args=model_args, device=device)
ckpt_state_dict = checkpoint['model']
unwanted_prefix = '_orig_mod.'
for k,v in list(ckpt_state_dict.items()):
if k.startswith(unwanted_prefix):
ckpt_state_dict[k[len(unwanted_prefix):]] = ckpt_state_dict.pop(k)
if load_partial:
mod_state_dict = model.state_dict()
for k,v in list(ckpt_state_dict.items()):
if k in mod_state_dict:
v_size = v.size()
mod_size = mod_state_dict[k].size()
if v_size == mod_size:
mod_state_dict[k] = v
else:
if len(v_size) == 1:
mod_state_dict[k][:v_size[-1]] = v
elif len(v_size) == 2:
mod_state_dict[k][:v_size[-2], :v_size[-1]] = v
ckpt_state_dict = mod_state_dict
load_status = model.load_state_dict(ckpt_state_dict)
print(load_status)
model.to(device)
return model
def sample_top_p(probs, threshold):
"""Perform top-p sampling on probability distribution using a threshold."""
probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
probs_sum = torch.cumsum(probs_sort, dim=-1)
mask = probs_sum - probs_sort > threshold
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
next_token = torch.multinomial(probs_sort, num_samples=1)
next_token = torch.gather(probs_idx, -1, next_token)
return next_token
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