#!/usr/bin/env python
+# Any copyright is dedicated to the Public Domain.
+# https://creativecommons.org/publicdomain/zero/1.0/
+
+# Written by Francois Fleuret <francois@fleuret.org>
+
import math, os, tqdm
import torch, torchvision
from torch import nn
from torch.nn import functional as F
+from mygpt import BracketedSequence
+
+try:
+ from graph import save_attention_image
+except ImportError:
+ save_attention_image = None
+
######################################################################
batches,
dynamic_ncols=True,
desc=progress_bar_desc,
- # total=input.size(0) // batch_size,
+ total=(input.size(0) + batch_size - 1) // batch_size,
)
with torch.autograd.no_grad():
pass
-######################################################################
-
-
-class Problem:
- def generate(nb):
- pass
-
- def perf(seq, logger):
- pass
-
+####################
-class ProblemByheart(Problem):
- def __init__(self):
- nb_seq, len_prompt, len_result = 100, 5, 5
- self.seq = torch.randint(10, (nb_seq, len_prompt + 1 + len_result))
- self.seq[:, len_prompt] = -1
-
- def generate_sequences(self, nb):
- return self.seq[torch.randint(self.seq.size(0), (nb,))]
+import problems
class SandBox(Task):
def __init__(
self,
+ problem,
nb_train_samples,
nb_test_samples,
batch_size,
logger=None,
device=torch.device("cpu"),
+ max_nb_codes=1024,
):
super().__init__()
self.batch_size = batch_size
+ self.device = device
+ self.problem = problem
- problems = [ProblemByheart()]
- nb_common_codes = 100
-
- def generate_sequences(nb_samples):
- problem_indexes = torch.randint(len(problems), (nb_samples,))
- nb_samples_per_problem = torch.one_hot(problem_indexes).sum(0)
- print(f"{nb_samples_per_problem}")
- all_seq = []
- for nb, p in zip(nb_samples_per_problem, problems):
- all_seq.append(p.generate_sequences(nb_samples_per_problem[nb]))
- return all_seq
-
- train_seq = generate_sequences(nb_train_samples)
- test_seq = generate_sequences(nb_test_samples)
+ self.train_input, self.train_ar_mask = self.problem.generate_sequences(
+ nb_train_samples
+ )
+ self.test_input, self.test_ar_mask = self.problem.generate_sequences(
+ nb_test_samples
+ )
- for strain, stest in zip(train_seq, test_seq):
- s = torch.cat((strain, stest), 0)
+ self.train_input, self.train_ar_mask = self.train_input.to(
+ device
+ ), self.train_ar_mask.to(device)
+ self.test_input, self.test_ar_mask = self.test_input.to(
+ device
+ ), self.test_ar_mask.to(device)
self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+ # A bit of paranoia never hurts
+ assert (
+ self.nb_codes <= max_nb_codes
+ and self.train_input.min() >= 0
+ and self.test_input.min() >= 0
+ and tuple(self.train_ar_mask.unique()) == (0, 1)
+ and tuple(self.test_ar_mask.unique()) == (0, 1)
+ )
+
def batches(self, split="train", nb_to_use=-1, desc=None):
assert split in {"train", "test"}
input = self.train_input if split == "train" else self.test_input
return self.nb_codes
def produce_results(
- self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis, nmax=1000
):
- # logger(
- # f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
- # )
- pass
+ def compute_accuracy(input, ar_mask, logger=None):
+ input, ar_mask = input[:nmax], ar_mask[:nmax]
+ result = input.clone() * (1 - ar_mask)
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ progress_bar_desc=None,
+ device=self.device,
+ )
+
+ if logger is not None:
+ for sp, st in zip(result[:10], input[:10]):
+ logger(
+ f"test_sequences {n_epoch} prediction {self.problem.seq2str(sp)}"
+ )
+ logger(
