Changed order of some parameters to be more consistent. Identical results.
This commit is contained in:
w4nderlust
Julien Chaumond
parent
f42816e7fc
commit
893d0d64fe
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@ -121,17 +121,17 @@ def perturb_past(
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accumulated_hidden=None,
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grad_norms=None,
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stepsize=0.01,
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one_hot_bows_vectors=None,
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classifier=None,
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class_label=None,
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one_hot_bows_vectors=None,
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loss_type=0,
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num_iterations=3,
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kl_scale=0.01,
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window_length=0,
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horizon_length=1,
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window_length=0,
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decay=False,
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gamma=1.5,
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device='cuda'
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kl_scale=0.01,
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device='cuda',
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):
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# Generate inital perturbed past
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grad_accumulator = [
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@ -351,8 +351,7 @@ def get_classifier(
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def get_bag_of_words_indices(bag_of_words_ids_or_paths: List[str], tokenizer) -> \
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List[
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List[List[int]]]:
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List[List[List[int]]]:
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bow_indices = []
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for id_or_path in bag_of_words_ids_or_paths:
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if id_or_path in BAG_OF_WORDS_ARCHIVE_MAP:
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@ -388,22 +387,22 @@ def full_text_generation(
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context=None,
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num_samples=1,
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device="cuda",
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sample=False,
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bag_of_words=None,
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discrim=None,
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class_label=None,
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bag_of_words=None,
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length=100,
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grad_length=10000,
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stepsize=0.02,
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num_iterations=3,
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temperature=1.0,
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gm_scale=0.9,
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kl_scale=0.01,
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top_k=10,
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window_length=0,
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sample=False,
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num_iterations=3,
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grad_length=10000,
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horizon_length=1,
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window_length=0,
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decay=False,
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gamma=1.5,
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gm_scale=0.9,
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kl_scale=0.01,
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**kwargs
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):
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classifier, class_id = get_classifier(
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@ -454,24 +453,24 @@ def full_text_generation(
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tokenizer=tokenizer,
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context=context,
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device=device,
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sample=sample,
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perturb=True,
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bow_indices=bow_indices,
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classifier=classifier,
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class_label=class_id,
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loss_type=loss_type,
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length=length,
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grad_length=grad_length,
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stepsize=stepsize,
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num_iterations=num_iterations,
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temperature=temperature,
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gm_scale=gm_scale,
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kl_scale=kl_scale,
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top_k=top_k,
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window_length=window_length,
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sample=sample,
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num_iterations=num_iterations,
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grad_length=grad_length,
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horizon_length=horizon_length,
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window_length=window_length,
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decay=decay,
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gamma=gamma,
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gm_scale=gm_scale,
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kl_scale=kl_scale,
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)
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pert_gen_tok_texts.append(pert_gen_tok_text)
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if classifier is not None:
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@ -490,24 +489,24 @@ def generate_text_pplm(
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context=None,
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past=None,
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device="cuda",
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sample=False,
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perturb=True,
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bow_indices=None,
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classifier=None,
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class_label=None,
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bow_indices=None,
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loss_type=0,
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length=100,
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grad_length=10000,
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stepsize=0.02,
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num_iterations=3,
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temperature=1.0,
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gm_scale=0.9,
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kl_scale=0.01,
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top_k=10,
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window_length=0,
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sample=False,
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num_iterations=3,
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grad_length=10000,
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horizon_length=1,
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window_length=0,
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decay=False,
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gamma=1.5,
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gm_scale=0.9,
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kl_scale=0.01,
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):
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output_so_far = (
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torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0)
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@ -561,17 +560,17 @@ def generate_text_pplm(
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accumulated_hidden=accumulated_hidden,
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grad_norms=grad_norms,
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stepsize=current_stepsize,
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one_hot_bows_vectors=one_hot_bows_vectors,
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classifier=classifier,
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class_label=class_label,
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one_hot_bows_vectors=one_hot_bows_vectors,
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loss_type=loss_type,
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num_iterations=num_iterations,
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kl_scale=kl_scale,
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window_length=window_length,
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horizon_length=horizon_length,
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window_length=window_length,
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decay=decay,
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gamma=gamma,
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device=device
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kl_scale=kl_scale,
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device=device,
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)
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loss_in_time.append(loss_this_iter)
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else:
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@ -685,7 +684,7 @@ def run_pplm_example(
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pretrained_model = DISCRIMINATOR_MODELS_PARAMS[discrim][
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"pretrained_model"
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]
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print("discrim = {}, setting pretrained_model "
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print("discrim = {}, pretrained_model set "
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"to discriminator's = {}".format(discrim, pretrained_model))
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# load pretrained model
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@ -810,6 +809,20 @@ if __name__ == '__main__':
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default="gpt2-medium",
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help="pretrained model name or path to local checkpoint",
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)
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parser.add_argument(
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"--cond_text", type=str, default="The lake",
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help="Prefix texts to condition on"
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)
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parser.add_argument(
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"--uncond", action="store_true",
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help="Generate from end-of-text as prefix"
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)
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parser.add_argument(
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"--num_samples",
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type=int,
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default=1,
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help="Number of samples to generate from the modified latents",
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)
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parser.add_argument(
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"--bag_of_words",
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"-B",
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@ -837,40 +850,16 @@ if __name__ == '__main__':
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default=-1,
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help="Class label used for the discriminator",
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)
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parser.add_argument("--stepsize", type=float, default=0.02)
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parser.add_argument("--length", type=int, default=100)
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--stepsize", type=float, default=0.02)
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parser.add_argument("--temperature", type=float, default=1.0)
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parser.add_argument("--top_k", type=int, default=10)
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parser.add_argument("--gm_scale", type=float, default=0.9)
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parser.add_argument("--kl_scale", type=float, default=0.01)
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parser.add_argument("--no_cuda", action="store_true", help="no cuda")
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parser.add_argument(
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"--sample", action="store_true",
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help="Generate from end-of-text as prefix"
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)
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parser.add_argument(
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"--uncond", action="store_true",
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help="Generate from end-of-text as prefix"
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)
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parser.add_argument(
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"--cond_text", type=str, default="The lake",
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help="Prefix texts to condition on"
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)
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parser.add_argument("--num_iterations", type=int, default=3)
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parser.add_argument("--grad_length", type=int, default=10000)
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parser.add_argument(
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"--num_samples",
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type=int,
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default=1,
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help="Number of samples to generate from the modified latents",
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)
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parser.add_argument(
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"--horizon_length",
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type=int,
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default=1,
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help="Length of future to optimize over",
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)
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parser.add_argument(
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"--window_length",
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type=int,
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@ -878,9 +867,19 @@ if __name__ == '__main__':
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help="Length of past which is being optimized; "
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"0 corresponds to infinite window length",
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)
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parser.add_argument(
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"--horizon_length",
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type=int,
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default=1,
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help="Length of future to optimize over",
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)
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parser.add_argument("--decay", action="store_true",
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help="whether to decay or not")
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parser.add_argument("--gamma", type=float, default=1.5)
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parser.add_argument("--gm_scale", type=float, default=0.9)
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parser.add_argument("--kl_scale", type=float, default=0.01)
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--no_cuda", action="store_true", help="no cuda")
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parser.add_argument("--colorama", action="store_true",
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help="colors keywords")
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