594 lines
24 KiB
Python
594 lines
24 KiB
Python
import inspect
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import tempfile
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import unittest
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import numpy as np
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import transformers
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from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig, is_flax_available, is_torch_available
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from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow
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from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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if is_flax_available():
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import jax
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import jax.numpy as jnp
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from transformers.modeling_flax_pytorch_utils import (
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convert_pytorch_state_dict_to_flax,
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load_flax_weights_in_pytorch_model,
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)
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from transformers.models.clip.modeling_flax_clip import (
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FlaxCLIPModel,
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FlaxCLIPTextModel,
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FlaxCLIPTextModelWithProjection,
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FlaxCLIPVisionModel,
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)
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if is_torch_available():
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import torch
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class FlaxCLIPVisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=0.02,
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scope=None,
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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.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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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.dropout = dropout
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.scope = scope
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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config = CLIPVisionConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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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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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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)
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return config, pixel_values
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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, pixel_values = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_flax
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class FlaxCLIPVisionModelTest(FlaxModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as CLIP does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (FlaxCLIPVisionModel,) if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxCLIPVisionModelTester(self)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.__call__)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_jit_compilation(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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model = model_class(config)
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@jax.jit
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def model_jitted(pixel_values, **kwargs):
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return model(pixel_values=pixel_values, **kwargs).to_tuple()
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with self.subTest("JIT Enabled"):
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jitted_outputs = model_jitted(**prepared_inputs_dict)
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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outputs = model_jitted(**prepared_inputs_dict)
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self.assertEqual(len(outputs), len(jitted_outputs))
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for jitted_output, output in zip(jitted_outputs, outputs):
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self.assertEqual(jitted_output.shape, output.shape)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.hidden_states
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self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
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# CLIP has a different seq_length
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image_size = (self.model_tester.image_size, self.model_tester.image_size)
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patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_length = num_patches + 1
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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)
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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# in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.model_tester.image_size, self.model_tester.image_size)
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patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_length = num_patches + 1
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_length, seq_length],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_length, seq_length],
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)
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# FlaxCLIPVisionModel does not have any base model
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def test_save_load_from_base(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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def test_save_load_to_base(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_from_base_pt(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_to_base_pt(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_bf16_to_base_pt(self):
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pass
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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("openai/clip-vit-base-patch32", from_pt=True)
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outputs = model(np.ones((1, 3, 224, 224)))
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self.assertIsNotNone(outputs)
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class FlaxCLIPTextModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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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_labels=True,
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vocab_size=99,
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hidden_size=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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scope=None,
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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_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.dropout = dropout
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self.attention_dropout = attention_dropout
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.scope = scope
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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 = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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config = CLIPTextConfig(
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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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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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max_position_embeddings=self.max_position_embeddings,
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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, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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@require_flax
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class FlaxCLIPTextModelTest(FlaxModelTesterMixin, unittest.TestCase):
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all_model_classes = (FlaxCLIPTextModel, FlaxCLIPTextModelWithProjection) if is_flax_available() else ()
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def setUp(self):
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self.model_tester = FlaxCLIPTextModelTester(self)
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# FlaxCLIPTextModel does not have any base model
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def test_save_load_from_base(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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def test_save_load_to_base(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_from_base_pt(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_to_base_pt(self):
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pass
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# FlaxCLIPVisionModel does not have any base model
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@is_pt_flax_cross_test
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def test_save_load_bf16_to_base_pt(self):
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pass
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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("openai/clip-vit-base-patch32", from_pt=True)
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outputs = model(np.ones((1, 1)))
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self.assertIsNotNone(outputs)
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class FlaxCLIPModelTester:
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def __init__(self, parent, is_training=True):
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self.parent = parent
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self.text_model_tester = FlaxCLIPTextModelTester(parent)
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self.vision_model_tester = FlaxCLIPVisionModelTester(parent)
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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config = CLIPConfig.from_text_vision_configs(text_config, vision_config, projection_dim=64)
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return config, input_ids, attention_mask, pixel_values