+ f" {n_epoch} ground truth {self.problem.seq2str(st)}"
+ )
+
+ nb_total = ar_mask.sum().item()
+ nb_correct = ((result == input).long() * ar_mask).sum().item()
+
+ return nb_total, nb_correct
+
+ train_nb_total, train_nb_correct = compute_accuracy(
+ self.train_input, self.train_ar_mask
+ )
+
+ logger(
+ f"accuracy_train {n_epoch} nb_total {train_nb_total} nb_correct {train_nb_correct} accuracy {(100.0*train_nb_correct)/train_nb_total:.02f}%"
+ )
+
+ test_nb_total, test_nb_correct = compute_accuracy(
+ self.test_input, self.test_ar_mask, logger
+ )
+
+ logger(
+ f"accuracy_test {n_epoch} nb_total {test_nb_total} nb_correct {test_nb_correct} accuracy {(100.0*test_nb_correct)/test_nb_total:.02f}%"
+ )
+
+ if save_attention_image is not None:
+ for k in range(10):
+ ns = torch.randint(self.test_input.size(0), (1,)).item()
+ input = self.test_input[ns : ns + 1].clone()
+
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+ model.record_attention(True)
+ model(BracketedSequence(input))
+ model.train(t)
+ ram = model.retrieve_attention()
+ model.record_attention(False)
+
+ tokens_output = [c for c in self.problem.seq2str(input[0])]
+ tokens_input = ["n/a"] + tokens_output[:-1]
+ for n_head in range(ram[0].size(1)):
+ filename = os.path.join(
+ result_dir, f"sandbox_attention_{k}_h{n_head}.pdf"
+ )
+ attention_matrices = [m[0, n_head] for m in ram]
+ save_attention_image(
+ filename,
+ tokens_input,
+ tokens_output,
+ attention_matrices,
+ k_top=10,
+ # min_total_attention=0.9,
+ token_gap=12,
+ layer_gap=50,
+ )
+ logger(f"wrote {filename}")
######################################################################
##############################################################
+######################################################################
+
+import rpl
+
+
+class RPL(Task):
+ def tensorize(self, sequences):
+ len_max = max([len(x) for x in sequences])
+ return torch.cat(
+ [
+ torch.tensor(
+ [
+ [
+ self.token2id[str(c)]
+ for c in s + ["<nul>"] * (len_max - len(s))
+ ]
+ for s in sequences
+ ]
+ )
+ ],
+ 0,
+ )
+
+ def seq2str(self, seq):
+ return " ".join([self.id2token[i] for i in seq])
+
+ def __init__(
+ self,
+ nb_train_samples,
+ nb_test_samples,
+ batch_size,
+ nb_starting_values=3,
+ max_input=9,
+ prog_len=6,
+ nb_runs=5,
+ no_prog=False,
+ logger=None,
+ device=torch.device("cpu"),
+ ):
+ super().__init__()
+
+ self.batch_size = batch_size
+ self.device = device
+ self.no_prog = no_prog
+
+ train_sequences = [
+ rpl.generate(
+ nb_starting_values=nb_starting_values,
+ nb_result_values_max=4 * nb_starting_values,
+ max_input=max_input,
+ prog_len=prog_len,
+ nb_runs=nb_runs,
+ )
+ for _ in tqdm.tqdm(range(nb_train_samples), desc="train-data")
+ ]
+
+ test_sequences = [
+ rpl.generate(
+ nb_starting_values=nb_starting_values,
+ nb_result_values_max=4 * nb_starting_values,
+ max_input=max_input,
+ prog_len=prog_len,
+ nb_runs=nb_runs,
+ )
+ for _ in tqdm.tqdm(range(nb_test_samples), desc="test-data")
+ ]
+
+ symbols = list(
+ set(["<nul>"] + [x for l in train_sequences + test_sequences for x in l])
+ )
+ val_max = max([x if type(x) is int else 0 for x in symbols])
+ symbols = list(filter(lambda x: type(x) is str, symbols))
+ symbols.sort()
+ symbols += [str(n) for n in range(val_max + 1)]
+ self.token2id = dict([(c, n) for n, c in enumerate(symbols)])
+ self.id2token = dict([(n, c) for c, n in self.token2id.items()])
+
+ self.t_nul = self.token2id["<nul>"]
+ self.t_input = self.token2id["<in>"]
+ self.t_output = self.token2id["<out>"]
