668 lines
28 KiB
Python
668 lines
28 KiB
Python
# Copyright 2020 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 copy
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import inspect
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import random
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import tempfile
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import unittest
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from typing import List, Tuple
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import numpy as np
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import transformers
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from huggingface_hub import HfApi
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from requests.exceptions import HTTPError
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from transformers import BertConfig, is_flax_available, is_torch_available
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from transformers.models.auto import get_values
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from transformers.testing_utils import (
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ENDPOINT_STAGING,
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PASS,
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USER,
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CaptureLogger,
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is_pt_flax_cross_test,
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is_staging_test,
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require_flax,
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)
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from transformers.utils import logging
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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 flax.core.frozen_dict import unfreeze
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from flax.traverse_util import flatten_dict
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from transformers import (
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FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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FLAX_MODEL_MAPPING,
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FlaxAutoModelForSequenceClassification,
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FlaxBertModel,
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)
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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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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 _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key or "initializer_factor" in key:
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setattr(configs_no_init, key, 1e-10)
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return configs_no_init
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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 floats_tensor(shape, scale=1.0, rng=None, name=None):
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"""Creates a random float32 tensor"""
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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.random() * scale)
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return np.array(values, dtype=jnp.float32).reshape(shape)
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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 FlaxModelTesterMixin:
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model_tester = None
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all_model_classes = ()
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is_encoder_decoder = False
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def _prepare_for_class(self, inputs_dict, model_class):
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inputs_dict = copy.deepcopy(inputs_dict)
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# hack for now until we have AutoModel classes
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if "ForMultipleChoice" in model_class.__name__:
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inputs_dict = {
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k: jnp.broadcast_to(v[:, None], (v.shape[0], self.model_tester.num_choices, v.shape[-1]))
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if isinstance(v, (jnp.ndarray, np.ndarray))
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else v
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for k, v in inputs_dict.items()
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}
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return inputs_dict
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def assert_almost_equals(self, a: np.ndarray, b: np.ndarray, tol: float):
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diff = np.abs((a - b)).max()
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self.assertLessEqual(diff, tol, f"Difference between torch and flax is {diff} (>= {tol}).")
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def test_model_outputs_equivalence(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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def set_nan_tensor_to_zero(t):
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t[t != t] = 0
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return t
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def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
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tuple_output = model(**tuple_inputs, return_dict=False, **additional_kwargs)
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dict_output = model(**dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
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def recursive_check(tuple_object, dict_object):
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if isinstance(tuple_object, (List, Tuple)):
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for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
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recursive_check(tuple_iterable_value, dict_iterable_value)
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elif tuple_object is None:
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return
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else:
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self.assert_almost_equals(
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set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), 1e-5
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)
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recursive_check(tuple_output, dict_output)
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for model_class in self.all_model_classes:
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model = model_class(config)
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs)
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tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
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dict_inputs = self._prepare_for_class(inputs_dict, model_class)
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check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
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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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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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pt_model = pt_model_class(config).eval()
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# Flax models don't use the `use_cache` option and cache is not returned as a default.
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# So we disable `use_cache` here for PyTorch model.
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pt_model.config.use_cache = False
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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
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with torch.no_grad():
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pt_outputs = pt_model(**pt_inputs).to_tuple()
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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, pt_outputs):
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self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
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with tempfile.TemporaryDirectory() as tmpdirname:
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pt_model.save_pretrained(tmpdirname)
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fx_model_loaded = model_class.from_pretrained(tmpdirname, from_pt=True)
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fx_outputs_loaded = fx_model_loaded(**prepared_inputs_dict).to_tuple()
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self.assertEqual(
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len(fx_outputs_loaded), len(pt_outputs), "Output lengths differ between Flax and PyTorch"
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)
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for fx_output_loaded, pt_output in zip(fx_outputs_loaded, pt_outputs):
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self.assert_almost_equals(fx_output_loaded, pt_output.numpy(), 4e-2)
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@is_pt_flax_cross_test
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def test_equivalence_flax_to_pt(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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# 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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pt_model = pt_model_class(config).eval()
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# Flax models don't use the `use_cache` option and cache is not returned as a default.
