tests / test_generation_flax_logits_process.py

add model

5f89bea almost 3 years ago

No virus

6.34 kB

	# coding=utf-8
	# Copyright 2021 The HuggingFace Team Inc.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a clone of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.


	import unittest

	import numpy as np

	from transformers import is_flax_available
	from transformers.testing_utils import require_flax

	from .test_modeling_flax_common import ids_tensor


	if is_flax_available():
	import jax
	import jax.numpy as jnp
	from transformers.generation_flax_logits_process import (
	FlaxLogitsProcessorList,
	FlaxTemperatureLogitsWarper,
	FlaxTopKLogitsWarper,
	FlaxTopPLogitsWarper,
	)


	@require_flax
	class LogitsProcessorTest(unittest.TestCase):
	def _get_uniform_logits(self, batch_size: int, length: int):
	scores = np.ones((batch_size, length)) / length
	return scores

	def test_temperature_dist_warper(self):
	input_ids = None
	length = 20

	scores = self._get_uniform_logits(batch_size=2, length=length)

	# tweak scores to not be uniform anymore
	scores[1, 5] = (1 / length) + 0.1 # peak, 1st batch
	scores[1, 10] = (1 / length) - 0.4 # valley, 1st batch

	# compute softmax
	probs = jax.nn.softmax(scores, axis=-1)

	temp_dist_warper_sharper = FlaxTemperatureLogitsWarper(temperature=0.5)
	temp_dist_warper_smoother = FlaxTemperatureLogitsWarper(temperature=1.3)

	warped_prob_sharp = jax.nn.softmax(temp_dist_warper_sharper(input_ids, scores.copy()), axis=-1)
	warped_prob_smooth = jax.nn.softmax(temp_dist_warper_smoother(input_ids, scores.copy()), axis=-1)

	# uniform distribution stays uniform
	self.assertTrue(jnp.allclose(probs[0, :], warped_prob_sharp[0, :], atol=1e-3))
	self.assertTrue(jnp.allclose(probs[0, :], warped_prob_smooth[0, :], atol=1e-3))

	# sharp peaks get higher, valleys get lower
	self.assertLess(probs[1, :].max(), warped_prob_sharp[1, :].max())
	self.assertGreater(probs[1, :].min(), warped_prob_sharp[1, :].min())

	# smooth peaks get lower, valleys get higher
	self.assertGreater(probs[1, :].max(), warped_prob_smooth[1, :].max())
	self.assertLess(probs[1, :].min(), warped_prob_smooth[1, :].min())

	def test_top_k_dist_warper(self):
	input_ids = None
	vocab_size = 10
	batch_size = 2

	# create ramp distribution
	ramp_logits = np.broadcast_to(np.arange(vocab_size)[None, :], (batch_size, vocab_size)).copy()
	ramp_logits[1:, : vocab_size // 2] = ramp_logits[1:, : vocab_size // 2] + vocab_size

	top_k_warp = FlaxTopKLogitsWarper(3)

	scores = top_k_warp(input_ids, ramp_logits)

	# check that correct tokens are filtered
	self.assertListEqual(jnp.isinf(scores[0]).tolist(), 7 * [True] + 3 * [False])
	self.assertListEqual(jnp.isinf(scores[1]).tolist(), 2 * [True] + 3 * [False] + 5 * [True])

	# check special case
	length = 5
	top_k_warp_safety_check = FlaxTopKLogitsWarper(top_k=1, filter_value=0.0, min_tokens_to_keep=3)

	ramp_logits = np.broadcast_to(np.arange(length)[None, :], (batch_size, length)).copy()
	scores = top_k_warp_safety_check(input_ids, ramp_logits)

	# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
	self.assertListEqual((scores == 0.0).sum(axis=-1).tolist(), [2, 2])

	def test_top_p_dist_warper(self):
	input_ids = None
	vocab_size = 10
	batch_size = 2

	# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
	dist = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]))

	top_p_warp = FlaxTopPLogitsWarper(0.7)
	filtered_dist = np.exp(top_p_warp(input_ids, dist))

	# dist should be filtered to keep min num values so that sum is >= 0.7
	# exp (-inf) => 0
	EXPECTED_FILTERED_DIST = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]])
	self.assertTrue(np.allclose(filtered_dist, EXPECTED_FILTERED_DIST, atol=1e-3))

	# check edge cases with negative and extreme logits
	ramp_logits = np.broadcast_to(np.arange(vocab_size)[None, :], (batch_size, vocab_size)).copy() - (
	vocab_size // 2
	)

	# make ramp_logits more extreme
	ramp_logits[1] = ramp_logits[1] * 100.0

	# make sure at least 2 tokens are kept
	top_p_warp = FlaxTopPLogitsWarper(0.9, min_tokens_to_keep=2, filter_value=0.0)
	filtered_dist = top_p_warp(input_ids, ramp_logits)

	# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
	self.assertListEqual((filtered_dist != 0.0).sum(axis=-1).tolist(), [3, 2])

	def test_processor_list(self):
	batch_size = 4
	sequence_length = 10
	vocab_size = 15

	# dummy input_ids and scores
	input_ids = ids_tensor((batch_size, sequence_length), vocab_size)
	input_ids_comp = input_ids.copy()

	scores = self._get_uniform_logits(batch_size, vocab_size)
	scores_comp = scores.copy()

	# instantiate all dist processors
	temp_dist_warp = FlaxTemperatureLogitsWarper(temperature=0.5)
	top_k_warp = FlaxTopKLogitsWarper(3)
	top_p_warp = FlaxTopPLogitsWarper(0.8)

	# no processor list
	scores = temp_dist_warp(input_ids, scores)
	scores = top_k_warp(input_ids, scores)
	scores = top_p_warp(input_ids, scores)

	# with processor list
	processor = FlaxLogitsProcessorList([temp_dist_warp, top_k_warp, top_p_warp])
	scores_comp = processor(input_ids, scores_comp)

	# scores should be equal
	self.assertTrue(jnp.allclose(scores, scores_comp, atol=1e-3))

	# input_ids should never be changed
	self.assertListEqual(input_ids.tolist(), input_ids_comp.tolist())