tests/models/esm/test_modeling_esm.py - main - githubmirror/transformers

tests/models/esm/test_modeling_esm.py
495 строк19 KB
Bruno Alvisio
Add THD support in ESM (#44145)
09 апр 2026, 16:54
09 апр 2026, 16:543170e36
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Testing suite for the PyTorch ESM model."""

import tempfile
import unittest

import pytest

from transformers import BitsAndBytesConfig, DataCollatorWithFlattening, EsmConfig, is_torch_available, set_seed
from transformers.testing_utils import (
    TestCasePlus,
    is_flaky,
    require_bitsandbytes,
    require_flash_attn,
    require_torch,
    require_torch_accelerator,
    slow,
    torch_device,
)

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
    from transformers.models.esm.modeling_esm import (
        EsmEmbeddings,
        create_position_ids_from_input_ids,
    )


# copied from tests.test_modeling_roberta
class EsmModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        seq_length=7,
        is_training=False,
        use_input_mask=True,
        use_token_type_ids=False,
        use_labels=True,
        vocab_size=33,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=16,
        type_sequence_label_size=2,
        initializer_range=0.02,
        num_labels=3,
        num_choices=4,
        scope=None,
        position_embedding_type="rotary",
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.seq_length = seq_length
        self.is_training = is_training
        self.use_input_mask = use_input_mask
        self.use_token_type_ids = use_token_type_ids
        self.use_labels = use_labels
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.num_labels = num_labels
        self.num_choices = num_choices
        self.scope = scope
        self.position_embedding_type = position_embedding_type

    def prepare_config_and_inputs(self):
        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)

        input_mask = None
        if self.use_input_mask:
            input_mask = random_attention_mask([self.batch_size, self.seq_length])

        sequence_labels = None
        token_labels = None
        choice_labels = None
        if self.use_labels:
            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
            choice_labels = ids_tensor([self.batch_size], self.num_choices)

        config = self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels

    def get_config(self):
        return EsmConfig(
            vocab_size=self.vocab_size,
            hidden_size=self.hidden_size,
            pad_token_id=1,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            max_position_embeddings=self.max_position_embeddings,
            type_vocab_size=self.type_vocab_size,
            initializer_range=self.initializer_range,
            position_embedding_type=self.position_embedding_type,
        )

    def create_and_check_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels):
        model = EsmModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask)
        result = model(input_ids)
        result = model(input_ids)

        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))

    def create_and_check_for_masked_lm(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        model = EsmForMaskedLM(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))

    def create_and_check_for_token_classification(
        self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
    ):
        config.num_labels = self.num_labels
        model = EsmForTokenClassification(config=config)
        model.to(torch_device)
        model.eval()
        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))

    def create_and_check_forward_and_backwards(
        self,
        config,
        input_ids,
        input_mask,
        sequence_labels,
        token_labels,
        choice_labels,
        gradient_checkpointing=False,
    ):
        model = EsmForMaskedLM(config)
        if gradient_checkpointing:
            model.gradient_checkpointing_enable()
        model.to(torch_device)
        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
        result.loss.backward()

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        (
            config,
            input_ids,
            input_mask,
            sequence_labels,
            token_labels,
            choice_labels,
        ) = config_and_inputs
        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict


@require_torch
class EsmModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    test_mismatched_shapes = False

    all_model_classes = (
        (
            EsmForMaskedLM,
            EsmModel,
            EsmForSequenceClassification,
            EsmForTokenClassification,
        )
        if is_torch_available()
        else ()
    )
    pipeline_model_mapping = (
        {
            "feature-extraction": EsmModel,
            "fill-mask": EsmForMaskedLM,
            "text-classification": EsmForSequenceClassification,
            "token-classification": EsmForTokenClassification,
            "zero-shot": EsmForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    test_sequence_classification_problem_types = True
    model_split_percents = [0.5, 0.8, 0.9]

    def setUp(self):
        self.model_tester = EsmModelTester(self)
        self.config_tester = ConfigTester(self, config_class=EsmConfig, hidden_size=48)

    def test_config(self):
        self.config_tester.run_common_tests()

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_for_masked_lm(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)

    def test_for_token_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)

    def test_esm_gradient_checkpointing(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs, gradient_checkpointing=True)

