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https://github.com/huggingface/transformers.git
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cee2d2135f
* fix_torch_device_generate_test * remove @ * correct greedy search * save intertmed * add final logits bias * correct * up * add more tests * fix another bug * finish tests * finish marian tests * up Co-authored-by: Patrick von Platen <patrick@huggingface.co>
277 lines
10 KiB
Python
277 lines
10 KiB
Python
# Copyright 2021 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import numpy as np
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import transformers
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from transformers import is_flax_available, is_torch_available
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from transformers.testing_utils import is_pt_flax_cross_test, require_flax
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if is_flax_available():
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import os
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import jax
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import jax.numpy as jnp
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from jax import jit
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from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
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os.environ["XLA_PYTHON_CLIENT_MEM_FRACTION"] = "0.12" # assumed parallelism: 8
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if is_torch_available():
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import torch
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def ids_tensor(shape, vocab_size, rng=None):
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"""Creates a random int32 tensor of the shape within the vocab size."""
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if rng is None:
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rng = random.Random()
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total_dims = 1
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for dim in shape:
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total_dims *= dim
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values = []
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for _ in range(total_dims):
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values.append(rng.randint(0, vocab_size - 1))
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output = np.array(values, dtype=jnp.int32).reshape(shape)
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return output
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def random_attention_mask(shape, rng=None):
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attn_mask = ids_tensor(shape, vocab_size=2, rng=rng)
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# make sure that at least one token is attended to for each batch
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attn_mask[:, -1] = 1
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return attn_mask
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@require_flax
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class FlaxGenerationTesterMixin:
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model_tester = None
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all_generative_model_classes = ()
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def _get_input_ids_and_config(self):
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config, inputs = self.model_tester.prepare_config_and_inputs_for_common()
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# cut to half length & take max batch_size 3
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max_batch_size = 2
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sequence_length = inputs["input_ids"].shape[-1] // 2
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input_ids = inputs["input_ids"][:max_batch_size, :sequence_length]
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attention_mask = jnp.ones_like(input_ids)
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attention_mask = attention_mask[:max_batch_size, :sequence_length]
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# generate max 5 tokens
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max_length = input_ids.shape[-1] + 5
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if config.eos_token_id is not None and config.pad_token_id is None:
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# hack to allow generate for models such as GPT2 as is done in `generate()`
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config.pad_token_id = config.eos_token_id
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return config, input_ids, attention_mask, max_length
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@is_pt_flax_cross_test
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def test_greedy_generate_pt_fx(self):
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config, input_ids, _, max_length = self._get_input_ids_and_config()
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config.do_sample = False
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config.max_length = max_length
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config.decoder_start_token_id = 0
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for model_class in self.all_generative_model_classes:
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flax_model = model_class(config)
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pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
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pt_model_class = getattr(transformers, pt_model_class_name)
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pt_model = pt_model_class(config).eval()
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pt_model = load_flax_weights_in_pytorch_model(pt_model, flax_model.params)
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flax_generation_outputs = flax_model.generate(input_ids).sequences
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pt_generation_outputs = pt_model.generate(torch.tensor(input_ids, dtype=torch.long))
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if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
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flax_generation_outputs = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
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self.assertListEqual(pt_generation_outputs.numpy().tolist(), flax_generation_outputs.tolist())
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def test_greedy_generate(self):
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config, input_ids, _, max_length = self._get_input_ids_and_config()
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config.do_sample = False
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config.max_length = max_length
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_sample_generate(self):
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config, input_ids, _, max_length = self._get_input_ids_and_config()
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config.do_sample = True
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config.max_length = max_length
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_beam_search_generate(self):
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config, input_ids, _, max_length = self._get_input_ids_and_config()
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config.do_sample = False
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config.max_length = max_length
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config.num_beams = 2
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_sample_generate_logits_warper(self):
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config, input_ids, _, max_length = self._get_input_ids_and_config()
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config.do_sample = True
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config.max_length = max_length
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config.temperature = 0.8
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config.top_k = 10
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config.top_p = 0.3
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config.min_length = 1
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config.forced_bos_token_id = 8
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config.forced_eos_token_id = 9
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_greedy_generate_logits_warper(self):
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config, input_ids, _, max_length = self._get_input_ids_and_config()
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config.max_length = max_length
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config.min_length = 1
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config.forced_bos_token_id = 8
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config.forced_eos_token_id = 9
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_beam_search_generate_logits_warper(self):
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config, input_ids, _, max_length = self._get_input_ids_and_config()
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config.max_length = max_length
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config.num_beams = 2
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config.min_length = 1
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config.forced_bos_token_id = 8
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config.forced_eos_token_id = 9
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_greedy_generate_attn_mask(self):
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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# pad attention mask on the left
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attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)
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config.do_sample = False
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config.max_length = max_length
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_sample_generate_attn_mask(self):
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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# pad attention mask on the left
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attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)
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config.do_sample = True
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config.max_length = max_length
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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def test_beam_search_generate_attn_mask(self):
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config, input_ids, attention_mask, max_length = self._get_input_ids_and_config()
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# pad attention mask on the left
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attention_mask = jax.ops.index_update(attention_mask, (0, 0), 0)
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config.num_beams = 2
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config.max_length = max_length
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for model_class in self.all_generative_model_classes:
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model = model_class(config)
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generation_outputs = model.generate(input_ids, attention_mask=attention_mask).sequences
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self.assertEqual(generation_outputs.shape[-1], max_length)
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jit_generate = jit(model.generate)
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jit_generation_outputs = jit_generate(input_ids, attention_mask=attention_mask).sequences
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self.assertListEqual(generation_outputs.tolist(), jit_generation_outputs.tolist())
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