schihei/llama2.c
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

only dart

Browse Source
This commit is contained in:
YiMing Han
2023-08-18 15:09:24 -04:00
parent 8607b11ea1
commit 01df3731d6
13 changed files with 0 additions and 1959 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.github/workflows/build.yml
-193
View File
@@ -1,193 +0,0 @@
name: Continuous Integration
on:
push:
branches:
- master
paths: ['.github/workflows/**', '**/Makefile', '**/*.c', '**/*.h', '**/*.py']
pull_request:
types: [opened, synchronize, reopened]
paths: ['**/Makefile', '**/*.c', '**/*.h', '**/*.py']
# for manual triggering
workflow_dispatch:
env:
BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
jobs:
# check basic builds to avoid breaking changes
ubuntu-focal-make:
runs-on: ubuntu-latest
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential -y
- name: Set up Python 3.10
uses: actions/setup-python@v3
with:
python-version: "3.10"
- name: Pip setup
run: |
python -m pip install --upgrade pip
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Build
id: make_build
run: |
make
- name: Build runfast
id: make_build_runfast
run: |
make runfast
- name: Test with pytest
run: |
pytest
macOS-latest-make:
runs-on: macos-latest
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update
- name: Set up Python 3.10
uses: actions/setup-python@v3
with:
python-version: "3.10"
- name: Pip setup
run: |
python -m pip install --upgrade pip
if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
- name: Build clang
id: make_build_clang
run: |
make run CC=clang
- name: Build
id: make_build
run: |
make
- name: Build runfast
id: make_build_runfast
run: |
make runfast
- name: Test with pytest
run: pytest
windows-latest-make:
runs-on: windows-latest
strategy:
fail-fast: false #necessary, otherwise the matrix breaks
matrix:
arch:
- amd64
- amd64_x86
- amd64_arm64
steps:
- name: Clone
id: checkout
uses: actions/checkout@v3
- name: Setup MSBuild
uses: microsoft/setup-msbuild@v1
- name: Setup MSVC ${{ matrix.arch }}
uses: ilammy/msvc-dev-cmd@v1
with:
arch: ${{ matrix.arch }}
- name: Set up Python 3.10
if: matrix.arch != 'amd64_arm64'
uses: actions/setup-python@v3
with:
python-version: "3.10"
- name: Pip setup
if: matrix.arch != 'amd64_arm64'
run: |
python -m pip install --upgrade pip
if (Test-Path requirements.txt) {
pip install -r requirements.txt
}
- name: Build ${{ matrix.arch }}
id: build_msvc
run: |
.\build_msvc.bat
#cross-comiled, cannot be run on host
- name: Test with pytest
if: matrix.arch != 'amd64_arm64'
run: pytest
windows-latest-mingw:
runs-on: windows-latest
defaults:
run:
shell: msys2 {0}
strategy:
matrix:
include:
- { sys: mingw64, env: x86_64 }
steps:
- name: Checkout
id: checkout
uses: actions/checkout@v3
- uses: msys2/setup-msys2@v2
id: setup-msys2
with:
msystem: ${{ matrix.sys }}
install: mingw-w64-${{matrix.env}}-gcc make
- name: Build ${{ matrix.sys }} ${{ matrix.env }}
id: build_mingw
run: |
make win64
- name: Set up Python 3.10
uses: actions/setup-python@v3
with:
python-version: "3.10"
- name: Pip setup
shell: powershell
run: |
python -m pip install --upgrade pip
if (Test-Path requirements.txt) {
pip install -r requirements.txt
}
- name: Test with pytest
shell: powershell
run: pytest
configurator.py
-47
View File
@@ -1,47 +0,0 @@
"""
Poor Man's Configurator. Probably a terrible idea. Example usage:
$ python train.py config/override_file.py --batch_size=32
this will first run config/override_file.py, then override batch_size to 32
The code in this file will be run as follows from e.g. train.py:
>>> exec(open('configurator.py').read())
So it's not a Python module, it's just shuttling this code away from train.py
The code in this script then overrides the globals()
I know people are not going to love this, I just really dislike configuration
complexity and having to prepend config. to every single variable. If someone
comes up with a better simple Python solution I am all ears.
"""
import sys
from ast import literal_eval
for arg in sys.argv[1:]:
if '=' not in arg:
# assume it's the name of a config file
assert not arg.startswith('--')
config_file = arg
print(f"Overriding config with {config_file}:")
with open(config_file) as f:
print(f.read())
exec(open(config_file).read())
else:
# assume it's a --key=value argument
assert arg.startswith('--')
key, val = arg.split('=')
key = key[2:]
if key in globals():
try:
# attempt to eval it it (e.g. if bool, number, or etc)
attempt = literal_eval(val)
except (SyntaxError, ValueError):
# if that goes wrong, just use the string
attempt = val
# ensure the types match ok
assert type(attempt) == type(globals()[key])
# cross fingers
print(f"Overriding: {key} = {attempt}")
globals()[key] = attempt
else:
raise ValueError(f"Unknown config key: {key}")
export_meta_llama_bin.py
-112
View File
@@ -1,112 +0,0 @@
"""
This script exports the Llama 2 weights in llama2c.bin format.
"""
import os
import sys
import struct
from pathlib import Path
import json
import torch
from model import precompute_freqs_cis
def export(p, state_dict, filepath='model.bin'):
"""export the model weights in fp32 into .bin file to be read from C"""
f = open(filepath, 'wb')
def serialize(key):
print(f"writing {key}...")
t = state_dict[key].contiguous().view(-1).type(torch.float32).numpy()
f.write(memoryview(t))
del state_dict[key]
# first write out the header
hidden_dim = state_dict['layers.0.feed_forward.w1.weight'].shape[0]
p['vocab_size'] = 32000
p['max_seq_len'] = 2048
n_kv_heads = p.get('n_kv_heads') or p['n_heads']
header = struct.pack(
'iiiiiii',
p['dim'], hidden_dim, p['n_layers'], p['n_heads'],
n_kv_heads, -p['vocab_size'], p['max_seq_len']
)
# NOTE ABOVE: -ve vocab_size is indicating that the classifier weights are present
# in the checkpoint and should be loaded.
f.write(header)
# next write out the embedding weights
print("writing tok_embeddings...")
serialize('tok_embeddings.weight')
# now all the layers
# attention weights
for i in range(p['n_layers']): serialize(f'layers.{i}.attention_norm.weight')
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wq.weight')
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wk.weight')
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wv.weight')
for i in range(p['n_layers']): serialize(f'layers.{i}.attention.wo.weight')
# ffn weights
for i in range(p['n_layers']): serialize(f'layers.{i}.ffn_norm.weight')
for i in range(p['n_layers']): serialize(f'layers.{i}.feed_forward.w1.weight')
for i in range(p['n_layers']): serialize(f'layers.{i}.feed_forward.w2.weight')
for i in range(p['n_layers']): serialize(f'layers.{i}.feed_forward.w3.weight')
# final rmsnorm
serialize('norm.weight')
# freqs_cos, freqs_sin
freqs_cos, freqs_sin = precompute_freqs_cis(p['dim'] // p['n_heads'], p['max_seq_len'] * 2)
state_dict['freqs_cos'] = freqs_cos[:p['max_seq_len']]
state_dict['freqs_sin'] = freqs_sin[:p['max_seq_len']]
serialize('freqs_cos')
serialize('freqs_sin')
# finally write the output weights
serialize('output.weight')
f.close()
print(f"wrote {filepath}")
def concat_weights(models):
state_dict = {}
for name in list(models[0]):
tensors = [model[name] for model in models]
if len(tensors) == 1 or len(tensors[0].shape) == 1:
state_dict[name] = tensors[0]
continue
is_axis_1 = (
name.startswith('tok_embeddings.')
