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transformers/tests/models/ernie4_5_moe/test_modeling_ernie4_5_moe.py

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+# Copyright 2025 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 Ernie4.5 MoE model."""
+
+import tempfile
+import unittest
+
+import pytest
+
+from transformers import Ernie4_5_MoeConfig, is_torch_available
+from transformers.testing_utils import (
+    cleanup,
+    is_flaky,
+    require_bitsandbytes,
+    require_flash_attn,
+    require_torch,
+    require_torch_gpu,
+    require_torch_large_accelerator,
+    require_torch_multi_accelerator,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        AutoTokenizer,
+        Ernie4_5_MoeForCausalLM,
+        Ernie4_5_MoeModel,
+    )
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class Ernie4_5_MoeModelTester(CausalLMModelTester):
+    config_class = Ernie4_5_MoeConfig
+    if is_torch_available():
+        base_model_class = Ernie4_5_MoeModel
+        causal_lm_class = Ernie4_5_MoeForCausalLM
+
+
+@require_torch
+class Ernie4_5_MoeModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (
+            Ernie4_5_MoeModel,
+            Ernie4_5_MoeForCausalLM,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Ernie4_5_MoeModel,
+            "text-generation": Ernie4_5_MoeForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_headmasking = False
+    test_pruning = False
+    test_all_params_have_gradient = False
+    model_tester_class = Ernie4_5_MoeModelTester
+
+    @require_flash_attn
+    @require_torch_gpu
+    @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")
+
+            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.bfloat16, attn_implementation="flash_attention_2"
+                )
+                model_fa.to(torch_device)
+
+                model = model_class.from_pretrained(tmpdirname, dtype=torch.bfloat16, 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]
+
+                # higher tolerance, not sure where it stems from
+                assert torch.allclose(logits_fa, logits, atol=1e-2, rtol=1e-2)
+
+    # Ignore copy
+    def test_load_balancing_loss(self):
+        r"""
+        Let's make sure we can actually compute the loss and do a backward on it.
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        config.num_experts = 8
+        config.expert_interval = 2
+        config.output_router_logits = True
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        model = Ernie4_5_MoeForCausalLM(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask)
+        self.assertEqual(result.router_logits[0].shape, (91, config.num_experts))
+        torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)
+
+        # First, we make sure that adding padding tokens doesn't change the loss
+        # loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding)
+        pad_length = 1000
+        # Add padding tokens (assume that pad_token_id=1) to input_ids
+        padding_block = torch.ones(input_ids.shape[0], pad_length, dtype=torch.int32).to(torch_device)
+        padded_input_ids = torch.cat((padding_block, input_ids), dim=1)  # this is to simulate padding to the left
+        padded_attention_mask = padded_input_ids.ne(1).to(torch_device)
+
+        padded_result = model(padded_input_ids, attention_mask=padded_attention_mask)
+        torch.testing.assert_close(result.aux_loss.cpu(), padded_result.aux_loss.cpu(), rtol=1e-4, atol=1e-4)
+
+        # We make sure that the loss of including padding tokens != the loss without padding tokens
+        # if attention_mask=None --> we don't exclude padding tokens
+        include_padding_result = model(padded_input_ids, attention_mask=None)
+
+        # This is to mimic torch.testing.assert_not_close
+        self.assertNotAlmostEqual(include_padding_result.aux_loss.item(), result.aux_loss.item())
+
+
+# Run on runners with larger accelerators (for example A10 instead of T4) with a lot of CPU RAM (e.g. g5-12xlarge)
+@require_torch_multi_accelerator
+@require_torch_large_accelerator
+@require_torch
+class Ernie4_5_MoeIntegrationTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model = None
+
+    @classmethod
+    def tearDownClass(cls):
+        del cls.model
+        cleanup(torch_device, gc_collect=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @classmethod
+    def get_model(cls):
+        if cls.model is None:
+            cls.model = Ernie4_5_MoeForCausalLM.from_pretrained(
+                "baidu/ERNIE-4.5-21B-A3B-PT",
+                device_map="auto",
+                load_in_4bit=True,
+            )
+
+        return cls.model
+
+    @require_bitsandbytes
+    @slow
+    def test_model_21b_a3b_generation(self):
+        EXPECTED_TEXT_COMPLETION = "User: Hey, are you conscious? Can you talk to me?\nAssistant:  I don't have consciousness in the way humans do. I'm a text-based AI created to process and generate responses based on patterns in data."  # fmt: skip
+
+        model = self.get_model()
+        tokenizer = AutoTokenizer.from_pretrained("baidu/ERNIE-4.5-21B-A3B-PT", revision="refs/pr/11")
+        prompt = "Hey, are you conscious? Can you talk to me?"
+        messages = [{"role": "user", "content": prompt}]
+        text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+        generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=32,
+            do_sample=False,
+        )
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True).strip("\n")
+        self.assertEqual(EXPECTED_TEXT_COMPLETION, text)