# 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 BitsAndBytesConfig, is_torch_available from transformers.testing_utils import ( cleanup, is_flaky, require_bitsandbytes, require_flash_attn, require_torch, require_torch_accelerator, 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): if is_torch_available(): base_model_class = Ernie4_5_MoeModel @require_torch class Ernie4_5_MoeModelTest(CausalLMModelTest, unittest.TestCase): test_all_params_have_gradient = False model_tester_class = Ernie4_5_MoeModelTester @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") 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) @is_flaky(max_attempts=2) 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 = 3 config.output_router_logits = True input_ids = input_dict["input_ids"] attention_mask = input_ids.ne(config.pad_token_id).to(torch_device) model = Ernie4_5_MoeForCausalLM(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask) bs, seqlen = input_ids.shape self.assertEqual(result.router_logits[0].shape, (bs * seqlen, 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) # (This length is selected from experiments) pad_length = input_ids.shape[1] * 4 # Add padding tokens to input_ids padding_block = config.pad_token_id * 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(config.pad_token_id).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", quantization_config=BitsAndBytesConfig(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)