or name.endswith('.attention.wo.weight')
or name.endswith('.feed_forward.w2.weight')
)
axis = 1 if is_axis_1 else 0
state_dict[name] = torch.cat(tensors, dim=axis)
for model in models:
del model[name]
return state_dict
def load_and_export(model_path, output_path):
params_path = os.path.join(model_path, 'params.json')
with open(params_path) as f:
params = json.load(f)
print(params)
model_paths = sorted(list(Path(model_path).glob('consolidated.*.pth')))
models = [torch.load(p, map_location='cpu') for p in model_paths]
state_dict = concat_weights(models)
del models
export(params, state_dict, output_path)
if __name__ == '__main__':
if len(sys.argv) == 1:
print('[Llama model folder path] [output path]')
exit()
model_path = sys.argv[1]
output_path = sys.argv[2]
load_and_export(model_path, output_path)
export_meta_llama_hf_bin.py
-113
View File
@@ -1,113 +0,0 @@
"""
This script exports the Llama 2 weights in llama2c.bin format.
"""
import os
import sys
import struct
from pathlib import Path
import json
import torch
from model import precompute_freqs_cis
def export(p, state_dict, filepath='model.bin'):
"""export the model weights in fp32 into .bin file to be read from C"""
f = open(filepath, 'wb')
def serialize(key):
print(f"writing {key}...")
t = state_dict[key].contiguous().view(-1).type(torch.float32).numpy()
f.write(memoryview(t))
del state_dict[key]
# first write out the header
hidden_dim = state_dict['model.layers.0.mlp.gate_proj.weight'].shape[0]
p['vocab_size'] = 32000
p['max_seq_len'] = 2048
n_kv_heads = p.get('n_kv_heads') or p['n_heads']
header = struct.pack(
'iiiiiii',
p['dim'], hidden_dim, p['n_layers'], p['n_heads'],
n_kv_heads, -p['vocab_size'], p['max_seq_len']
)
# NOTE ABOVE: -ve vocab_size is indicating that the classifier weights are present
# in the checkpoint and should be loaded.
f.write(header)
# next write out the embedding weights
print("writing tok_embeddings...")
serialize('model.embed_tokens.weight')
# now all the layers
# attention weights
for i in range(p['n_layers']): serialize(f'model.layers.{i}.input_layernorm.weight')
for i in range(p['n_layers']): serialize(f'model.layers.{i}.self_attn.q_proj.weight')
for i in range(p['n_layers']): serialize(f'model.layers.{i}.self_attn.k_proj.weight')
for i in range(p['n_layers']): serialize(f'model.layers.{i}.self_attn.v_proj.weight')
for i in range(p['n_layers']): serialize(f'model.layers.{i}.self_attn.o_proj.weight')
# ffn weights
for i in range(p['n_layers']): serialize(f'model.layers.{i}.post_attention_layernorm.weight')
for i in range(p['n_layers']): serialize(f'model.layers.{i}.mlp.gate_proj.weight')
for i in range(p['n_layers']): serialize(f'model.layers.{i}.mlp.down_proj.weight')
for i in range(p['n_layers']): serialize(f'model.layers.{i}.mlp.up_proj.weight')
# final rmsnorm
serialize('model.norm.weight')
# freqs_cos, freqs_sin
freqs_cos, freqs_sin = precompute_freqs_cis(p['dim'] // p['n_heads'], p['max_seq_len'] * 2)
state_dict['freqs_cos'] = freqs_cos[:p['max_seq_len']]
state_dict['freqs_sin'] = freqs_sin[:p['max_seq_len']]
# check if this requires addtional conversion
serialize('freqs_cos')
serialize('freqs_sin')
# finally write the output weights
serialize('lm_head.weight')
f.close()
print(f"wrote {filepath}")
def concat_weights(models):
state_dict = {}
for name in list(models[0]):
tensors = [model[name] for model in models]
if len(tensors) == 1 or len(tensors[0].shape) == 1:
state_dict[name] = tensors[0]
continue
is_axis_1 = (
name.startswith('model.embed_tokens.weight')
or name.endswith('.self_attn.o_proj.weight')
or name.endswith('.mlp.down_proj.weight')
)
axis = 1 if is_axis_1 else 0
state_dict[name] = torch.cat(tensors, dim=axis)
for model in models:
del model[name]
return state_dict
def load_and_export(model_path, output_path):
params_path = os.path.join(model_path, 'params.json')
with open(params_path) as f:
params = json.load(f)
print(params)
model_paths = sorted(list(Path(model_path).glob('consolidated.*.pth')))
models = [torch.load(p, map_location='cpu') for p in model_paths]
state_dict = concat_weights(models)
del models
export(params, state_dict, output_path)
if __name__ == '__main__':
if len(sys.argv) == 1:
print('[Llama model folder path] [output path]')
exit()
model_path = sys.argv[1]
output_path = sys.argv[2]
load_and_export(model_path, output_path)
model.py
-392
View File
@@ -1,392 +0,0 @@
import math
import struct
import inspect
from dataclasses import dataclass
from typing import Any, Optional, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
@dataclass
class ModelArgs:
# default hyperparameters for the Llama 7B model
dim: int = 4096
n_layers: int = 32
n_heads: int = 32
n_kv_heads: Optional[int] = None
vocab_size: int = 32000
multiple_of: int = 256 # MLP hidden layer size will be multiple of
norm_eps: float = 1e-5
max_seq_len: int = 2048
dropout: float = 0.0
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
output = self._norm(x.float()).type_as(x)
return output * self.weight
def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
t = torch.arange(end, device=freqs.device) # type: ignore
freqs = torch.outer(t, freqs).float() # type: ignore
freqs_cos = torch.cos(freqs) # real part
freqs_sin = torch.sin(freqs) # imaginary part
return freqs_cos, freqs_sin
def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
ndim = x.ndim
assert 0 <= 1 < ndim
assert freqs_cis.shape == (x.shape[1], x.shape[-1])
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return freqs_cis.view(shape)
def apply_rotary_emb(
xq: torch.Tensor,
xk: torch.Tensor,
freqs_cos: torch.Tensor,
freqs_sin: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# reshape xq and xk to match the complex representation
xq_r, xq_i = xq.float().reshape(xq.shape[:-1] + (-1, 2)).unbind(-1)
xk_r, xk_i = xk.float().reshape(xk.shape[:-1] + (-1, 2)).unbind(-1)
# reshape freqs_cos and freqs_sin for broadcasting
freqs_cos = reshape_for_broadcast(freqs_cos, xq_r)
freqs_sin = reshape_for_broadcast(freqs_sin, xq_r)
# apply rotation using real numbers
xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
xk_out_r = xk_r * freqs_cos - xk_i * freqs_sin
xk_out_i = xk_r * freqs_sin + xk_i * freqs_cos
# flatten last two dimensions
xq_out = torch.stack([xq_out_r, xq_out_i], dim=-1).flatten(3)
xk_out = torch.stack([xk_out_r, xk_out_i], dim=-1).flatten(3)
return xq_out.type_as(xq), xk_out.type_as(xk)
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
bs, slen, n_kv_heads, head_dim = x.shape
if n_rep == 1:
return x
return (
x[:, :, :, None, :]
.expand(bs, slen, n_kv_heads, n_rep, head_dim)
.reshape(bs, slen, n_kv_heads * n_rep, head_dim)
)
class Attention(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
assert args.n_heads % self.n_kv_heads == 0
model_parallel_size = 1
self.n_local_heads = args.n_heads // model_parallel_size
self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
self.n_rep = self.n_local_heads // self.n_local_kv_heads
self.head_dim = args.dim // args.n_heads
self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
self.attn_dropout = nn.Dropout(args.dropout)
self.resid_dropout = nn.Dropout(args.dropout)