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# So we disable `use_cache` here for PyTorch model.
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pt_model.config.use_cache = False
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fx_model = model_class(config, dtype=jnp.float32)
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pt_model = load_flax_weights_in_pytorch_model(pt_model, fx_model.params)
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# make sure weights are tied in PyTorch
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pt_model.tie_weights()
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with torch.no_grad():
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pt_outputs = pt_model(**pt_inputs).to_tuple()
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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, pt_outputs):
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self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
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with tempfile.TemporaryDirectory() as tmpdirname:
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fx_model.save_pretrained(tmpdirname)
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pt_model_loaded = pt_model_class.from_pretrained(tmpdirname, from_flax=True)
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with torch.no_grad():
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pt_outputs_loaded = pt_model_loaded(**pt_inputs).to_tuple()
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self.assertEqual(
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len(fx_outputs), len(pt_outputs_loaded), "Output lengths differ between Flax and PyTorch"
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)
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for fx_output, pt_output in zip(fx_outputs, pt_outputs_loaded):
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self.assert_almost_equals(fx_output, pt_output.numpy(), 4e-2)
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def test_from_pretrained_save_pretrained(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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model = model_class(config)
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prepared_inputs_dict = self._prepare_for_class(inputs_dict, model_class)
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outputs = model(**prepared_inputs_dict).to_tuple()
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# verify that normal save_pretrained works as expected
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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model_loaded = model_class.from_pretrained(tmpdirname)
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outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()
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for output_loaded, output in zip(outputs_loaded, outputs):
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self.assert_almost_equals(output_loaded, output, 1e-3)
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# verify that save_pretrained for distributed training
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# with `params=params` works as expected
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname, params=model.params)
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model_loaded = model_class.from_pretrained(tmpdirname)
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outputs_loaded = model_loaded(**prepared_inputs_dict).to_tuple()
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for output_loaded, output in zip(outputs_loaded, outputs):
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self.assert_almost_equals(output_loaded, output, 1e-3)
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def test_save_load_from_base(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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base_class = FLAX_MODEL_MAPPING[config.__class__]
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for model_class in self.all_model_classes:
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if model_class == base_class:
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continue
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model = base_class(config)
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base_params = flatten_dict(unfreeze(model.params))
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# check that all base model weights are loaded correctly
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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head_model = model_class.from_pretrained(tmpdirname)
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base_param_from_head = flatten_dict(unfreeze(head_model.params[head_model.base_model_prefix]))
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for key in base_param_from_head.keys():
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max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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def test_save_load_to_base(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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base_class = FLAX_MODEL_MAPPING[config.__class__]
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for model_class in self.all_model_classes:
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if model_class == base_class:
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continue
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model = model_class(config)
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base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
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# check that all base model weights are loaded correctly
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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base_model = base_class.from_pretrained(tmpdirname)
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base_params = flatten_dict(unfreeze(base_model.params))
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for key in base_params_from_head.keys():
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max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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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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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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base_class = FLAX_MODEL_MAPPING[config.__class__]
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for model_class in self.all_model_classes:
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if model_class == base_class:
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continue