+ self.t_prog = self.token2id["<prg>"]
+ self.t_end = self.token2id["<end>"]
+
+ self.train_input = self.tensorize(train_sequences)
+ self.test_input = self.tensorize(test_sequences)
+
+ if no_prog:
+ # Excise the program from every train and test example
+ k = torch.arange(self.train_input.size(1), device=self.train_input.device)[
+ None, :
+ ]
+ p = (
+ ((self.train_input == self.t_prog).long() * k)
+ .max(1, keepdim=True)
+ .values
+ )
+ self.train_input = (
+ self.train_input * (k <= p).long()
+ + self.t_end * (k == p + 1).long()
+ + self.t_nul * (k > p + 1).long()
+ )
+ k = torch.arange(self.test_input.size(1), device=self.test_input.device)[
+ None, :
+ ]
+ p = (
+ ((self.test_input == self.t_prog).long() * k)
+ .max(1, keepdim=True)
+ .values
+ )
+ self.test_input = (
+ self.test_input * (k <= p).long()
+ + self.t_end * (k == p + 1).long()
+ + self.t_nul * (k > p + 1).long()
+ )
+
+ if logger is not None:
+ logger(f"value_max {val_max}")
+ for x in self.train_input[:25]:
+ end = (x != self.t_nul).nonzero().max().item() + 1
+ seq = [self.id2token[i.item()] for i in x[:end]]
+ s = " ".join(seq)
+ logger(f"example_seq {s}")
+
+ self.nb_codes = max(self.train_input.max(), self.test_input.max()) + 1
+
+ def batches(self, split="train", nb_to_use=-1, desc=None):
+ assert split in {"train", "test"}
+ input = self.train_input if split == "train" else self.test_input
+ if nb_to_use > 0:
+ input = input[:nb_to_use]
+ if desc is None:
+ desc = f"epoch-{split}"
+ for batch in tqdm.tqdm(
+ input.split(self.batch_size), dynamic_ncols=True, desc=desc
+ ):
+ last = (batch != self.t_nul).max(0).values.nonzero().max() + 3
+ batch = batch[:, :last].to(self.device)
+ yield batch
+
+ def vocabulary_size(self):
+ return self.nb_codes
+
+ def produce_results(
+ self, n_epoch, model, result_dir, logger, deterministic_synthesis
+ ):
+ # --------------------------------------------------------------------
+ def compute_nb_errors_prog(input, nb_to_log=0):
+ result = input.clone()
+ s = (result == self.t_prog).long()
+ ar_mask = (s.cumsum(dim=1) - s).clamp(min=0, max=1)
+ result = (1 - ar_mask) * result + ar_mask * self.t_nul
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ sum_nb_total, sum_nb_errors = 0, 0
+ for one_input, one_result in zip(input, result):
+ seq = [self.id2token[i.item()] for i in one_result]
+ nb_total, nb_errors, prog, stacks = rpl.compute_nb_errors(seq)
+ sum_nb_total += 1
+ sum_nb_errors += 0 if nb_errors == 0 else 1
+ if nb_to_log > 0:
+ gt_seq = [self.id2token[i.item()] for i in one_input]
+ _, _, gt_prog, _ = rpl.compute_nb_errors(gt_seq)
+ gt_prog = " ".join([str(x) for x in gt_prog])
+ prog = " ".join([str(x) for x in prog])
+ comment = "*" if nb_errors == 0 else "-"
+ logger(f"{comment} PROG [{gt_prog}] PREDICTED [{prog}]")
+ for start_stack, target_stack, result_stack, correct in stacks:
+ comment = "*" if correct else "-"
+ start_stack = " ".join([str(x) for x in start_stack])
+ target_stack = " ".join([str(x) for x in target_stack])
+ result_stack = " ".join([str(x) for x in result_stack])
+ logger(
+ f" {comment} [{start_stack}] -> [{target_stack}] PREDICTED [{result_stack}]"
+ )
+ nb_to_log -= 1
+
+ return sum_nb_total, sum_nb_errors
+
+ # --------------------------------------------------------------------
+ def compute_nb_errors_output(input, nb_to_log=0):
+ result = input.clone()
+ k = torch.arange(result.size(1), device=result.device)[None, :]
+ last_output_idx = (
+ ((result == self.t_output) * k).max(dim=1, keepdim=True).values
+ )
+ first_prog_idx = (