self.dropout = args.dropout
# use flash attention or a manual implementation?
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")
mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
mask = torch.triu(mask, diagonal=1)
self.register_buffer("mask", mask)
def forward(
self,
x: torch.Tensor,
freqs_cos: torch.Tensor,
freqs_sin: torch.Tensor,
):
bsz, seqlen, _ = x.shape
# QKV
xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
# RoPE relative positional embeddings
xq, xk = apply_rotary_emb(xq, xk, freqs_cos, freqs_sin)
# grouped multiquery attention: expand out keys and values
xk = repeat_kv(xk, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
xv = repeat_kv(xv, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
# make heads into a batch dimension
xq = xq.transpose(1, 2) # (bs, n_local_heads, seqlen, head_dim)
xk = xk.transpose(1, 2)
xv = xv.transpose(1, 2)
# flash implementation
if self.flash:
output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None, dropout_p=self.dropout if self.training else 0.0, is_causal=True)
else:
# manual implementation
scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
assert hasattr(self, 'mask')
scores = scores + self.mask[:, :, :seqlen, :seqlen] # (bs, n_local_heads, seqlen, cache_len + seqlen)
scores = F.softmax(scores.float(), dim=-1).type_as(xq)
scores = self.attn_dropout(scores)
output = torch.matmul(scores, xv) # (bs, n_local_heads, seqlen, head_dim)
# restore time as batch dimension and concat heads
output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
# final projection into the residual stream
output = self.wo(output)
output = self.resid_dropout(output)
return output
class FeedForward(nn.Module):
def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
super().__init__()
hidden_dim = int(2 * hidden_dim / 3)
hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
self.w1 = nn.Linear(dim, hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim, dim, bias=False)
self.w3 = nn.Linear(dim, hidden_dim, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
class TransformerBlock(nn.Module):
def __init__(self, layer_id: int, args: ModelArgs):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
self.attention = Attention(args)
self.feed_forward = FeedForward(
dim=args.dim,
hidden_dim=4 * args.dim,
multiple_of=args.multiple_of,
dropout=args.dropout,
)
self.layer_id = layer_id
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
def forward(self, x, freqs_cos, freqs_sin):
h = x + self.attention.forward(self.attention_norm(x), freqs_cos, freqs_sin)
out = h + self.feed_forward.forward(self.ffn_norm(h))
return out
class Transformer(nn.Module):
last_loss: Optional[torch.Tensor]
def __init__(self, params: ModelArgs):
super().__init__()
self.params = params
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
self.dropout = nn.Dropout(params.dropout)
self.layers = torch.nn.ModuleList()
for layer_id in range(params.n_layers):
self.layers.append(TransformerBlock(layer_id, params))
self.norm = RMSNorm(params.dim, eps=params.norm_eps)
self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
# share the unembedding parameters with the embedding parameters
self.tok_embeddings.weight = self.output.weight # https://paperswithcode.com/method/weight-tying
# some useful precompute for the RoPE relative positional embeddings
freqs_cos, freqs_sin = precompute_freqs_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
self.register_buffer("freqs_cos", freqs_cos, persistent=False)
self.register_buffer("freqs_sin", freqs_sin, persistent=False)
# init all weights
self.apply(self._init_weights)
# apply special scaled init to the residual projections, per GPT-2 paper
for pn, p in self.named_parameters():
if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * params.n_layers))
# Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
self.last_loss = None
def _init_weights(self, module):
if isinstance(module, nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, tokens: torch.Tensor, targets: Optional[torch.Tensor] = None) -> torch.Tensor:
_bsz, seqlen = tokens.shape
h = self.tok_embeddings(tokens)
h = self.dropout(h)
freqs_cos = self.freqs_cos[:seqlen]
freqs_sin = self.freqs_sin[:seqlen]
for layer in self.layers:
h = layer(h, freqs_cos, freqs_sin)
h = self.norm(h)
if targets is not None:
# if we are given some desired targets also calculate the loss
logits = self.output(h)
self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
# inference-time mini-optimization: only forward the output on the very last position
logits = self.output(h[:, [-1], :]) # note: using list [-1] to preserve the time dim
self.last_loss = None
return logits
def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
# start with all of the candidate parameters
param_dict = {pn: p for pn, p in self.named_parameters()}
# filter out those that do not require grad
param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
# create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
# i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
optim_groups = [
{'params': decay_params, 'weight_decay': weight_decay},
{'params': nodecay_params, 'weight_decay': 0.0}
]
num_decay_params = sum(p.numel() for p in decay_params)
num_nodecay_params = sum(p.numel() for p in nodecay_params)
print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
# Create AdamW optimizer and use the fused version if it is available
fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and device_type == 'cuda'
extra_args = dict(fused=True) if use_fused else dict()
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
print(f"using fused AdamW: {use_fused}")
return optimizer
def estimate_mfu(self, fwdbwd_per_iter, dt):
""" estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """
# first estimate the number of flops we do per iteration.
# see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
N = sum(p.numel() for p in self.parameters())
cfg = self.params
L, H, Q, T = cfg.n_layers, cfg.n_heads, cfg.dim//cfg.n_heads, cfg.max_seq_len
flops_per_token = 6*N + 12*L*H*Q*T
flops_per_fwdbwd = flops_per_token * T
flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
# express our flops throughput as ratio of A100 bfloat16 peak flops
flops_achieved = flops_per_iter * (1.0/dt) # per second
flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
mfu = flops_achieved / flops_promised
return mfu
@torch.inference_mode()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
"""
Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
the sequence max_new_tokens times, feeding the predictions back into the model each time.
Most likely you'll want to make sure to be in model.eval() mode of operation for this.
Also note this is a super inefficient version of sampling with no key/value cache.
