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@@ -25,11 +25,11 @@ source .venv/bin/activate
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For instructions installing pytorch refer to the [PyTorch Home page]
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```bash
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pip3 install numpy
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# use the nvidia CUDA or CPU only packages if required.
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# I use the ROCm packages, so the repo uses the ROCm packages.
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pip3 install numpy pandas matplotlib
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# I use the ROCm packages
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pip3 install torch torchvision --index-url https://download.pytorch.org/whl/rocm6.4
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pip3 install pandas matplotlib
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# if you need the CPU only package
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# pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
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```
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## Running the code
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286
output_graphs.py
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output_graphs.py
@@ -1,286 +0,0 @@
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import re
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import pandas as pd
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import matplotlib.pyplot as plt
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|
||||
INFO:__main__:step=24600 loss=0.0000105 acc=1.0000000
|
||||
INFO:__main__:step=24700 loss=0.0000001 acc=1.0000000
|
||||
INFO:__main__:step=24800 loss=0.0000193 acc=1.0000000
|
||||
INFO:__main__:step=24900 loss=0.0000280 acc=1.0000000
|
||||
"""
|
||||
|
||||
pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
|
||||
rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
|
||||
df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)
|
||||
|
||||
# Avoid log(0) issues for loss plot by clamping at a tiny positive value
|
||||
eps = 1e-10
|
||||
df["loss_clamped"] = df["loss"].clip(lower=eps)
|
||||
|
||||
# Plot 1: Loss
|
||||
plt.figure(figsize=(9, 4.8))
|
||||
plt.plot(df["step"], df["loss_clamped"])
|
||||
plt.yscale("log")
|
||||
plt.xlabel("Step")
|
||||
plt.ylabel("Loss (log scale)")
|
||||
plt.title("Training Loss vs Step")
|
||||
plt.tight_layout()
|
||||
plt.savefig('./files/training_loss_v_step.png')
|
||||
plt.show()
|
||||
|
||||
# Plot 2: Accuracy
|
||||
df["err"] = (1.0 - df["acc"]).clip(lower=eps)
|
||||
plt.figure(figsize=(9, 4.8))
|
||||
plt.plot(df["step"], df["err"])
|
||||
plt.yscale("log")
|
||||
plt.xlabel("Step")
|
||||
plt.ylabel("Error rate (1 - accuracy) (log scale)")
|
||||
plt.title("Training Error Rate vs Step")
|
||||
plt.tight_layout()
|
||||
plt.savefig('./files/training_error_v_step.png')
|
||||
plt.show()
|
||||
@@ -3,32 +3,35 @@ from torch import nn
|
||||
|
||||
# 2) Number "embedding" network: R -> R^d
|
||||
class NumberEmbedder(nn.Module):
|
||||
def __init__(self, d=8):
|
||||
def __init__(self, d=2, hidden=4):
|
||||
super().__init__()
|
||||
self.net = nn.Sequential(
|
||||
nn.Linear(1, 16),
|
||||
nn.Linear(1, hidden),
|
||||
nn.ReLU(),
|
||||
nn.Linear(16, d),
|
||||
nn.Linear(hidden, d),
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
return self.net(x)
|
||||
|
||||
# 3) Comparator head: takes (ea, eb) -> logit for "a > b"
|
||||
# MLP Comparator head: takes (ea, eb, e) -> logit for "a > b"
|
||||
class PairwiseComparator(nn.Module):
|
||||
def __init__(self, d=8):
|
||||
def __init__(self, d=2, hidden=4, k=0.5):
|
||||
super().__init__()
|
||||
self.embed = NumberEmbedder(d)