+ ((result == self.t_prog) * k).max(dim=1, keepdim=True).values
+ )
+ ar_mask = (k > last_output_idx).long() * (k < first_prog_idx).long()
+ result = (1 - ar_mask) * result + ar_mask * self.t_nul
+
+ masked_inplace_autoregression(
+ model,
+ self.batch_size,
+ result,
+ ar_mask,
+ deterministic_synthesis,
+ device=self.device,
+ )
+
+ sum_nb_total, sum_nb_errors = 0, 0
+ for one_input, one_result, i, j in zip(
+ input, result, last_output_idx, first_prog_idx
+ ):
+ seq = [self.id2token[i.item()] for i in one_result]
+ sum_nb_total += 1
+ correct = (one_input - one_result).abs().max() == 0
+ sum_nb_errors += 0 if correct else 1
+ if nb_to_log > 0:
+ result_stack = [
+ self.id2token[i.item()] for i in one_result[i : j + 1]
+ ]
+ target_stack = [
+ self.id2token[i.item()] for i in one_input[i : j + 1]
+ ]
+ comment = "*" if correct else "-"
+ result_stack = " ".join([str(x) for x in result_stack])
+ target_stack = " ".join([str(x) for x in target_stack])
+ logger(
+ f"output_test {comment} [{target_stack}] PREDICTED [{result_stack}]"
+ )
+ nb_to_log -= 1
+
+ return sum_nb_total, sum_nb_errors
+
+ # --------------------------------------------------------------------
+
+ if not self.no_prog:
+ test_nb_total, test_nb_errors = compute_nb_errors_prog(
+ self.test_input[:1000].to(self.device), nb_to_log=10
+ )
+
+ logger(
+ f"accuracy_prog_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
+ )
+
+ test_nb_total, test_nb_errors = compute_nb_errors_output(
+ self.test_input[:1000].to(self.device), nb_to_log=10
+ )
+
+ logger(
+ f"accuracy_output_test {n_epoch} nb_total {test_nb_total} nb_errors {test_nb_errors} accuracy {100.0*(1-test_nb_errors/test_nb_total):.02f}%"
+ )
+
+ if save_attention_image is not None:
+ ns = torch.randint(self.test_input.size(0), (1,)).item()
+ input = self.test_input[ns : ns + 1].clone()
+ last = (input != self.t_nul).max(0).values.nonzero().max() + 3
+ input = input[:, :last].to(self.device)
+
+ with torch.autograd.no_grad():
+ t = model.training
+ model.eval()
+ model.record_attention(True)
+ model(BracketedSequence(input))
+ model.train(t)
+ ram = model.retrieve_attention()
+ model.record_attention(False)
+
+ tokens_output = [self.id2token[i.item()] for i in input[0]]
+ tokens_input = ["n/a"] + tokens_output[:-1]
+ for n_head in range(ram[0].size(1)):
+ filename = os.path.join(
+ result_dir, f"rpl_attention_{n_epoch}_h{n_head}.pdf"
+ )
+ attention_matrices = [m[0, n_head] for m in ram]
+ save_attention_image(
+ filename,
+ tokens_input,
+ tokens_output,
+ attention_matrices,
+ k_top=10,
+ # min_total_attention=0.9,
+ token_gap=12,
+ layer_gap=50,
+ )
+ logger(f"wrote {filename}")
+
+
######################################################################
device_storage=device_storage,
)
- print(f"{train_action_seq.size()=}")
-
train_frame_seq = self.frame2seq(train_frames).to(device_storage)
test_frame_seq = self.frame2seq(test_frames).to(device_storage)
self.nb_codes = nb_frame_codes + nb_action_codes
train_frame_seq = train_frame_seq.reshape(train_frame_seq.size(0) // 2, 2, -1)
- print(f"{train_action_seq.device=} {nb_frame_codes.device=}")
+
train_action_seq += nb_frame_codes
self.train_input = torch.cat(
(train_frame_seq[:, 0, :], train_action_seq, train_frame_seq[:, 1, :]), 1
(seq_start[:, None, :], seq_end[:, None, :], seq_predicted[:, None, :]), 1
)
result = result.reshape(-1, result.size(-1))
- print(f"{result.size()=}")
frames = self.seq2frame(result)
image_name = os.path.join(result_dir, f"world_result_{n_epoch:04d}.png")