"""
for _ in range(max_new_tokens):
# if the sequence context is growing too long we must crop it at block_size
idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:]
# forward the model to get the logits for the index in the sequence
logits = self(idx_cond)
logits = logits[:, -1, :] # crop to just the final time step
if temperature == 0.0:
# "sample" the single most likely index
_, idx_next = torch.topk(logits, k=1, dim=-1)
else:
# pluck the logits at the final step and scale by desired temperature
logits = logits / temperature
# optionally crop the logits to only the top k options
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('Inf')
# apply softmax to convert logits to (normalized) probabilities
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
# append sampled index to the running sequence and continue
idx = torch.cat((idx, idx_next), dim=1)
return idx
def export(self, filepath='model.bin'):
"""export the model weights in fp32 into .bin file to be read from C"""
f = open(filepath, 'wb')
def serialize(t):
d = t.detach().cpu().view(-1).numpy().astype(np.float32)
b = struct.pack(f'{len(d)}f', *d)
f.write(b)
# first write out the header
hidden_dim = self.layers[0].feed_forward.w1.weight.shape[0]
p = self.params
n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
n_kv_heads, p.vocab_size, p.max_seq_len)
f.write(header)
# next write out the embedding weights
serialize(self.tok_embeddings.weight)
# now all the layers
# attention weights
for layer in self.layers:
serialize(layer.attention_norm.weight)
for layer in self.layers:
serialize(layer.attention.wq.weight)
for layer in self.layers:
serialize(layer.attention.wk.weight)
for layer in self.layers:
serialize(layer.attention.wv.weight)
for layer in self.layers:
serialize(layer.attention.wo.weight)
# ffn weights
for layer in self.layers:
serialize(layer.ffn_norm.weight)
for layer in self.layers:
serialize(layer.feed_forward.w1.weight)
for layer in self.layers:
serialize(layer.feed_forward.w2.weight)
for layer in self.layers:
serialize(layer.feed_forward.w3.weight)
# final rmsnorm
serialize(self.norm.weight)
# note: no need to write final classifier weights due to weight sharing
# freqs_cis
serialize(self.freqs_cos[:p.max_seq_len])
serialize(self.freqs_sin[:p.max_seq_len])
# write to binary file
f.close()
print(f"wrote {filepath}")
requirements.txt
-7
View File
@@ -1,7 +0,0 @@
numpy==1.23.5
pytest==7.4.0
Requests==2.31.0
sentencepiece==0.1.99
torch==2.0.1
tqdm==4.64.1
wandb==0.15.5
run.ipynb
-130
View File
@@ -1,130 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "HLdoj4cz-xal"
},
"source": [
"# Run.c\n",
"\n",
"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/karpathy/llama2.c/blob/master/run.ipynb)\n",
"\n",
"More details can be found in the [README.md](README.md) ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Une3Ozlnu1B7"
},
"outputs": [],
"source": [
"#@title Clone Project\n",
"\n",
"!git clone https://github.com/karpathy/llama2.c.git\n",
"%cd llama2.c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Build\n",
"\n",
"!make runfast"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "thm0ZBrtSgoC"
},
"outputs": [],
"source": [
"#@title Pick Your Model\n",
"\n",
"#@markdown Choose model\n",
"model = \"stories15M\" #@param [\"stories15M\", \"stories42M\", \"stories110M\"]\n",
"\n",
"download_url = \"\"\n",
"\n",
"if(model == \"stories15M\"):\n",
" download_url = \"https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.bin\"\n",
"if(model == \"stories42M\"):\n",
" download_url = \"https://huggingface.co/karpathy/tinyllamas/resolve/main/stories42M.bin\"\n",
"if(model == \"stories110M\"):\n",
" download_url = \"https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.bin\"\n",
"\n",
"print(f\"download_url: {download_url}\")\n",
"\n",
"!wget $download_url\n",
"\n",
"model_file = model + \".bin\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OgAc3KjuT-NM"
},
"outputs": [],
"source": [
"#@title Generate Stories\n",
"\n",
"# Generate args\n",
"max_token = 256 #@param {type:\"slider\", min:32, max:1024, step:32}\n",
"temperature = 0.8 #@param {type:\"slider\", min:0.0, max:1, step:0.05}\n",
"top_p = 0.9 #@param {type:\"slider\", min:0.0, max:1.0, step:0.05}\n",
"prompt = \"One day, Lily met a Shoggoth\" #@param {type:\"string\"}\n",
"\n",
"print(f\"model: {model_file}, max_token: {max_token}, temperature: {temperature}, top_p: {top_p}, prompt: {prompt}\")\n",
"print(f\"----------------------------\\n\")\n",
"\n",
"cmd = f'./run {model_file} -t {temperature} -p {top_p} -n {max_token} -i \"{prompt}\"'\n",
"!{cmd}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#@title Run Meta's Llama 2 models\n",
"\n",
"#@markdown input your huggingface [access token](https://huggingface.co/settings/tokens) to download Meta's Llama 2 models.\n",
"\n",
"from huggingface_hub import snapshot_download\n",
"\n",
"token = \"replace your huggingface access token\" #@param {type:\"string\"}\n",
"path = snapshot_download(repo_id=\"meta-llama/Llama-2-7b\",cache_dir=\"Llama-2-7b\", use_auth_token=token)\n",
"\n",
"!python export_meta_llama_bin.py $path llama2_7b.bin\n",
"\n",
"print(\"./run llama2_7b.bin\\n\")\n",
"!./run llama2_7b.bin"
]
}
],
"metadata": {
"colab": {
"private_outputs": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
sample.py
-79
View File
@@ -1,79 +0,0 @@
"""
Sample from the trained model with PyTorch
"""
import os
import pickle
from contextlib import nullcontext
import torch
from model import ModelArgs, Transformer
from tokenizer import Tokenizer
from tinystories import get_tokenizer_model_path
# -----------------------------------------------------------------------------
checkpoint = 'out/ckpt.pt'
start = "" # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
num_samples = 1 # number of samples to draw
max_new_tokens = 100 # number of tokens generated in each sample
temperature = 1.0 # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
top_k = 300 # retain only the top_k most likely tokens, clamp others to have 0 probability
tokenizer = "" # override the tokenizer model path
seed = 1337
device = 'cuda' if torch.cuda.is_available() else 'cpu' # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
#dtype = 'bfloat16' if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else 'float16' # 'float32' or 'bfloat16' or 'float16'
dtype = "float32"
compile = False # use PyTorch 2.0 to compile the model to be faster
exec(open('configurator.py').read()) # overrides from command line or config file
# -----------------------------------------------------------------------------
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
device_type = 'cuda' if 'cuda' in device else 'cpu' # for later use in torch.autocast
ptdtype = {'float32': torch.float32, 'bfloat16': torch.bfloat16, 'float16': torch.float16}[dtype]
ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
# init from a model saved in a specific directory
checkpoint_dict = torch.load(checkpoint, map_location=device)
gptconf = ModelArgs(**checkpoint_dict['model_args'])
model = Transformer(gptconf)
state_dict = checkpoint_dict['model']
unwanted_prefix = '_orig_mod.'
for k,v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
model.load_state_dict(state_dict, strict=False)
model.eval()
model.to(device)
if compile:
print("Compiling the model...")
model = torch.compile(model) # requires PyTorch 2.0 (optional)
# load the tokenizer
vocab_source = checkpoint_dict.get("vocab_source", "llama2")
vocab_size = gptconf.vocab_size
if tokenizer:
# a specific tokenizer is provided, use it
tokenizer_model = tokenizer
else:
# let's try to find the tokenizer model automatically. bit gross here...
query_vocab_size = 0 if vocab_source == "llama2" else vocab_size
tokenizer_model = get_tokenizer_model_path(vocab_size=query_vocab_size)
enc = Tokenizer(tokenizer_model=tokenizer_model)
# encode the beginning of the prompt
if start.startswith('FILE:'):
with open(start[5:], 'r', encoding='utf-8') as f:
start = f.read()
start_ids = enc.encode(start, bos=True, eos=False)
x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
# run generation
with torch.no_grad():
with ctx:
for k in range(num_samples):
y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
print(enc.decode(y[0].tolist()))
print('---------------')
save_torchscript.py
-66
View File
@@ -1,66 +0,0 @@
#!/usr/bin/env python
"""Saves the model as a TorchScript.
Usage examples:
./save_torchscript.py
./save_torchscript.py --dim=300
./save_torchscript.py --gzip_output=True --zero_params=True
The resulting file can be loaded in C++ code and then used for training or
inference with:
#include <torch/script.h>
torch::jit::Module module = torch::jit::load("model.pt")
Note that the serialized model includes the initial parameters and with the default
ModelArgs the file is 59M and gzips down to 55M. If you want to serialize/distribute
the model parameters separately you can zero out the parameters before saving it and
it will gzip down to 780K.