    @require_flash_attn
    @require_torch_accelerator
    @pytest.mark.flash_attn_test
    def test_model_generation_flash_attn_with_packing(self):
        """Test that packing two sequences produces the same per-sample outputs as running
        them in a batched run.
        """
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        config = config_and_inputs[0]
        config.position_embedding_type = "rotary"
        config.attn_implementation = "flash_attention_2"

        model = EsmModel(config=config).to(dtype=torch.bfloat16, device=torch_device).eval()

        seq_len_a = 3
        seq_len_b = 4

        input_ids_a = ids_tensor([1, seq_len_a], config.vocab_size)
        input_ids_b = ids_tensor([1, seq_len_b], config.vocab_size)

        # --- Batched run ----
        max_len = max(seq_len_a, seq_len_b)
        pad_a = max_len - seq_len_a
        pad_b = max_len - seq_len_b

        batched_input_ids = torch.cat(
            [
                torch.nn.functional.pad(input_ids_a, (0, pad_a), value=config.pad_token_id),
                torch.nn.functional.pad(input_ids_b, (0, pad_b), value=config.pad_token_id),
            ],
            dim=0,
        ).to(torch_device)

        batched_attention_mask = torch.cat(
            [
                torch.nn.functional.pad(torch.ones(1, seq_len_a, dtype=torch.long), (0, pad_a)),
                torch.nn.functional.pad(torch.ones(1, seq_len_b, dtype=torch.long), (0, pad_b)),
            ],
            dim=0,
        ).to(torch_device)

        with torch.no_grad():
            result_batched = model(batched_input_ids, attention_mask=batched_attention_mask)

        # --- Packed run ----
        collator = DataCollatorWithFlattening(
            return_position_ids=True,
            return_flash_attn_kwargs=True,
        )

        features = [
            {"input_ids": input_ids_a.squeeze(0)},
            {"input_ids": input_ids_b.squeeze(0)},
        ]

        packed = collator(features)

        packed_kwargs = {
            "input_ids": packed["input_ids"].to(torch_device),
            "attention_mask": None,
            "position_ids": packed["position_ids"].to(torch_device),
            "cu_seq_lens_q": packed["cu_seq_lens_q"].to(torch_device),
            "cu_seq_lens_k": packed["cu_seq_lens_k"].to(torch_device),
            "max_length_q": packed["max_length_q"],
            "max_length_k": packed["max_length_k"],
        }

        with torch.no_grad():
            result_packed = model(**packed_kwargs)

        # Compare per-sample outputs
        torch.testing.assert_close(
            result_batched.last_hidden_state[0, :seq_len_a],
            result_packed.last_hidden_state[0, :seq_len_a],
            atol=1e-5,
            rtol=1e-5,
        )
        torch.testing.assert_close(
            result_batched.last_hidden_state[1, :seq_len_b],
            result_packed.last_hidden_state[0, seq_len_a:],
            atol=1e-5,
            rtol=1e-5,
        )

    @slow
    def test_model_from_pretrained(self):
        model_name = "facebook/esm2_t6_8M_UR50D"
        model = EsmModel.from_pretrained(model_name)
        self.assertIsNotNone(model)

    def test_create_position_ids_respects_padding_index(self):
        """This is a regression test for https://github.com/huggingface/transformers/issues/1761

        The position ids should be masked with the embedding object's padding index. Therefore, the
        first available non-padding position index is EsmEmbeddings.padding_idx + 1
        """
        config = self.model_tester.prepare_config_and_inputs()[0]
        model = EsmEmbeddings(config=config)

        input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]])
        expected_positions = torch.as_tensor(
            [
                [
                    0 + model.padding_idx + 1,
                    1 + model.padding_idx + 1,
                    2 + model.padding_idx + 1,
                    model.padding_idx,
                ]
            ]
        )
        position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx)
        self.assertEqual(position_ids.shape, expected_positions.shape)
        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

    def test_create_position_ids_from_inputs_embeds(self):
        """This is a regression test for https://github.com/huggingface/transformers/issues/1761

