mirror of
https://github.com/huggingface/transformers.git
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200 lines
8.4 KiB
Python
200 lines
8.4 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 unittest
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from packaging import version
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from transformers import AutoTokenizer, ModernBertDecoderConfig, is_torch_available
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from transformers.testing_utils import (
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require_torch,
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slow,
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)
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from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
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from ...test_modeling_common import _config_zero_init
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if is_torch_available():
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import torch
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from transformers import (
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ModernBertDecoderForCausalLM,
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ModernBertDecoderForSequenceClassification,
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ModernBertDecoderModel,
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)
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class ModernBertDecoderModelTester(CausalLMModelTester):
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config_class = ModernBertDecoderConfig
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if is_torch_available():
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base_model_class = ModernBertDecoderModel
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causal_lm_class = ModernBertDecoderForCausalLM
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@require_torch
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class ModernBertDecoderModelTest(CausalLMModelTest, unittest.TestCase):
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all_model_classes = (
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(ModernBertDecoderModel, ModernBertDecoderForCausalLM, ModernBertDecoderForSequenceClassification)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{
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"feature-extraction": ModernBertDecoderModel,
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"text-generation": ModernBertDecoderForCausalLM,
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"text-classification": ModernBertDecoderForSequenceClassification,
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}
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if is_torch_available()
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else {}
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)
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test_head_masking = False
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test_pruning = False
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model_tester_class = ModernBertDecoderModelTester
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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# The classifier.weight from ModernBertDecoderForSequenceClassification
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# is initialized without `initializer_range`, so it's not set to ~0 via the _config_zero_init
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if param.requires_grad and not (
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name == "classifier.weight" and model_class in [ModernBertDecoderForSequenceClassification]
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):
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data = torch.flatten(param.data)
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n_elements = torch.numel(data)
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# skip 2.5% of elements on each side to avoid issues caused by `nn.init.trunc_normal_` described in
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# https://github.com/huggingface/transformers/pull/27906#issuecomment-1846951332
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n_elements_to_skip_on_each_side = int(n_elements * 0.025)
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data_to_check = torch.sort(data).values
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if n_elements_to_skip_on_each_side > 0:
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data_to_check = data_to_check[n_elements_to_skip_on_each_side:-n_elements_to_skip_on_each_side]
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self.assertIn(
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((data_to_check.mean() * 1e9).round() / 1e9).item(),
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[0.0, 1.0],
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
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)
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@slow
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@require_torch
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class ModernBertDecoderIntegrationTest(unittest.TestCase):
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def test_inference_causal_lm(self):
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if version.parse(torch.__version__) < version.parse("2.4.0"):
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self.skipTest(reason="This test requires torch >= 2.4 to run.")
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model = ModernBertDecoderForCausalLM.from_pretrained(
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"blab-jhu/test-32m-dec", reference_compile=False, attn_implementation="sdpa"
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)
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tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
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inputs = tokenizer("Paris is the capital of", return_tensors="pt")
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with torch.no_grad():
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output = model(**inputs)[0]
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expected_shape = torch.Size((1, 6, model.config.vocab_size))
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self.assertEqual(output.shape, expected_shape)
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[[[-8.0183, -7.1578, -0.4453], [-6.2909, -6.1557, 4.9063], [-6.7689, -5.8068, 6.1078]]]
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)
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torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
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def test_inference_no_head(self):
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if version.parse(torch.__version__) < version.parse("2.4.0"):
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self.skipTest(reason="This test requires torch >= 2.4 to run.")
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model = ModernBertDecoderModel.from_pretrained(
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"blab-jhu/test-32m-dec", reference_compile=False, attn_implementation="sdpa"
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)
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tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
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inputs = tokenizer("Paris is the capital of", return_tensors="pt")
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with torch.no_grad():
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output = model(**inputs)[0]
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expected_shape = torch.Size((1, 6, model.config.hidden_size))
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self.assertEqual(output.shape, expected_shape)
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# compare the actual values for a slice.
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expected_slice = torch.tensor(
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[[[0.3151, -0.6417, -0.7027], [-0.7834, -1.5810, 0.4576], [1.0614, -0.7268, -0.0871]]]
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)
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torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
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def test_generation(self):
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if version.parse(torch.__version__) < version.parse("2.4.0"):
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self.skipTest(reason="This test requires torch >= 2.4 to run.")
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model = ModernBertDecoderForCausalLM.from_pretrained("blab-jhu/test-32m-dec")
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tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
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inputs = tokenizer("The weather today is", return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=10, do_sample=False)
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output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
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# Check that we got some reasonable output
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self.assertEqual(len(output_text), 1)
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self.assertTrue(len(output_text[0]) > len("The weather today is"))
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def test_sliding_window_long_context(self):
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"""
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Test that ModernBertDecoder works with sliding window attention for longer sequences.
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"""
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if version.parse(torch.__version__) < version.parse("2.4.0"):
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self.skipTest(reason="This test requires torch >= 2.4 to run.")
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model = ModernBertDecoderForCausalLM.from_pretrained("blab-jhu/test-32m-dec")
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tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
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# Create a longer input to test sliding window attention
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long_input = "This is a test. " * 50 # Repeat to make it longer
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inputs = tokenizer(long_input, return_tensors="pt", truncation=True, max_length=512)
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outputs = model.generate(**inputs, max_new_tokens=20, do_sample=False)
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# Check that generation worked with longer context
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self.assertEqual(outputs.shape[0], 1)
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self.assertGreater(outputs.shape[1], inputs["input_ids"].shape[1])
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def test_sequence_classification(self):
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"""
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Test that ModernBertDecoderForSequenceClassification works correctly.
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"""
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if version.parse(torch.__version__) < version.parse("2.4.0"):
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self.skipTest(reason="This test requires torch >= 2.4 to run.")
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model = ModernBertDecoderForSequenceClassification.from_pretrained("blab-jhu/test-32m-dec", num_labels=2)
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tokenizer = AutoTokenizer.from_pretrained("blab-jhu/test-32m-dec")
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# Test with sample input
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inputs = tokenizer("This is a positive example.", return_tensors="pt")
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with torch.no_grad():
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outputs = model(**inputs)
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# Check output shape
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expected_shape = (1, 2) # batch_size=1, num_labels=2
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self.assertEqual(outputs.logits.shape, expected_shape)
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# Test with labels
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labels = torch.tensor([1])
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outputs_with_loss = model(**inputs, labels=labels)
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# Check that loss is computed
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self.assertIsNotNone(outputs_with_loss.loss)
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self.assertTrue(isinstance(outputs_with_loss.loss.item(), float))
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