"""
import gzip
import os
import shutil
from inspect import signature
import torch
from model import ModelArgs, Transformer
# Model args config
dim = 288
n_layers = 6
n_heads = 6
n_kv_heads = n_heads
multiple_of = 32
max_seq_len = 256
dropout = 0.0
vocab_size = 32000
norm_eps = 1e-5
# Save config
model_path = "model.pt"
zero_params = False
gzip_output = False
# Allow config overrides
exec(open("configurator.py").read())
def main() -> None:
model_args = {k: globals()[k] for k in signature(ModelArgs).parameters}
model = Transformer(ModelArgs(**model_args))
# If requested zero params before saving the model. This is useful in
# conjunction with gzip_output.
if zero_params:
for p in model.parameters():
p.detach().zero_()
torch.jit.save(torch.jit.script(model), model_path)
if gzip_output:
with open(model_path, "rb") as f_in:
with gzip.open(f"{model_path}.gz", "wb") as f_out:
shutil.copyfileobj(f_in, f_out)
os.unlink(model_path)
if __name__ == "__main__":
main()
tinystories.py
-274
View File
@@ -1,274 +0,0 @@
"""
Download, preprocess and serve the TinyStories dataset as a DataLoader.
"""
import argparse
import glob
import json
import os
import random
from typing import List
from concurrent.futures import ProcessPoolExecutor
from functools import partial
import numpy as np
import requests
import torch
import torch.distributed as dist
from tqdm import tqdm
from tokenizer import Tokenizer
DATA_CACHE_DIR = "data"
def download_file(url: str, fname: str, chunk_size=1024):
"""Helper function to download a file from a given url"""
resp = requests.get(url, stream=True)
total = int(resp.headers.get("content-length", 0))
with open(fname, "wb") as file, tqdm(
desc=fname,
total=total,
unit="iB",
unit_scale=True,
unit_divisor=1024,
) as bar:
for data in resp.iter_content(chunk_size=chunk_size):
size = file.write(data)
bar.update(size)
def download():
"""Downloads the TinyStories dataset to DATA_CACHE_DIR"""
os.makedirs(DATA_CACHE_DIR, exist_ok=True)
# download the TinyStories dataset, unless it's already downloaded
data_url = "https://huggingface.co/datasets/roneneldan/TinyStories/resolve/main/TinyStories_all_data.tar.gz"
data_filename = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data.tar.gz")
if not os.path.exists(data_filename):
print(f"Downloading {data_url} to {data_filename}...")
download_file(data_url, data_filename)
else:
print(f"{data_filename} already exists, skipping download...")
# unpack the tar.gz file into all the data shards (json files)
data_dir = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data")
if not os.path.exists(data_dir):
os.makedirs(data_dir, exist_ok=True)
print(f"Unpacking {data_filename}...")
os.system(f"tar -xzf {data_filename} -C {data_dir}")
else:
print(f"{data_dir} already exists, skipping unpacking...")
# print a single example just for debugging and such
shard_filenames = sorted(glob.glob(os.path.join(data_dir, "*.json")))
with open(shard_filenames[0], "r") as f:
data = json.load(f)
print("Download done.")
print(f"Number of shards: {len(shard_filenames)}")
print(f"Example story:\n{data[0]}")
def train_vocab(vocab_size):
"""
Trains a custom sentencepiece tokenizer on the TinyStories dataset.
The custom tokenizer files will be saved in DATA_CACHE_DIR/tok{N} directories,
where N is the vocab size. This is also where the pretok .bin files will go.
"""
assert vocab_size > 0, "Vocab size must be positive"
# output file prefix path for sentencepiece
prefix = os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}")
# how many shards we'll use for vocab training, kept low for efficiency
num_shards = 10
# 1) export a large chunk of text as a single text file tiny.txt
tiny_file = os.path.join(DATA_CACHE_DIR, "tiny.txt")
data_dir = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data")
shard_filenames = sorted(glob.glob(os.path.join(data_dir, "*.json")))
print(f"Writing temporary file {tiny_file} with {num_shards} shards...")
with open(tiny_file, "w") as of:
for shard in tqdm(shard_filenames[:num_shards]):
with open(shard, "r") as f:
data = json.load(f)
for example in data:
text = example["story"]
text = text.strip()
of.write(text + "\n")
print(f"Size is: {os.path.getsize(tiny_file) / 1024 / 1024:.2f} MB")
# 2) run the train_vocab.sh script that trains the sentencepiece model
print("Will now train the vocab with:")
cmd = f"bash train_vocab.sh {tiny_file} {prefix} {vocab_size}"
print(cmd)
print("OK? [y/N] ")
dec = input()
if dec.lower() != "y":
print("Exiting...")
return
os.system(cmd)
# 3) optional cleanup, ask the user if they'd like to delete tiny.txt
dec = input(f"Delete the temporary file {tiny_file}? [y/N] ")
if dec.lower() == "y":
os.remove(tiny_file)
print(f"Deleted {tiny_file}")
print(f"Trained tokenizer is in {prefix}.model")
print("Done.")
def process_shard(args, vocab_size):
shard_id, shard = args
tokenizer_model = get_tokenizer_model_path(vocab_size)
enc = Tokenizer(tokenizer_model)
with open(shard, "r") as f:
data = json.load(f)
all_tokens = []
for example in tqdm(data, position=shard_id):
text = example["story"]
text = text.strip() # get rid of leading/trailing whitespace
tokens = enc.encode(text, bos=True, eos=False) # encode the text, use BOS
all_tokens.extend(tokens)
# convert to uint16 nparray
all_tokens = np.array(all_tokens, dtype=np.uint16)
# calculate the output filename
if vocab_size == 0:
# if we're using Llama 2, just save the tokenized file in the same dir
tokenized_filename = shard.replace(".json", ".bin")
else:
# save .bin files into a new tok{N} directory
bin_dir = os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}")
shard_basename = os.path.basename(shard)
bin_basename = shard_basename.replace(".json", ".bin")
tokenized_filename = os.path.join(bin_dir, bin_basename)
# write the bytes
with open(tokenized_filename, "wb") as f:
f.write(all_tokens.tobytes())
# calculate the average sequence length (they are separated by BOS=1)
avg_seq_len = all_tokens.size / ((all_tokens == 1).sum())
print(f"Saved {tokenized_filename}, average seqlen: {avg_seq_len:.2f}")
def pretokenize(vocab_size):
# iterate the shards and tokenize all of them one by one
data_dir = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data")
shard_filenames = sorted(glob.glob(os.path.join(data_dir, "*.json")))
if vocab_size > 0:
# .bin files will be saved into tok{N} directory, create it once here
bin_dir = os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}")
os.makedirs(bin_dir, exist_ok=True)
# process all the shards in a process pool
fun = partial(process_shard, vocab_size=vocab_size)
with ProcessPoolExecutor() as executor:
executor.map(fun, enumerate(shard_filenames))
print("Done.")
class PretokDataset(torch.utils.data.IterableDataset):
"""Loads pretokenized examples from disk and yields them as PyTorch tensors."""