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model = base_class(config)
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base_params = flatten_dict(unfreeze(model.params))
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# convert Flax model to PyTorch model
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pt_model_class = getattr(transformers, base_class.__name__[4:]) # Skip the "Flax" at the beginning
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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, model.params)
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# check that all base model weights are loaded correctly
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with tempfile.TemporaryDirectory() as tmpdirname:
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# save pt model
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pt_model.save_pretrained(tmpdirname)
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head_model = model_class.from_pretrained(tmpdirname, from_pt=True)
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base_param_from_head = flatten_dict(unfreeze(head_model.params[head_model.base_model_prefix]))
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for key in base_param_from_head.keys():
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max_diff = (base_params[key] - base_param_from_head[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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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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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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base_class = FLAX_MODEL_MAPPING[config.__class__]
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for model_class in self.all_model_classes:
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if model_class == base_class:
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continue
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model = model_class(config)
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base_params_from_head = flatten_dict(unfreeze(model.params[model.base_model_prefix]))
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# convert Flax model to PyTorch model
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pt_model_class = getattr(transformers, model_class.__name__[4:]) # Skip the "Flax" at the beginning
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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, model.params)
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# check that all base model weights are loaded correctly
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with tempfile.TemporaryDirectory() as tmpdirname:
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pt_model.save_pretrained(tmpdirname)
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base_model = base_class.from_pretrained(tmpdirname, from_pt=True)
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base_params = flatten_dict(unfreeze(base_model.params))
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for key in base_params_from_head.keys():
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max_diff = (base_params[key] - base_params_from_head[key]).sum().item()
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self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
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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, attention_mask=None, **kwargs):
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return model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
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with self.subTest("JIT Enabled"):
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jitted_outputs = model_jitted(**prepared_inputs_dict).to_tuple()
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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).to_tuple()
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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_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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if model.config.is_encoder_decoder:
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expected_arg_names = [
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"input_ids",
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"attention_mask",
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"decoder_input_ids",
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"decoder_attention_mask",
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]
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self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
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else:
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expected_arg_names = ["input_ids", "attention_mask"]
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self.assertListEqual(arg_names[:2], expected_arg_names)
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def test_naming_convention(self):
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for model_class in self.all_model_classes:
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model_class_name = model_class.__name__
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module_class_name = (
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model_class_name[:-5] + "Module" if model_class_name[-5:] == "Model" else model_class_name + "Module"
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)
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bert_modeling_flax_module = __import__(model_class.__module__, fromlist=[module_class_name])
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module_cls = getattr(bert_modeling_flax_module, module_class_name)
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self.assertIsNotNone(module_cls)
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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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|
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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|
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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|
)
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self.assertEqual(len(hidden_states), expected_num_layers)
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|
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if hasattr(self.model_tester, "encoder_seq_length"):
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seq_length = self.model_tester.encoder_seq_length
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else:
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|
seq_length = self.model_tester.seq_length
|
|