|
||||
self.log_k = nn.Parameter(torch.tensor([k]))
|
||||
self.embed = NumberEmbedder(d, hidden)
|
||||
self.head = nn.Sequential(
|
||||
nn.Linear(2 * d + 1, 16),
|
||||
nn.Linear(d, hidden),
|
||||
nn.ReLU(),
|
||||
nn.Linear(16, 1),
|
||||
nn.Linear(hidden, hidden),
|
||||
nn.ReLU(),
|
||||
nn.Linear(hidden, 1),
|
||||
)
|
||||
|
||||
def forward(self, a, b):
|
||||
ea = self.embed(a)
|
||||
eb = self.embed(b)
|
||||
delta_ab = a - b
|
||||
x = torch.cat([ea, eb, delta_ab], dim=-1)
|
||||
# trying to force antisym here: h(a,b)=-h(b,a)
|
||||
phi = self.head(self.embed(a-b))
|
||||
phi_neg = self.head(self.embed(b-a))
|
||||
logit = phi - phi_neg
|
||||
|
||||
return self.head(x) # logits
|
||||
return (self.log_k ** 2) * logit
|
||||
@@ -2,27 +2,126 @@
|
||||
# pairwise_compare.py
|
||||
import logging
|
||||
import random
|
||||
import lzma
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import re
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import pairwise_comp_nn as comp_nn
|
||||
|
||||
# early pytorch device setup
|
||||
DEVICE = torch.accelerator.current_accelerator() if torch.accelerator.is_available() else "cpu"
|
||||
|
||||
# Valves
|
||||
DIMENSIONS = 1
|
||||
TRAIN_STEPS = 25000
|
||||
TRAIN_BATCHSZ = 16384
|
||||
TRAIN_PROGRESS = 100
|
||||
BATCH_LOWER = -512.0
|
||||
BATCH_UPPER = 512.0
|
||||
DO_VERBOSE_EARLY_TRAIN = False
|
||||
DIMENSIONS = 2
|
||||
HIDDEN_NEURONS = 4
|
||||
ADAMW_LR = 5e-3
|
||||
ADAMW_DECAY = 5e-4
|
||||
TRAIN_STEPS = 2000
|
||||
TRAIN_BATCHSZ = 8192
|
||||
TRAIN_PROGRESS = 10
|
||||
BATCH_LOWER = -100.0
|
||||
BATCH_UPPER = 100.0
|
||||
|
||||
# Files
|
||||
MODEL_PATH = "./files/pwcomp.model"
|
||||
LOGGING_PATH = "./files/output.log"
|
||||
EMBED_CHART_PATH = "./files/embedding_chart.png"
|
||||
EMBEDDINGS_DATA_PATH = "./files/embedding_data.csv"
|
||||
TRAINING_LOG_PATH = "./files/training.log.xz"
|
||||
LOSS_CHART_PATH = "./files/training_loss_v_step.png"
|
||||
ACC_CHART_PATH = "./files/training_error_v_step.png"
|
||||
|
||||
def get_torch_info():
|
||||
# TODO: Move plotting into its own file
|
||||
def parse_training_log(file_path: str) -> pd.DataFrame:
|
||||
text: str = ""
|
||||
with lzma.open(file_path, mode='rt') as f:
|
||||
text = f.read()
|
||||
|
||||
pattern = re.compile(r"step=\s*(\d+)\s+loss=([0-9.]+)\s+acc=([0-9.]+)")
|
||||
rows = [(int(s), float(l), float(a)) for s, l, a in pattern.findall(text)]
|
||||
df = pd.DataFrame(rows, columns=["step", "loss", "acc"]).sort_values("step").reset_index(drop=True)
|
||||
|
||||
# Avoid log(0) issues for loss plot by clamping at a tiny positive value
|
||||
eps = 1e-10
|
||||
df["loss_clamped"] = df["loss"].clip(lower=eps)
|
||||
|
||||
return df
|
||||
|
||||
# TODO: Move plotting into its own file
|
||||
def plt_loss_tstep(df: pd.DataFrame) -> None:
|
||||
# Plot 1: Loss
|
||||
plt.figure(figsize=(10, 6))
|
||||
plt.plot(df["step"], df["loss_clamped"])
|
||||
plt.yscale("log")
|
||||
plt.xlabel("Step")
|
||||
plt.ylabel("Loss (log scale)")
|
||||
plt.title("Training Loss vs Step")
|
||||
plt.tight_layout()
|
||||
plt.savefig(LOSS_CHART_PATH)
|
||||
plt.close()
|
||||
|
||||
return None
|
||||
|
||||
# TODO: Move plotting into its own file
|
||||
def plt_acc_tstep(df: pd.DataFrame, eps=1e-10) -> None:
|
||||
# Plot 2: Accuracy
|
||||
df["err"] = (1.0 - df["acc"]).clip(lower=eps)
|
||||
plt.figure(figsize=(10, 6))
|
||||
plt.plot(df["step"], df["err"])
|
||||
plt.yscale("log")
|
||||
plt.xlabel("Step")
|
||||
plt.ylabel("Error rate (1 - accuracy) (log scale)")
|
||||
plt.title("Training Error Rate vs Step")
|
||||
plt.tight_layout()
|
||||
plt.savefig(ACC_CHART_PATH)
|
||||
plt.close()
|
||||
|
||||
return None
|
||||
|
||||
# TODO: Move plotting into its own file
|
||||
def plt_embeddings(model: comp_nn.PairwiseComparator) -> None:
|
||||
import csv
|
||||
|
||||
log.info("Starting embeddings sweep...")