def __init__(self, split, max_seq_len, vocab_size, vocab_source):
super().__init__()
self.split = split
self.max_seq_len = max_seq_len
self.vocab_size = vocab_size
self.vocab_source = vocab_source
def __iter__(self):
# get worker info within a DataLoader
worker_info = torch.utils.data.get_worker_info()
worker_id = worker_info.id if worker_info else 0
# get DDP rank info
rank = dist.get_rank() if dist.is_initialized() else 0
# combine the worker_id and worker_rank to create a unique seed for rng
seed = 42 + worker_id + 1337 * rank
rng = random.Random(seed)
print(f"Created a PretokDataset with rng seed {seed}")
if self.vocab_source == "llama2":
# the .bin files are right along the .json files
bin_dir = os.path.join(DATA_CACHE_DIR, "TinyStories_all_data")
shard_filenames = sorted(glob.glob(os.path.join(bin_dir, "*.bin")))
elif self.vocab_source == "custom":
# the .bin files are in tok{N} directory
bin_dir = os.path.join(DATA_CACHE_DIR, f"tok{self.vocab_size}")
shard_filenames = sorted(glob.glob(os.path.join(bin_dir, "*.bin")))
# train/test split. let's use only shard 0 for test split, rest train
shard_filenames = shard_filenames[1:] if self.split == "train" else shard_filenames[:1]
while True:
rng.shuffle(shard_filenames)
for shard in shard_filenames:
# open the dataset for reading but keep it on disk with memmap
m = np.memmap(shard, dtype=np.uint16, mode="r")
num_batches = len(m) // self.max_seq_len
num_batches -= 1 # drop the last partial batch
assert num_batches > 0, "this shard is way too small? investigate."
ixs = list(range(num_batches))
rng.shuffle(ixs)
for ix in ixs:
start = ix * self.max_seq_len
end = start + self.max_seq_len + 1
# calling .astype will copy the data into a new numpy array, now in RAM
chunk = torch.from_numpy((m[start:end]).astype(np.int64))
x = chunk[:-1]
y = chunk[1:]
yield x, y
# -----------------------------------------------------------------------------
# public interface functions
def get_tokenizer_model_path(vocab_size):
"""
Returns path to the sentencepiece tokenizer model for a given vocab size
vocab_size = 0 designates the default Llama 2 tokenizer, in that case
None is returned.
"""
if vocab_size == 0:
return None
else:
return os.path.join(DATA_CACHE_DIR, f"tok{vocab_size}.model")
class Task:
@staticmethod
def iter_batches(batch_size, device, num_workers=0, **dataset_kwargs):
ds = PretokDataset(**dataset_kwargs)
dl = torch.utils.data.DataLoader(
ds, batch_size=batch_size, pin_memory=True, num_workers=num_workers
)
for x, y in dl:
x = x.to(device, non_blocking=True)
y = y.to(device, non_blocking=True)
yield x, y
# -----------------------------------------------------------------------------
# CLI for constructing the dataset
if __name__ == "__main__":
"""
These stages are designed to be run in order.
To tokenize data with the Llama 2 tokenizer:
python tinystories.py download
python tinystories.py pretokenize
To tokenize data with a custom tokenizer we train ourselves with sentencepiece, e.g.:
python tinystories.py download
python tinystories.py train_vocab --vocab_size=2048
python tinystories.py pretokenize --vocab_size=2048
"""
parser = argparse.ArgumentParser()
parser.add_argument("stage", type=str, choices=["download", "pretokenize", "train_vocab"])
parser.add_argument("--vocab_size", type=int, default=0, help="pretokenization vocab size. 0 = use Llama 2 tokenizer.")
args = parser.parse_args()
# depending on the stage call the appropriate function
if args.stage == "download":
download()
elif args.stage == "train_vocab":
train_vocab(vocab_size=args.vocab_size)
elif args.stage == "pretokenize":
pretokenize(vocab_size=args.vocab_size)
else:
raise ValueError(f"Unknown stage {args.stage}")
tokenizer.py
-78
View File
@@ -1,78 +0,0 @@
# Taken from llama code and lightly modified
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.
import os
import struct
import argparse
from typing import List
from sentencepiece import SentencePieceProcessor
TOKENIZER_MODEL = "tokenizer.model" # the llama sentencepiece tokenizer model
class Tokenizer:
def __init__(self, tokenizer_model=None):
model_path = tokenizer_model if tokenizer_model else TOKENIZER_MODEL
assert os.path.isfile(model_path), model_path
self.sp_model = SentencePieceProcessor(model_file=model_path)
self.model_path = model_path
# BOS / EOS token IDs
self.n_words: int = self.sp_model.vocab_size()
self.bos_id: int = self.sp_model.bos_id()
self.eos_id: int = self.sp_model.eos_id()
self.pad_id: int = self.sp_model.pad_id()
#print(f"#words: {self.n_words} - BOS ID: {self.bos_id} - EOS ID: {self.eos_id}")
assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
def encode(self, s: str, bos: bool, eos: bool) -> List[int]:
assert type(s) is str
t = self.sp_model.encode(s)
if bos:
t = [self.bos_id] + t
if eos:
t = t + [self.eos_id]
return t
def decode(self, t: List[int]) -> str:
return self.sp_model.decode(t)
def export(self):
# get all the tokens (postprocessed) and their scores as floats
tokens, scores = [], []
for i in range(self.n_words):
# decode the token and light postprocessing
t = self.sp_model.id_to_piece(i)
s = self.sp_model.get_score(i)
if i == self.bos_id:
t = '\n<s>\n'
elif i == self.eos_id:
t = '\n</s>\n'
t = t.replace('▁', ' ') # sentencepiece uses this character as whitespace
b = t.encode('utf-8') # bytes of this token, utf-8 encoded
tokens.append(b)
scores.append(s)
# record the max token length
max_token_length = max(len(t) for t in tokens)
# write to a binary file
# the tokenizer.bin file is the same as .model file, but .bin
tokenizer_bin = self.model_path.replace('.model', '.bin')
with open(tokenizer_bin, 'wb') as f:
f.write(struct.pack("I", max_token_length))
for bytes, score in zip(tokens, scores):
f.write(struct.pack("fI", score, len(bytes)))
f.write(bytes)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("-t", "--tokenizer-model", type=str, help="optional path to custom tokenizer ")
args = parser.parse_args()
t = Tokenizer(args.tokenizer_model)
t.export()
train.py
-342
View File
@@ -1,342 +0,0 @@
"""
This training script can be run both on a single gpu in debug mode,
and also in a larger training run with distributed data parallel (ddp).