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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, pixel_values = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"pixel_values": pixel_values,
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}
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return config, inputs_dict
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@require_flax
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class FlaxCLIPModelTest(FlaxModelTesterMixin, unittest.TestCase):
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all_model_classes = (FlaxCLIPModel,) if is_flax_available() else ()
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test_attention_outputs = False
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def setUp(self):
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self.model_tester = FlaxCLIPModelTester(self)
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# hidden_states are tested in individual model tests
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def test_hidden_states_output(self):
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pass
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def test_jit_compilation(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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model = model_class(config)
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@jax.jit
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def model_jitted(input_ids, pixel_values, **kwargs):
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return model(input_ids=input_ids, pixel_values=pixel_values, **kwargs).to_tuple()
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with self.subTest("JIT Enabled"):
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jitted_outputs = model_jitted(**prepared_inputs_dict)
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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outputs = model_jitted(**prepared_inputs_dict)
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self.assertEqual(len(outputs), len(jitted_outputs))
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for jitted_output, output in zip(jitted_outputs[:4], outputs[:4]):
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self.assertEqual(jitted_output.shape, output.shape)
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.__call__)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["input_ids", "pixel_values", "attention_mask", "position_ids"]
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self.assertListEqual(arg_names[:4], expected_arg_names)
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def test_get_image_features(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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model = FlaxCLIPModel(config)
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@jax.jit
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def model_jitted(pixel_values):
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return model.get_image_features(pixel_values=pixel_values)
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with self.subTest("JIT Enabled"):
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jitted_output = model_jitted(inputs_dict["pixel_values"])
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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output = model_jitted(inputs_dict["pixel_values"])
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self.assertEqual(jitted_output.shape, output.shape)
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self.assertTrue(np.allclose(jitted_output, output, atol=1e-3))
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def test_get_text_features(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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model = FlaxCLIPModel(config)
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@jax.jit
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def model_jitted(input_ids, attention_mask, **kwargs):
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return model.get_text_features(input_ids=input_ids, attention_mask=attention_mask)
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with self.subTest("JIT Enabled"):
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jitted_output = model_jitted(**inputs_dict)
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with self.subTest("JIT Disabled"):
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with jax.disable_jit():
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output = model_jitted(**inputs_dict)
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self.assertEqual(jitted_output.shape, output.shape)
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self.assertTrue(np.allclose(jitted_output, output, atol=1e-3))
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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("openai/clip-vit-base-patch32", from_pt=True)
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outputs = model(input_ids=np.ones((1, 1)), pixel_values=np.ones((1, 3, 224, 224)))
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self.assertIsNotNone(outputs)
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|
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# overwrite from common since FlaxCLIPModel returns nested output
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# which is not supported in the common test
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@is_pt_flax_cross_test
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def test_equivalence_pt_to_flax(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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|
|
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for model_class in self.all_model_classes:
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with self.subTest(model_class.__name__):
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# prepare inputs
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|
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
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|
|
|
# load corresponding PyTorch class
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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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|
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|
pt_model = pt_model_class(config).eval()
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|
fx_model = model_class(config, dtype=jnp.float32)
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|
|
|
fx_state = convert_pytorch_state_dict_to_flax(pt_model.state_dict(), fx_model)
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|
fx_model.params = fx_state
|
|
|
|
with torch.no_grad():
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|
pt_outputs = pt_model(**pt_inputs).to_tuple()
|
|
|
|
fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
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|
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
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|
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
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|
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
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|
pt_model.save_pretrained(tmpdirname)
|
|
fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
|
|
|
|
fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple()
|
|
self.assertEqual(
|
|
len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
|
|
)
|
|
for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4], pt_outputs[:4]):
|
|
self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
|
|
|
|
# overwrite from common since FlaxCLIPModel returns nested output
|
|
# which is not supported in the common test
|
|
@is_pt_flax_cross_test
|
|
def test_equivalence_flax_to_pt(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
with self.subTest(model_class.__name__):
|
|
# prepare inputs
|
|
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
|
pt_inputs = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}
|
|
|
|
# load corresponding PyTorch class
|
|
pt_model_class_name = model_class.__name__[4:] # Skip the "Flax" at the beginning
|
|
pt_model_class = getattr(transformers, pt_model_class_name)
|
|
|
|
pt_model = pt_model_class(config).eval()
|
|
fx_model = model_class(config, dtype=jnp.float32)
|
|
|
|
pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
|
|
|
|
# make sure weights are tied in PyTorch
|
|
pt_model.tie_weights()
|
|
|
|
with torch.no_grad():
|
|
pt_outputs = pt_model(**pt_inputs).to_tuple()
|
|
|
|
fx_outputs = fx_model(**prepared_inputs_dict).to_tuple()
|
|
self.assertEqual(len(fx_outputs), len(pt_outputs), "Output lengths differ between Flax and PyTorch")
|
|
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs[:4]):
|
|
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
fx_model.save_pretrained(tmpdirname)
|
|
pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)
|
|
|
|
with torch.no_grad():
|
|
pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
|
|
|
|
self.assertEqual(
|
|
len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
|
|
)
|
|
for fx_output, pt_output in zip(fx_outputs[:4], pt_outputs_loaded[:4]):
|
|
self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
|
|
|
|
# overwrite from common since FlaxCLIPModel returns nested output
|
|
# which is not supported in the common test
|
|
def test_from_pretrained_save_pretrained(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class.__name__ != "FlaxBertModel":
|
|
continue
|
|
|
|
with self.subTest(model_class.__name__):
|
|
model = model_class(config)
|
|
|
|
prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
|
|
outputs = model(**prepared_inputs_dict).to_tuple()
|
|
|
|
# verify that normal save_pretrained works as expected
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model_loaded = model_class.from_pretrained(tmpdirname)
|
|
|
|
outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()[:4]
|
|
for output_loaded, output in zip(outputs_loaded, outputs):
|
|
self.assert_almost_equals(output_loaded, output, 1e-3)
|
|
|
|
# verify that save_pretrained for distributed training
|
|
# with `params=params` works as expected
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname, params=model.params)
|
|
model_loaded = model_class.from_pretrained(tmpdirname)
|
|
|
|
outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()[:4]
|
|
for output_loaded, output in zip(outputs_loaded, outputs):
|
|
self.assert_almost_equals(output_loaded, output, 1e-3)
|