|
||||
# samples for embedding mapping
|
||||
with torch.no_grad():
|
||||
xs = torch.arange(
|
||||
BATCH_LOWER,
|
||||
BATCH_UPPER + 1.0,
|
||||
0.1,
|
||||
).unsqueeze(1).to(DEVICE) # shape: (N, 1)
|
||||
|
||||
embeddings = model.embed(xs) # shape: (N, d)
|
||||
|
||||
# move data back to CPU for plotting
|
||||
embeddings = embeddings.cpu()
|
||||
xs = xs.cpu()
|
||||
|
||||
# Plot 3: x vs h(x)
|
||||
plt.figure(figsize=(10, 6))
|
||||
for i in range(embeddings.shape[1]):
|
||||
plt.plot(xs.squeeze(), embeddings[:, i], label=f"dim {i}")
|
||||
plt.title("x vs h(x)")
|
||||
plt.xlabel("x [input]")
|
||||
plt.ylabel("h(x) [embedding]")
|
||||
plt.legend()
|
||||
plt.savefig(EMBED_CHART_PATH)
|
||||
plt.close()
|
||||
|
||||
# save all our embeddings data to csv
|
||||
csv_data = list(zip(xs.squeeze().tolist(), embeddings.tolist()))
|
||||
with open(file=EMBEDDINGS_DATA_PATH, mode="w", newline='') as f:
|
||||
csv_file = csv.writer(f)
|
||||
csv_file.writerows(csv_data)
|
||||
|
||||
return None
|
||||
|
||||
def get_torch_info() -> None:
|
||||
log.info("PyTorch Version: %s", torch.__version__)
|
||||
log.info("HIP Version: %s", torch.version.hip)
|
||||
log.info("CUDA support: %s", torch.cuda.is_available())
|
||||
@@ -32,60 +131,69 @@ def get_torch_info():
|
||||
|
||||
log.info("Using %s compute mode", DEVICE)
|
||||
|
||||
def set_seed(seed: int):
|
||||
def set_seed(seed: int) -> None:
|
||||
random.seed(seed)
|
||||
torch.manual_seed(seed)
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
|
||||
# 1) Data: pairs (a, b) with label y = 1 if a > b else 0
|
||||
def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER):
|
||||
# pairs (a, b) with label y = 1 if a > b else 0 -> (a,b,y)
|
||||
# uses epsi to select the window in which a == b for equality training
|
||||
def sample_batch(batch_size: int, low=BATCH_LOWER, high=BATCH_UPPER, epsi=1e-4) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
||||
a = (high - low) * torch.rand(batch_size, 1) + low
|
||||
b = (high - low) * torch.rand(batch_size, 1) + low
|
||||
|
||||
# train for if a > b
|
||||
y = (a > b).float()
|
||||
epsi = 1e-4
|
||||
y = torch.where(a > b + epsi, 1.0,
|
||||
torch.where(a < b - epsi, 0.0, 0.5))
|
||||
|
||||
# removed but left for my notes; it seems training for equality hurts classifing results that are ~eq
|
||||
# when trained only on "if a > b => y", the model produces more accurate results when classifing if things are equal (~.5 prob).
|
||||
# eq = (a == b).float()
|
||||
# y = gt + 0.5 * eq
|
||||
return a, b, y
|
||||
|
||||
def training_entry():
|
||||
get_torch_info()
|
||||
|
||||
# all prng seeds to 0 for deterministic outputs durring testing
|
||||
# the seed should initialized normally otherwise
|
||||
set_seed(0)
|
||||
|
||||
model = comp_nn.PairwiseComparator(d=DIMENSIONS).to(DEVICE)
|
||||
opt = torch.optim.AdamW(model.parameters(), lr=9e-4, weight_decay=1e-3)
|
||||
model = comp_nn.PairwiseComparator(d=DIMENSIONS, hidden=HIDDEN_NEURONS).to(DEVICE)
|
||||
opt = torch.optim.AdamW(model.parameters(), lr=ADAMW_LR, weight_decay=ADAMW_DECAY)
|
||||
|
||||
# 4) Train
|
||||
for step in range(TRAIN_STEPS):
|
||||
a, b, y = sample_batch(TRAIN_BATCHSZ)
|
||||
a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
|
||||
log.info(f"Using {TRAINING_LOG_PATH} as the logging destination for training...")