To run on a single GPU small debug run, example:
$ python -m train.py --compile=False --eval_iters=10 --batch_size=8
To run with DDP on 4 gpus on 1 node, example:
$ torchrun --standalone --nproc_per_node=4 train.py
To run with DDP on 4 gpus across 2 nodes, example:
- Run on the first (master) node with example IP 123.456.123.456:
$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=0 --master_addr=123.456.123.456 --master_port=1234 train.py
- Run on the worker node:
$ torchrun --nproc_per_node=8 --nnodes=2 --node_rank=1 --master_addr=123.456.123.456 --master_port=1234 train.py
(If your cluster does not have Infiniband interconnect prepend NCCL_IB_DISABLE=1)
"""
import math
import os
import time
from contextlib import nullcontext
from datetime import datetime
from functools import partial
import torch
from model import Transformer, ModelArgs
from torch.distributed import destroy_process_group, init_process_group
from torch.nn.parallel import DistributedDataParallel as DDP
from tinystories import Task
# -----------------------------------------------------------------------------
# I/O
out_dir = "out"
eval_interval = 2000
log_interval = 1
eval_iters = 100
eval_only = False # if True, script exits right after the first eval
always_save_checkpoint = False # if True, always save a checkpoint after each eval
init_from = "scratch" # 'scratch' or 'resume'
# wandb logging
wandb_log = False # disabled by default
wandb_project = "llamac"
wandb_run_name = "run" + datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
# data
batch_size = 128 # if gradient_accumulation_steps > 1, this is the micro-batch size
max_seq_len = 256
vocab_source = "llama2" # llama2|custom; use Lllama 2 vocab from Meta, or custom trained
vocab_size = 32000 # the Llama 2 tokenizer has 32K tokens
# model
dim = 288
n_layers = 6
n_heads = 6
n_kv_heads = 6
multiple_of = 32
dropout = 0.0
# adamw optimizer
gradient_accumulation_steps = 4 # used to simulate larger batch sizes
learning_rate = 5e-4 # max learning rate
max_iters = 100000 # total number of training iterations
weight_decay = 1e-1
beta1 = 0.9
beta2 = 0.95
grad_clip = 1.0 # clip gradients at this value, or disable if == 0.0
# learning rate decay settings
decay_lr = True # whether to decay the learning rate
warmup_iters = 1000 # how many steps to warm up for
# system
device = "cuda" # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1' etc., or try 'mps' on macbooks
dtype = "bfloat16" # float32|bfloat16|float16
compile = True # use PyTorch 2.0 to compile the model to be faster
# -----------------------------------------------------------------------------
config_keys = [
k
for k, v in globals().items()
if not k.startswith("_") and isinstance(v, (int, float, bool, str))
]
exec(open("configurator.py").read()) # overrides from command line or config file
config = {k: globals()[k] for k in config_keys} # will be useful for logging
# -----------------------------------------------------------------------------
# fixing some hyperparams to sensible defaults
lr_decay_iters = max_iters # should be ~= max_iters per Chinchilla
min_lr = 0.0 # minimum learning rate, should be ~= learning_rate/10 per Chinchilla
# validating checks
assert vocab_source in ["llama2", "custom"]
assert vocab_source == "custom" or vocab_size == 32000, "The vocab from Meta has 32K tokens"
# various inits, derived attributes, I/O setup
ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
if ddp:
init_process_group(backend="nccl")
ddp_rank = int(os.environ["RANK"])
ddp_local_rank = int(os.environ["LOCAL_RANK"])
ddp_world_size = int(os.environ["WORLD_SIZE"])
device = f"cuda:{ddp_local_rank}"
torch.cuda.set_device(device)
master_process = ddp_rank == 0 # this process will do logging, checkpointing etc.
seed_offset = ddp_rank # each process gets a different seed
# world_size number of processes will be training simultaneously, so we can scale
# down the desired gradient accumulation iterations per process proportionally
assert gradient_accumulation_steps % ddp_world_size == 0
gradient_accumulation_steps //= ddp_world_size
else:
# if not ddp, we are running on a single gpu, and one process
master_process = True
seed_offset = 0
ddp_world_size = 1
tokens_per_iter = gradient_accumulation_steps * ddp_world_size * batch_size * max_seq_len
if master_process:
print(f"tokens per iteration will be: {tokens_per_iter:,}")
print(f"breaks down as: {gradient_accumulation_steps} grad accum steps * {ddp_world_size} processes * {batch_size} batch size * {max_seq_len} max seq len")
if master_process:
os.makedirs(out_dir, exist_ok=True)
torch.manual_seed(1337 + seed_offset)
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
device_type = "cuda" if "cuda" in device else "cpu" # for later use in torch.autocast
# note: float16 data type will automatically use a GradScaler
ptdtype = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}[dtype]
ctx = (
nullcontext()
if device_type == "cpu"
else torch.amp.autocast(device_type=device_type, dtype=ptdtype)
)
# task-specific setup
iter_batches = partial(
Task.iter_batches,
batch_size=batch_size,
max_seq_len=max_seq_len,
vocab_size=vocab_size,
vocab_source=vocab_source,
device=device,
num_workers=0,
)
# init these up here, can override if init_from='resume' (i.e. from a checkpoint)
iter_num = 0
best_val_loss = 1e9
# model init
model_args = dict(
dim=dim,
n_layers=n_layers,
n_heads=n_heads,
n_kv_heads=n_kv_heads,
vocab_size=vocab_size,
multiple_of=multiple_of,
max_seq_len=max_seq_len,
dropout=dropout,
) # start with model_args from command line
if init_from == "scratch":
# init a new model from scratch
print("Initializing a new model from scratch")
gptconf = ModelArgs(**model_args)
model = Transformer(gptconf)
elif init_from == "resume":
print(f"Resuming training from {out_dir}")
# resume training from a checkpoint.
ckpt_path = os.path.join(out_dir, "ckpt.pt")
checkpoint = torch.load(ckpt_path, map_location=device)
checkpoint_model_args = checkpoint["model_args"]
# force these config attributes to be equal otherwise we can't even resume training
# the rest of the attributes (e.g. dropout) can stay as desired from command line
for k in ["dim", "n_layers", "n_heads", "n_kv_heads", "vocab_size", "multiple_of", "max_seq_len"]:
model_args[k] = checkpoint_model_args[k]
# create the model
gptconf = ModelArgs(**model_args)
model = Transformer(gptconf)
state_dict = checkpoint["model"]
# fix the keys of the state dictionary :(
# honestly no idea how checkpoints sometimes get this prefix, have to debug more
unwanted_prefix = "_orig_mod."
for k, v in list(state_dict.items()):
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
model.load_state_dict(state_dict)
iter_num = checkpoint["iter_num"]
best_val_loss = checkpoint["best_val_loss"]
model.to(device)
# initialize a GradScaler. If enabled=False scaler is a no-op
scaler = torch.cuda.amp.GradScaler(enabled=(dtype == "float16"))
# optimizer
optimizer = model.configure_optimizers(weight_decay, learning_rate, (beta1, beta2), device_type)
if init_from == "resume" and "optimizer" in checkpoint:
optimizer.load_state_dict(checkpoint["optimizer"])
checkpoint = None # free up memory
# compile the model
if compile:
print("compiling the model... (takes a ~minute)")
unoptimized_model = model
model = torch.compile(model) # requires PyTorch 2.0
# wrap model into DDP container
if ddp:
# Ignore the `freqs_cis` buffer so that DDP does not broadcast it at
# construction time since NCCL does not support `ComplexFloat`
prefix = "_orig_mod." if compile else ""
model._ddp_params_and_buffers_to_ignore = {prefix + "freqs_cis"}
model = DDP(model, device_ids=[ddp_local_rank])
# helps estimate an arbitrarily accurate loss over either split using many batches
@torch.no_grad()
def estimate_loss():
out = {}
model.eval()
for split in ["train", "val"]:
batch_iter = iter_batches(split=split)
losses = torch.zeros(eval_iters) # keep on CPU
for k in range(eval_iters):
X, Y = next(batch_iter)
with ctx:
logits = model(X, Y)
loss = raw_model.last_loss
losses[k] = loss.item()
out[split] = losses.mean()
model.train()
return out
# learning rate decay scheduler (cosine with warmup)
def get_lr(it):
# 1) linear warmup for warmup_iters steps
if it < warmup_iters:
return learning_rate * it / warmup_iters
# 2) if it > lr_decay_iters, return min learning rate
if it > lr_decay_iters:
return min_lr
# 3) in between, use cosine decay down to min learning rate
decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
assert 0 <= decay_ratio <= 1
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio)) # coeff ranges 0..1
return min_lr + coeff * (learning_rate - min_lr)
# logging
if wandb_log and master_process:
import wandb
wandb.init(project=wandb_project, name=wandb_run_name, config=config)
# training loop
train_batch_iter = iter_batches(split="train")
X, Y = next(train_batch_iter) # fetch the very first batch
t0 = time.time()
local_iter_num = 0 # number of iterations in the lifetime of this process
raw_model = model.module if ddp else model # unwrap DDP container if needed
running_mfu = -1.0
while True:
# determine and set the learning rate for this iteration
lr = get_lr(iter_num) if decay_lr else learning_rate
for param_group in optimizer.param_groups:
param_group["lr"] = lr
# evaluate the loss on train/val sets and write checkpoints
if iter_num % eval_interval == 0 and master_process:
losses = estimate_loss()
print(f"step {iter_num}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
if wandb_log:
try:
wandb.log(
{
"iter": iter_num,
"tokens": iter_num * tokens_per_iter,
"loss/train": losses["train"],
"loss/val": losses["val"],
"lr": lr,
"mfu": running_mfu * 100, # convert to percentage
}
)
except Exception as e:
print(f"logging to wandb failed: {e}")
if losses["val"] < best_val_loss or always_save_checkpoint:
best_val_loss = losses["val"]
if iter_num > 0:
checkpoint = {
"model": raw_model.state_dict(),
"optimizer": optimizer.state_dict(),
"model_args": model_args,
"iter_num": iter_num,
"best_val_loss": best_val_loss,
"config": config,
}
print(f"saving checkpoint to {out_dir}")
torch.save(checkpoint, os.path.join(out_dir, "ckpt.pt"))
raw_model.export(os.path.join(out_dir, "model.bin"))
if iter_num == 0 and eval_only:
break
# forward backward update, with optional gradient accumulation to simulate larger batch size
# and using the GradScaler if data type is float16
for micro_step in range(gradient_accumulation_steps):
if ddp:
# in DDP training we only need to sync gradients at the last micro step.
# the official way to do this is with model.no_sync() context manager, but
# I really dislike that this bloats the code and forces us to repeat code
# looking at the source of that context manager, it just toggles this variable
model.require_backward_grad_sync = micro_step == gradient_accumulation_steps - 1
with ctx:
logits = model(X, Y)
loss = raw_model.last_loss
loss = loss / gradient_accumulation_steps
# immediately async prefetch next batch while model is doing the forward pass on the GPU
X, Y = next(train_batch_iter)
# backward pass, with gradient scaling if training in fp16
scaler.scale(loss).backward()
# clip the gradient
if grad_clip != 0.0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
# step the optimizer and scaler if training in fp16
scaler.step(optimizer)
scaler.update()
# flush the gradients as soon as we can, no need for this memory anymore
optimizer.zero_grad(set_to_none=True)
# timing and logging
t1 = time.time()
dt = t1 - t0
t0 = t1
if iter_num % log_interval == 0 and master_process:
# get loss as float, scale up due to the divide above. note: this is a CPU-GPU sync point
lossf = loss.item() * gradient_accumulation_steps
if local_iter_num >= 5: # let the training loop settle a bit
mfu = raw_model.estimate_mfu(batch_size * gradient_accumulation_steps, dt)
running_mfu = mfu if running_mfu == -1.0 else 0.9 * running_mfu + 0.1 * mfu
print(
f"{iter_num} | loss {lossf:.4f} | lr {lr:e} | {dt*1000:.2f}ms | mfu {running_mfu*100:.2f}%"
)
iter_num += 1
local_iter_num += 1
# termination conditions
if iter_num > max_iters:
break
if ddp:
destroy_process_group()
train_vocab.sh
-126
View File
@@ -1,126 +0,0 @@
#!/bin/bash
# Trains a sentencepiece tokenizer model on a bunch of given data, my best
# effort attempt to replicate how Meta trained their Llama 2 tokenizer.
# usage: $ train_vocab.sh <input> <model_prefix> <vocab_size>
# example:
# ./train_vocab.sh tiny.txt tokenizer_tiny 1024
# requirements:
# install https://github.com/google/sentencepiece
# check if the correct number of arguments are provided
if [ $# -ne 3 ]; then
echo "Usage: $0 <input> <model_prefix> <vocab_size>"
exit 1
fi
# assign command-line arguments to variables
input=$1
model_prefix=$2
vocab_size=$3
# check if input file exists
if [ ! -f "$input" ]; then
echo "Usage: $0 <input> <model_prefix> <vocab_size>"
echo "input '$input' not found."
exit 1
fi
# check if vocab_size is a positive integer
if ! [[ "$vocab_size" =~ ^[0-9]+$ ]] || [ "$vocab_size" -lt 1 ]; then
echo "Usage: $0 <input> <model_prefix> <vocab_size>"
echo "vocab_size size must be a positive integer."
exit 1
fi
# Print the processed inputs
echo "Input: $input"
echo "Model Prefix: $model_prefix"
echo "Vocabulary Size: $vocab_size"
# train a sentencepiece tokenizer model
# Llama 2 config can be printed as follows:
# import sentencepiece.sentencepiece_model_pb2
# mp = sentencepiece.sentencepiece_model_pb2.ModelProto()
# mp.ParseFromString(open("tokenizer.model", "rb").read())
# print(mp.trainer_spec)
# print(mp.normalizer_spec)
# this gives:
# trainer_spec {
# input: "/large_experiments/theorem/datasets/MERGED/all.test1.merged"
# model_prefix: "spm_model_32k_200M_charcov099995_allowWSO__v2"
# model_type: BPE
# vocab_size: 32000
# self_test_sample_size: 0
# input_format: "text"
# character_coverage: 0.9999499917030334
# input_sentence_size: 200000000
# seed_sentencepiece_size: 1000000
# shrinking_factor: 0.75
# num_threads: 80
# num_sub_iterations: 2
# max_sentence_length: 4192
# shuffle_input_sentence: true
# max_sentencepiece_length: 16
# split_by_unicode_script: true
# split_by_whitespace: true
# split_by_number: true
# treat_whitespace_as_suffix: false
# split_digits: true
# allow_whitespace_only_pieces: true
# vocabulary_output_piece_score: true
# hard_vocab_limit: true
# use_all_vocab: false
# byte_fallback: true
# required_chars: ""
# unk_id: 0
# bos_id: 1
# eos_id: 2
# pad_id: -1
# unk_surface: " \342\201\207 "
# unk_piece: "<unk>"
# bos_piece: "<s>"
# eos_piece: "</s>"
# pad_piece: "<pad>"
# train_extremely_large_corpus: false
# enable_differential_privacy: false
# differential_privacy_noise_level: 0.0
# differential_privacy_clipping_threshold: 0
# }
# normalizer_spec {
# name: "identity"
# precompiled_charsmap: ""
# add_dummy_prefix: true
# remove_extra_whitespaces: false
# normalization_rule_tsv: ""
# }
# let's now use spm_train to train this exact model
# options docs: https://github.com/google/sentencepiece/blob/master/doc/options.md
# we'll depart on a few settings:
# character_coverage -> 1.0
# other important notes:
# --split-digits = true, per the paper
# --allow_whitespace_only_pieces is true, default in spm is false
# --byte_fallback is true, default in spm is false
# --normalization_rule_name is identity, default in spm is nmt_nfkc
spm_train --input="$input" \
--model_prefix="$model_prefix" \
--model_type=bpe \
--vocab_size="$vocab_size" \
--self_test_sample_size=0 \
--input_format="text" \
--character_coverage=1.0 \
--num_threads="$(nproc)" \
--split_digits=true \
--allow_whitespace_only_pieces=true \
--byte_fallback=true \
--unk_surface=" \342\201\207 " \
--normalization_rule_name=identity \