X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=stack.py;h=ba452aab0c600230d0e60b95da18e60cfee00c07;hb=c5daf2eeedb26a25789de370171d592c621a2fac;hp=d3be4f87e1984094bbb9524a4c4fb0b3e9cefb6c;hpb=5dad808c1e8e72c40711b0350b1c1bebee16a446;p=picoclvr.git diff --git a/stack.py b/stack.py index d3be4f8..ba452aa 100755 --- a/stack.py +++ b/stack.py @@ -13,45 +13,53 @@ import torch, torchvision # CODE_VAL=val + 2 * nb_stacks -def generate(nb, seq_len, nb_stacks, nb_values): - stack = torch.empty(nb, nb_stacks, seq_len, dtype=torch.int64) - stack_pointers = torch.zeros(nb, nb_stacks, dtype=torch.int64) +def generate_sequences(nb, nb_steps, nb_stacks, nb_values, device=torch.device("cpu")): + stack = torch.empty(nb, nb_stacks, nb_steps, dtype=torch.int64) + stack_counts = torch.zeros(nb, nb_stacks, dtype=torch.int64) k = torch.arange(nb) - result = torch.empty(nb, 2 * seq_len, dtype=torch.int64) + result = torch.empty(nb, 2 * nb_steps, dtype=torch.int64) + recorded_stack_counts = torch.zeros(nb, 2 * nb_steps, dtype=torch.int64) - for t in range(seq_len): + for t in range(nb_steps): op = torch.randint(2, (nb,)) st = torch.randint(nb_stacks, (nb,)) - op = op * (stack_pointers[k, st] > 0) + op = op * (stack_counts[k, st] > 0) val_push = torch.randint(nb_values, (nb,)) - # top_val[n,s]=stack[n,stack_pointers[n,s]] - top_values = stack[ + val_pop = stack[ k, st, - (stack_pointers[k, st] - 1).clamp(min=0), + (stack_counts[k, st] - 1).clamp(min=0), ] - stack[ - k[:, None].expand_as(stack_pointers), - st[:, None].expand_as(stack_pointers), - stack_pointers, - ] = val_push[:, None].expand_as(stack_pointers) - stack_pointers[k[op == 0], st[op == 0]] += 1 - stack_pointers[k[op == 1], st[op == 1]] -= 1 + stack[k, st, stack_counts[k, st]] = val_push + recorded_stack_counts[:, 2 * t + 1] = stack_counts[k, st] + stack_counts[k[op == 0], st[op == 0]] += 1 + stack_counts[k[op == 1], st[op == 1]] -= 1 result[:, 2 * t] = st * 2 + op - result[:, 2 * t + 1] = (op * top_values + (1 - op) * val_push) + 2 * nb_stacks + result[:, 2 * t + 1] = (op * val_pop + (1 - op) * val_push) + 2 * nb_stacks - return result + return result.to(device), recorded_stack_counts.to(device) -def seq_to_str(seq): +def remove_poped_values(seq, nb_stacks): + m = torch.logical_and(seq % 2 == 1, seq < 2 * nb_stacks).long() + seq[:, 1:] = -m[:, :-1] + (1 - m[:, :-1]) * seq[:, 1:] + + +def seq_to_str(seq, show_stack_nb=True,recorded_stack_counts=None): assert seq.size(0) % 2 == 0 s = "" - for t in range(0, seq.size(0), 2): - op = seq[t] - op = f"POP_{op//2}" if op % 2 == 1 else f"PUSH_{op//2}" - val = seq[t + 1] - 2 * nb_stacks + for t in range(seq.size(0) // 2): + n_op = seq[2 * t] + op = f"POP" if n_op % 2 == 1 else f"PSH" + if show_stack_nb: op+=f"_{n_op//2}" + if seq[2 * t + 1] == -1: + val = "?" + else: + val = seq[2 * t + 1] - 2 * nb_stacks if t > 0: s += " " + if recorded_stack_counts is not None: + s += f"[{recorded_stack_counts[2*t+1]}] " s += f"{op} {val}" return s @@ -59,7 +67,20 @@ def seq_to_str(seq): ###################################################################### if __name__ == "__main__": - nb, seq_len, nb_stacks, nb_values = 3, 10, 1, 5 - result = generate(nb=nb, seq_len=seq_len, nb_stacks=nb_stacks, nb_values=nb_values) - for n in range(result.size(0)): - print(seq_to_str(result[n])) + nb, nb_steps, nb_stacks, nb_values = 150000, 10, 1, 5 + seq, recorded_stack_counts = generate_sequences( + nb=nb, nb_steps=nb_steps, nb_stacks=nb_stacks, nb_values=nb_values + ) + + print("-- TRAIN -----------------------------") + + for n in range(min(10, seq.size(0))): + # print(seq_to_str(seq[n], recorded_stack_counts[n])) + print(seq_to_str(seq[n],show_stack_nb=nb_stacks>1)) + + print("-- TEST ------------------------------") + + remove_poped_values(seq, nb_stacks) + + for n in range(min(10, seq.size(0))): + print(seq_to_str(seq[n],show_stack_nb=nb_stacks>1))