|
||||
with lzma.open(TRAINING_LOG_PATH, mode='wt') as tlog:
|
||||
# training loop
|
||||
training_start_time = datetime.datetime.now()
|
||||
last_ack = datetime.datetime.now()
|
||||
|
||||
logits = model(a, b)
|
||||
loss_fn = F.binary_cross_entropy_with_logits(logits, y)
|
||||
for step in range(TRAIN_STEPS):
|
||||
a, b, y = sample_batch(TRAIN_BATCHSZ)
|
||||
a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
|
||||
|
||||
opt.zero_grad()
|
||||
loss_fn.backward()
|
||||
opt.step()
|
||||
logits = model(a, b)
|
||||
loss_fn = F.binary_cross_entropy_with_logits(logits, y)
|
||||
|
||||
opt.zero_grad()
|
||||
loss_fn.backward()
|
||||
opt.step()
|
||||
|
||||
if step <= TRAIN_PROGRESS and DO_VERBOSE_EARLY_TRAIN is True:
|
||||
with torch.no_grad():
|
||||
pred = (torch.sigmoid(logits) > 0.5).float()
|
||||
acc = (pred == y).float().mean().item()
|
||||
log.info(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}")
|
||||
elif step % TRAIN_PROGRESS == 0:
|
||||
with torch.no_grad():
|
||||
pred = (torch.sigmoid(logits) > 0.5).float()
|
||||
acc = (pred == y).float().mean().item()
|
||||
log.info(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}")
|
||||
tlog.write(f"step={step:5d} loss={loss_fn.item():.7f} acc={acc:.7f}\n")
|
||||
|
||||
# 5) Quick test: evaluate final model accuracy on fresh pairs
|
||||
# also print to normal text log occasionally to show some activity.
|
||||
# every 10 steps check if its been longer than 5 seconds since we've updated the user
|
||||
if step % 10 == 0:
|
||||
if (datetime.datetime.now() - last_ack).total_seconds() > 5:
|
||||
log.info(f"still training... step={step} of {TRAIN_STEPS}")
|
||||
last_ack = datetime.datetime.now()
|
||||
|
||||
training_end_time = datetime.datetime.now()
|
||||
log.info(f"Training steps complete. Start time: {training_start_time} End time: {training_end_time}")
|
||||
|
||||
# evaluate final model accuracy on fresh pairs
|
||||
with torch.no_grad():
|
||||
a, b, y = sample_batch(TRAIN_BATCHSZ)
|
||||
a, b, y = sample_batch(TRAIN_BATCHSZ*4)
|
||||
a, b, y = a.to(DEVICE), b.to(DEVICE), y.to(DEVICE)
|
||||
logits = model(a, b)
|
||||
pred = (torch.sigmoid(logits) > 0.5).float()
|
||||
@@ -94,12 +202,14 @@ def training_entry():
|
||||
log.info(f"Final test acc: {acc} errors: {errors}")
|
||||
|
||||
# embed model dimensions into the model serialization
|
||||
torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS}, MODEL_PATH)
|
||||
log.info("Saved PyTorch Model State to model.pth")
|
||||
torch.save({"state_dict": model.state_dict(), "d": DIMENSIONS, "h": HIDDEN_NEURONS}, MODEL_PATH)
|
||||
log.info(f"Saved PyTorch Model State to {MODEL_PATH}")
|
||||
|
||||
def infer_entry():
|
||||
get_torch_info()
|
||||
|
||||
model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
|
||||
model = comp_nn.PairwiseComparator(d=model_ckpt["d"]).to(DEVICE)
|
||||
model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
|
||||
model.load_state_dict(model_ckpt["state_dict"])
|
||||
model.eval()
|
||||
|
||||
@@ -115,42 +225,99 @@ def infer_entry():
|
||||
with torch.no_grad():
|
||||
probs = torch.sigmoid(model(a, b))
|
||||
|
||||
log.info(f"Output probabilities for {pairs.__len__()} pairs")
|
||||
for (x, y), p in zip(pairs, probs):
|
||||
log.info(f"P({x} > {y}) = {p.item():.3f}")
|
||||
|
||||
|
||||
|
||||
def graphs_entry():
|
||||
get_torch_info()
|
||||
|
||||
model_ckpt = torch.load(MODEL_PATH, map_location=DEVICE)
|
||||
model = comp_nn.PairwiseComparator(d=model_ckpt["d"], hidden=model_ckpt["h"]).to(DEVICE)
|
||||
model.load_state_dict(model_ckpt["state_dict"])
|
||||
model.eval()
|
||||
|
||||
plt_embeddings(model)
|
||||
|
||||
data = parse_training_log(TRAINING_LOG_PATH)
|
||||
plt_loss_tstep(data)
|
||||
plt_acc_tstep(data)
|
||||
|
||||
help_text = r"""
|
||||
pairwise_compare.py — tiny pairwise "a > b?" neural comparator
|
||||
|
||||
USAGE
|
||||
python3 pairwise_compare.py train
|
||||
Train a PairwiseComparator on synthetic (a,b) pairs sampled uniformly from
|
||||
[BATCH_LOWER, BATCH_UPPER]. Labels are:
|
||||
1.0 if a > b + epsi
|
||||
0.0 if a < b - epsi
|
||||
0.5 otherwise (near-equality window)
|
||||
Writes training metrics to:
|
||||
./files/training.log.xz
|
||||
Saves the trained model checkpoint to:
|
||||
./files/pwcomp.model
|
||||
|
||||
python3 pairwise_compare.py infer
|
||||
Load ./files/pwcomp.model and run inference on a built-in list of test pairs.
