tests / test_generation_flax_utils.py

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	# Copyright 2021 The HuggingFace Team. All rights reserved.
	#
	# 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 copy 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 random

	import numpy as np

	from transformers import is_flax_available
	from transformers.testing_utils import require_flax


	if is_flax_available():
	import os

	import jax
	import jax.numpy as jnp
	from jax import jit

	os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8


	def ids_tensor(shape, vocab_size, rng=None):
	"""Creates a random int32 tensor of the shape within the vocab size."""
	if rng is None:
	rng = random.Random()

	total_dims = 1
	for dim in shape:
	total_dims *= dim

	values = []
	for _ in range(total_dims):
	values.append(rng.randint(0, vocab_size - 1))

	output = np.array(values, dtype=jnp.int32).reshape(shape)

	return output


	def random_attention_mask(shape, rng=None):
	attn_mask = ids_tensor(shape, vocab_size=2, rng=rng)
	# make sure that at least one token is attended to for each batch
	attn_mask[:, -1] = 1
	return attn_mask


	@require_flax
	class FlaxGenerationTesterMixin:
	model_tester = None
	all_generative_model_classes = ()

	def _get_input_ids_and_config(self):
	config, inputs = self.model_tester.prepare_config_and_inputs_for_common()

	# cut to half length & take max batch_size 3
	max_batch_size = 2
	sequence_length = inputs["input_ids"].shape[-1] // 2
	input_ids = inputs["input_ids"][:max_batch_size, :sequence_length]

	attention_mask = jnp.ones_like(input_ids)
	attention_mask = attention_mask[:max_batch_size, :sequence_length]

	# generate max 5 tokens
	max_length = input_ids.shape[-1] + 5
	if config.eos_token_id is not None and config.pad_token_id is None:
	# hack to allow generate for models such as GPT2 as is done in `generate()`
	config.pad_token_id = config.eos_token_id
	return config, input_ids, attention_mask, max_length

	def test_greedy_generate(self):
	config, input_ids, _, max_length = self._get_input_ids_and_config()
	config.do_sample = False
	config.max_length = max_length

	for model_class in self.all_generative_model_classes:
	model = model_class(config)

	generation_outputs = model.generate(input_ids).sequences
	self.assertEqual(generation_outputs.shape[-1], max_length)

	jit_generate = jit(model.generate)
	jit_generation_outputs = jit_generate(input_ids).sequences

	self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())

	def test_sample_generate(self):
	config, input_ids, _, max_length = self._get_input_ids_and_config()
	config.do_sample = True
	config.max_length = max_length

	for model_class in self.all_generative_model_classes:
	model = model_class(config)

	generation_outputs = model.generate(input_ids).sequences
	self.assertEqual(generation_outputs.shape[-1], max_length)

	jit_generate = jit(model.generate)
	jit_generation_outputs = jit_generate(input_ids).sequences

	self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())

	def test_sample_generate_logits_warper(self):
	config, input_ids, _, max_length = self._get_input_ids_and_config()
	config.do_sample = True
	config.max_length = max_length
	config.temperature = 0.8
	config.top_k = 10
	config.top_p = 0.3

	for model_class in self.all_generative_model_classes:
	model = model_class(config)

	generation_outputs = model.generate(input_ids).sequences
	self.assertEqual(generation_outputs.shape[-1], max_length)

	jit_generate = jit(model.generate)
	jit_generation_outputs = jit_generate(input_ids).sequences

	self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())

	def test_greedy_generate_attn_mask(self):
	config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

	# pad attention mask on the left
	attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)

	config.do_sample = False
	config.max_length = max_length

	for model_class in self.all_generative_model_classes:
	model = model_class(config)

	generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences
	self.assertEqual(generation_outputs.shape[-1], max_length)

	jit_generate = jit(model.generate)
	jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences

	self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())

	def test_sample_generate_attn_mask(self):
	config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()

	# pad attention mask on the left
	attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)

	config.do_sample = True
	config.max_length = max_length

	for model_class in self.all_generative_model_classes:
	model = model_class(config)

	generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences
	self.assertEqual(generation_outputs.shape[-1], max_length)

	jit_generate = jit(model.generate)
	jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences

	self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())