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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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|
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if config.is_encoder_decoder:
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hidden_states = outputs.decoder_hidden_states
|
|
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self.assertIsInstance(hidden_states, (list, tuple))
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self.assertEqual(len(hidden_states), expected_num_layers)
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|
seq_len = getattr(self.model_tester, "seq_length", None)
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|
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len)
|
|
|
|
self.assertListEqual(
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|
list(hidden_states[0].shape[-2:]),
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|
[decoder_seq_length, self.model_tester.hidden_size],
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|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
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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|
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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
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
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
|
|
|
|
seq_length = getattr(self.model_tester, "seq_length", None)
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|
decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_length)
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|
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_length)
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|
decoder_key_length = getattr(self.model_tester, "decoder_key_length", decoder_seq_length)
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|
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
|
|
|
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.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
# check that output_attentions also work using config
|
|
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.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
out_len = len(outputs)
|
|
|
|
if self.is_encoder_decoder:
|
|
correct_outlen = 5
|
|
|
|
# Question Answering model returns start_logits and end_logits
|
|
if model_class in get_values(FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING):
|
|
correct_outlen += 1 # start_logits and end_logits instead of only 1 output
|
|
|
|
self.assertEqual(out_len, correct_outlen)
|
|
|
|
# decoder attentions
|
|
decoder_attentions = outputs.decoder_attentions
|
|
self.assertIsInstance(decoder_attentions, (list, tuple))
|
|
self.assertEqual(len(decoder_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(decoder_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, decoder_seq_length, decoder_key_length],
|
|
)
|
|
|
|
# cross attentions
|
|
cross_attentions = outputs.cross_attentions
|
|
self.assertIsInstance(cross_attentions, (list, tuple))
|
|
self.assertEqual(len(cross_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(cross_attentions[0].shape[-3:]),
|
|
[
|
|
self.model_tester.num_attention_heads,
|
|
decoder_seq_length,
|
|
encoder_key_length,
|
|
],
|
|
)
|
|
|
|
# Check attention is always last and order is fine
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = True
|
|
model = model_class(config)
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
if hasattr(self.model_tester, "num_hidden_states_types"):
|
|
added_hidden_states = self.model_tester.num_hidden_states_types
|
|
elif self.is_encoder_decoder:
|
|
added_hidden_states = 2
|
|
else:
|
|
added_hidden_states = 1
|
|
self.assertEqual(out_len + added_hidden_states, len(outputs))
|
|
|
|
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
|
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
|
|
def test_load_with_mismatched_shapes(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
if model_class not in get_values(FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING):
|
|
continue
|
|
|
|
with self.subTest(msg=f"Testing {model_class}"):
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model = model_class(config)
|
|
model.save_pretrained(tmp_dir)
|
|
|
|
# Fails when we don't set ignore_mismatched_sizes=True
|
|
with self.assertRaises(ValueError):
|
|
new_model = FlaxAutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
|
|
|
|
logger = logging.get_logger("transformers.modeling_flax_utils")
|
|
with CaptureLogger(logger) as cl:
|
|
new_model = FlaxAutoModelForSequenceClassification.from_pretrained(
|
|
tmp_dir, num_labels=42, ignore_mismatched_sizes=True
|
|
)
|
|
self.assertIn("the shapes did not match", cl.out)
|
|
|
|
logits = new_model(**inputs_dict)["logits"]
|
|
self.assertEqual(logits.shape[1], 42)
|
|
|
|
|
|
@require_flax
|
|
@is_staging_test
|
|
class FlaxModelPushToHubTester(unittest.TestCase):
|
|
@classmethod
|
|
def setUpClass(cls):
|
|
cls._api = HfApi(endpoint=ENDPOINT_STAGING)
|
|
cls._token = cls._api.login(username=USER, password=PASS)
|
|
|
|
@classmethod
|
|
def tearDownClass(cls):
|
|
try:
|
|
cls._api.delete_repo(token=cls._token, name="test-model-flax")
|
|
except HTTPError:
|
|
pass
|
|
|
|
try:
|
|
cls._api.delete_repo(token=cls._token, name="test-model-flax-org", organization="valid_org")
|
|
except HTTPError:
|
|
pass
|
|
|
|
def test_push_to_hub(self):
|
|
config = BertConfig(
|
|
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
|
|
)
|
|
model = FlaxBertModel(config)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(
|
|
os.path.join(tmp_dir, "test-model-flax"), push_to_hub=True, use_auth_token=self._token
|
|
)
|
|
|
|
new_model = FlaxBertModel.from_pretrained(f"{USER}/test-model-flax")
|
|
|
|
base_params = flatten_dict(unfreeze(model.params))
|
|
new_params = flatten_dict(unfreeze(new_model.params))
|
|
|
|
for key in base_params.keys():
|
|
max_diff = (base_params[key] - new_params[key]).sum().item()
|
|
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
|
|
|
|
def test_push_to_hub_in_organization(self):
|
|
config = BertConfig(
|
|
vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37
|
|
)
|
|
model = FlaxBertModel(config)
|
|
with tempfile.TemporaryDirectory() as tmp_dir:
|
|
model.save_pretrained(
|
|
os.path.join(tmp_dir, "test-model-flax-org"),
|
|
push_to_hub=True,
|
|
use_auth_token=self._token,
|
|
organization="valid_org",
|
|
)
|
|
|
|
new_model = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")
|
|
|
|
base_params = flatten_dict(unfreeze(model.params))
|
|
new_params = flatten_dict(unfreeze(new_model.params))
|
|
|
|
for key in base_params.keys():
|
|
max_diff = (base_params[key] - new_params[key]).sum().item()
|
|
self.assertLessEqual(max_diff, 1e-3, msg=f"{key} not identical")
|