|
||||
Prints probabilities as:
|
||||
P(a > b) = sigmoid(model(a,b))
|
||||
|
||||
python3 pairwise_compare.py graphs
|
||||
Load ./files/pwcomp.model and generate plots + exports:
|
||||
./files/embedding_chart.png (embed(x) vs x for each embedding dimension)
|
||||
./files/embedding_data.csv (x and embedding vectors)
|
||||
./files/training_loss_v_step.png
|
||||
./files/training_error_v_step.png (1 - acc, log scale)
|
||||
Requires that ./files/training.log.xz exists (i.e., you ran "train" first).
|
||||
|
||||
FILES
|
||||
./files/output.log General runtime log (info/errors)
|
||||
./files/pwcomp.model Torch checkpoint: {"state_dict": ..., "d": DIMENSIONS}
|
||||
./files/training.log.xz step/loss/acc trace used for plots
|
||||
|
||||
NOTES
|
||||
- DEVICE is chosen via torch.accelerator if available, else CPU.
|
||||
- Hyperparameters are controlled by the "Valves" constants near the top.
|
||||
"""
|
||||
|
||||
if __name__ == '__main__':
|
||||
import sys
|
||||
import os
|
||||
import datetime
|
||||
|
||||
# TODO: tidy up the paths to files and checking if the directory exists
|
||||
if not os.path.exists("./files/"):
|
||||
os.mkdir("./files")
|
||||
|
||||
logging.basicConfig(level=logging.INFO,
|
||||
format='%(asctime)s - %(levelname)s - %(message)s',
|
||||
handlers=[
|
||||
logging.FileHandler(LOGGING_PATH),
|
||||
logging.StreamHandler(stream=sys.stdout)
|
||||
])
|
||||
|
||||
log = logging.getLogger(__name__)
|
||||
logging.basicConfig(filename=LOGGING_PATH, level=logging.INFO)
|
||||
log.info(f"Log file {LOGGING_PATH} opened {datetime.datetime.now()}")
|
||||
|
||||
log.info(f"Log opened {datetime.datetime.now()}")
|
||||
get_torch_info()
|
||||
|
||||
name = os.path.basename(sys.argv[0])
|
||||
if name == 'train.py':
|
||||
training_entry()
|
||||
elif name == 'infer.py':
|
||||
infer_entry()
|
||||
else:
|
||||
# alt call patern
|
||||
# python3 pairwise_compare.py train
|
||||
# python3 pairwise_compare.py infer
|
||||
if len(sys.argv) > 1:
|
||||
mode = sys.argv[1].strip().lower()
|
||||
if mode == "train":
|
||||
# python3 pairwise_compare.py train
|
||||
# python3 pairwise_compare.py infer
|
||||
# python3 pairwise_compare.py graphs
|
||||
if len(sys.argv) > 1:
|
||||
match sys.argv[1].strip().lower():
|
||||
case "train":
|
||||
training_entry()
|
||||
elif mode == "infer":
|
||||
case "infer":
|
||||
infer_entry()
|
||||
else:
|
||||
case "graphs":
|
||||
graphs_entry()
|
||||
case "help":
|
||||
log.info(help_text)
|
||||
case mode:
|
||||
log.error(f"Unknown operation: {mode}")
|
||||
log.error("Invalid call syntax, call script as \"train.py\" or \"infer.py\" or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")
|
||||
else:
|
||||
log.error("Not enough arguments passed to script; call as train.py or infer.py or as pairwise_compare.py <mode> where mode is \"train\" or \"infer\"")
|
||||
log.error("valid options are one of [\"train\", \"infer\", \"graphs\", \"help\"]")
|
||||
log.info(help_text)
|
||||
|
||||
log.info(f"Log closed {datetime.datetime.now()}")
|
||||
Reference in New Issue
Block a user