        The position ids should be masked with the embedding object's padding index. Therefore, the
        first available non-padding position index is EsmEmbeddings.padding_idx + 1
        """
        config = self.model_tester.prepare_config_and_inputs()[0]
        embeddings = EsmEmbeddings(config=config)

        inputs_embeds = torch.empty(2, 4, 30)
        expected_single_positions = [
            0 + embeddings.padding_idx + 1,
            1 + embeddings.padding_idx + 1,
            2 + embeddings.padding_idx + 1,
            3 + embeddings.padding_idx + 1,
        ]
        expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions])
        position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds)
        self.assertEqual(position_ids.shape, expected_positions.shape)
        self.assertTrue(torch.all(torch.eq(position_ids, expected_positions)))

    @unittest.skip(reason="Esm does not support embedding resizing")
    def test_resize_embeddings_untied(self):
        pass

    @unittest.skip(reason="Esm does not support embedding resizing")
    def test_resize_tokens_embeddings(self):
        pass

    @require_flash_attn
    @require_torch_accelerator
    @pytest.mark.flash_attn_test
    @is_flaky()
    @slow
    def test_flash_attn_2_equivalence(self):
        for model_class in self.all_model_classes:
            if not model_class._supports_flash_attn:
                self.skipTest(reason="Model does not support Flash Attention 2")

            # Set seed for deterministic test - ensures reproducible model initialization and inputs
            set_seed(42)
            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
            model = model_class(config)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(tmpdirname)
                model_fa = model_class.from_pretrained(
                    tmpdirname, dtype=torch.float16, attn_implementation="flash_attention_2"
                )
                model_fa.to(torch_device)

                model = model_class.from_pretrained(tmpdirname, dtype=torch.float16, attn_implementation="eager")
                model.to(torch_device)

                dummy_input = inputs_dict[model_class.main_input_name]
                dummy_input = dummy_input.to(torch_device)
                outputs = model(dummy_input, output_hidden_states=True)
                outputs_fa = model_fa(dummy_input, output_hidden_states=True)

                logits = outputs.hidden_states[-1]
                logits_fa = outputs_fa.hidden_states[-1]

                torch.testing.assert_close(logits_fa, logits, atol=1e-2, rtol=1e-3)

    @unittest.skip("ESM embeddings are scaled due to token dropout so the test does not apply")
    def test_inputs_embeds_matches_input_ids(self):
        pass


@slow
@require_torch
class EsmModelIntegrationTest(TestCasePlus):
    def test_inference_masked_lm(self):
        with torch.no_grad():
            model = EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
            model.eval()
            input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
            output = model(input_ids)[0]

            vocab_size = 33

            expected_shape = torch.Size((1, 6, vocab_size))
            self.assertEqual(output.shape, expected_shape)

            expected_slice = torch.tensor(
                [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]
            )
            torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)

    def test_inference_no_head(self):
        with torch.no_grad():
            model = EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
            model.eval()

            input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
            output = model(input_ids)[0]
            # compare the actual values for a slice.
            expected_slice = torch.tensor(
                [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]
            )
            torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)

    def test_inv_freq_preserves_checkpoint_precision(self):
        """The checkpoint's inv_freq was saved after an fp16 cast, so it differs from a fresh
        float32 computation but matches exactly when the fresh values are round-tripped through fp16."""
        model_from_ckpt = EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
        config = EsmConfig.from_pretrained("facebook/esm2_t6_8M_UR50D")
        model_fresh = EsmModel(config)

        inv_freq_ckpt = model_from_ckpt.rotary_embeddings.inv_freq
        inv_freq_fresh = model_fresh.rotary_embeddings.inv_freq

        self.assertFalse(torch.equal(inv_freq_ckpt, inv_freq_fresh))
        self.assertTrue(torch.equal(inv_freq_ckpt, inv_freq_fresh.to(torch.float16).float()))

    @require_bitsandbytes
    def test_inference_bitsandbytes(self):
        model = EsmForMaskedLM.from_pretrained(
            "facebook/esm2_t36_3B_UR50D", quantization_config=BitsAndBytesConfig(load_in_8bit=True)
        )

        input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]).to(model.device)
        # Just test if inference works
        with torch.no_grad():
            _ = model(input_ids)[0]

        model = EsmForMaskedLM.from_pretrained(
            "facebook/esm2_t36_3B_UR50D", quantization_config=BitsAndBytesConfig(load_in_4bit=True)
        )

        input_ids = torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]).to(model.device)
        # Just test if inference works
        _ = model(input_ids)[0]