262 lines
10 KiB
Python
262 lines
10 KiB
Python
# Copyright 2023 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 unittest
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import numpy as np
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from transformers import LlamaConfig, is_flax_available, is_tokenizers_available
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from transformers.testing_utils import require_flax, slow
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from ...generation.test_flax_utils import FlaxGenerationTesterMixin
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from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
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if is_flax_available():
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import jax.numpy as jnp
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from transformers.models.llama.modeling_flax_llama import FlaxLlamaForCausalLM, FlaxLlamaModel
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if is_tokenizers_available():
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from transformers import LlamaTokenizerFast
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class FlaxLlamaModelTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=False,
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use_labels=True,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=2,
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intermediate_size=64,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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window_size=7,
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initializer_range=0.02,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.window_size = window_size
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self.initializer_range = initializer_range
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self.scope = None
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self.bos_token_id = vocab_size - 1
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self.eos_token_id = vocab_size - 1
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self.pad_token_id = vocab_size - 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = np.tril(np.ones((self.batch_size, self.seq_length)))
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config = LlamaConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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use_cache=True,
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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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return (config, input_ids, input_mask)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, attention_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
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return config, inputs_dict
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def check_use_cache_forward(self, model_class_name, config, input_ids, attention_mask):
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max_decoder_length = 20
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model = model_class_name(config)
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past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
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attention_mask = jnp.ones((input_ids.shape[0], max_decoder_length), dtype="i4")
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
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)
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outputs_cache = model(
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input_ids[:, :-1],
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attention_mask=attention_mask,
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past_key_values=past_key_values,
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position_ids=position_ids,
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)
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position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
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outputs_cache_next = model(
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input_ids[:, -1:],
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attention_mask=attention_mask,
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past_key_values=outputs_cache.past_key_values,
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position_ids=position_ids,
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)
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outputs = model(input_ids)
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diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
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self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
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def check_use_cache_forward_with_attn_mask(self, model_class_name, config, input_ids, attention_mask):
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max_decoder_length = 20
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model = model_class_name(config)
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attention_mask_cache = jnp.concatenate(
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[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))],
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axis=-1,
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)
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past_key_values = model.init_cache(input_ids.shape[0], max_decoder_length)
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position_ids = jnp.broadcast_to(
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jnp.arange(input_ids.shape[-1] - 1)[None, :], (input_ids.shape[0], input_ids.shape[-1] - 1)
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)
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outputs_cache = model(
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input_ids[:, :-1],
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attention_mask=attention_mask_cache,
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past_key_values=past_key_values,
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position_ids=position_ids,
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)
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position_ids = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]], dtype="i4")
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outputs_cache_next = model(
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input_ids[:, -1:],
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past_key_values=outputs_cache.past_key_values,
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attention_mask=attention_mask_cache,
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position_ids=position_ids,
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)
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outputs = model(input_ids, attention_mask=attention_mask)
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diff = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
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self.parent.assertTrue(diff < 1e-3, msg=f"Max diff is {diff}")
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@require_flax
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class FlaxLlamaModelTest(FlaxModelTesterMixin, FlaxGenerationTesterMixin, unittest.TestCase):
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all_model_classes = (FlaxLlamaModel, FlaxLlamaForCausalLM) if is_flax_available() else ()
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all_generative_model_classes = (FlaxLlamaForCausalLM,) if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxLlamaModelTester(self)
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def test_use_cache_forward(self):
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for model_class_name in self.all_model_classes:
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config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_use_cache_forward(model_class_name, config, input_ids, attention_mask)
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def test_use_cache_forward_with_attn_mask(self):
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for model_class_name in self.all_model_classes:
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config, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs()
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self.model_tester.check_use_cache_forward_with_attn_mask(
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model_class_name, config, input_ids, attention_mask
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)
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@slow
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def test_model_from_pretrained(self):
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for model_class_name in self.all_model_classes:
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model = model_class_name.from_pretrained("openlm-research/open_llama_3b_v2", from_pt=True)
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outputs = model(np.ones((1, 1)))
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self.assertIsNotNone(outputs)
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@slow
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@require_flax
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class FlaxLlamaIntegrationTest(unittest.TestCase):
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def setUp(self):
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self.model_id = "openlm-research/open_llama_3b_v2"
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self.model = FlaxLlamaForCausalLM.from_pretrained(self.model_id, from_pt=True)
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self.test_batch = jnp.arange(32).reshape(4, 8) + 1911
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def test_model_logits(self):
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flax_logits = self.model(self.test_batch).logits
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# fmt: off
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EXPECTED_LOGITS = [-74.4243, -74.0680, -65.2507, -79.1658, -77.7460, -69.2379, -86.4588, -84.8933, -77.8456]
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EXPECTED_MIN, EXPECTED_MAX, EXPECTED_MEAN = -96.9952
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EXPECTED_MAX = -18.4571
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EXPECTED_MEAN = -65.0608
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# fmt: on
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self.assertTrue(np.allclose(flax_logits[0, :3, :3].flatten(), EXPECTED_LOGITS, atol=1e-4))
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self.assertAlmostEqual(flax_logits.min(), EXPECTED_MIN, places=3)
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self.assertAlmostEqual(flax_logits.max(), EXPECTED_MAX, places=3)
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self.assertAlmostEqual(flax_logits.mean(), EXPECTED_MEAN, places=3)
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def test_model_hidden_states(self):
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flax_hidden_states = self.model(self.test_batch, output_hidden_states=True).hidden_states
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flax_hidden_means = [h.mean() for h in flax_hidden_states]
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# fmt: off
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EXPECTED_HIDDEN_MEANS = [
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-0.00007,-0.00049,-0.00169,-0.00253,-0.00271,
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-0.00290,-0.00252,0.00230,0.00230,0.00198,
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0.00196,0.00174,0.00246,0.00205,0.00242,
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0.00171,0.00092,0.00054,0.00102,0.00024,
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0.00029,0.00037,-0.00101,-0.00062,-0.00341,-0.00636,-0.00357
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]
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# fmt: on
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self.assertTrue(np.allclose(flax_hidden_means, EXPECTED_HIDDEN_MEANS, atol=1e-4))
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def test_generated_text(self):
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tokenizer = LlamaTokenizerFast.from_pretrained(self.model_id)
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tokenizer.pad_token_id = 2
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test_batch = ["Aloha, World! ", "2 + 2 = ", "Paris is the capital of ", "我很高興認識"]
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inputs = tokenizer(test_batch, return_tensors="np", truncation=True, padding=True)
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generated_ids = self.model.generate(**inputs, max_length=15).sequences
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generated_text = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
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# fmt: off
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EXPECTED_GENERATION = [
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"Aloha, World! 201",
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"2 + 2 = 4\n2",
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"Paris is the capital of Île-",
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"我很高興認識你，我"
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]
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# fmt: on
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self.assertListEqual(generated_text, EXPECTED_GENERATION)
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