# Copyright 2024 The Qwen team, Alibaba Group and 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 Qwen3MoE model.""" import unittest import pytest from transformers import AutoTokenizer, Qwen3MoeConfig, is_torch_available, set_seed from transformers.testing_utils import ( cleanup, require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, require_torch_large_accelerator, require_torch_multi_accelerator, require_torch_sdpa, slow, torch_device, ) if is_torch_available(): import torch from transformers import ( Qwen3ForQuestionAnswering, Qwen3MoeForCausalLM, Qwen3MoeForQuestionAnswering, Qwen3MoeForSequenceClassification, Qwen3MoeForTokenClassification, Qwen3MoeModel, ) from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester class Qwen3MoeModelTester(CausalLMModelTester): config_class = Qwen3MoeConfig if is_torch_available(): base_model_class = Qwen3MoeModel causal_lm_class = Qwen3MoeForCausalLM sequence_class = Qwen3MoeForSequenceClassification token_class = Qwen3MoeForTokenClassification question_answering_class = Qwen3MoeForQuestionAnswering @require_torch class Qwen3MoeModelTest(CausalLMModelTest, unittest.TestCase): all_model_classes = ( ( Qwen3MoeModel, Qwen3MoeForCausalLM, Qwen3MoeForSequenceClassification, Qwen3MoeForTokenClassification, Qwen3MoeForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": Qwen3MoeModel, "text-classification": Qwen3MoeForSequenceClassification, "token-classification": Qwen3MoeForTokenClassification, "text-generation": Qwen3MoeForCausalLM, "question-answering": Qwen3ForQuestionAnswering, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False test_all_params_have_gradient = False model_tester_class = Qwen3MoeModelTester # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146 def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): return True @require_flash_attn @require_torch_gpu @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): self.skipTest(reason="Qwen3Moe flash attention does not support right padding") # 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 = Qwen3MoeForCausalLM(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 Qwen3MoeIntegrationTest(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 = Qwen3MoeForCausalLM.from_pretrained( "Qwen/Qwen3-30B-A3B-Base", device_map="auto", load_in_4bit=True ) return cls.model @slow def test_model_15b_a2b_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = self.get_model() input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) with torch.no_grad(): out = model(input_ids).logits.float().cpu() # Expected mean on dim = -1 EXPECTED_MEAN = torch.tensor([[0.3244, 0.4406, 9.0972, 7.3597, 4.9985, 8.0314, 8.2148, 9.2134]]) torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, rtol=1e-2, atol=1e-2) # slicing logits[0, 0, 0:30] EXPECTED_SLICE = torch.tensor([6.8984, 4.8633, 4.7734, 4.5898, 2.5664, 2.9902, 4.8828, 5.9414, 4.6250, 3.0840, 5.1602, 6.0117, 4.9453, 5.3008, 3.3145, 11.3906, 12.8359, 12.4844, 11.2891, 11.0547, 11.0391, 10.3359, 10.3438, 10.2578, 10.7969, 5.9688, 3.7676, 5.5938, 5.3633, 5.8203]) # fmt: skip torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-4, atol=1e-4) @slow def test_model_15b_a2b_generation(self): EXPECTED_TEXT_COMPLETION = "To be or not to be: the role of the cell cycle in the regulation of apoptosis.\nThe cell cycle is a highly" prompt = "To be or not to" tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Base", use_fast=False) model = self.get_model() input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) @require_bitsandbytes @slow @require_flash_attn @pytest.mark.flash_attn_test def test_model_15b_a2b_long_prompt(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # An input with 4097 tokens that is above the size of the sliding window input_ids = [1] + [306, 338] * 2048 model = Qwen3MoeForCausalLM.from_pretrained( "Qwen/Qwen3-30B-A3B-Base", device_map="auto", load_in_4bit=True, attn_implementation="flash_attention_2", ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) @slow @require_torch_sdpa def test_model_15b_a2b_long_prompt_sdpa(self): EXPECTED_OUTPUT_TOKEN_IDS = [306, 338] # An input with 4097 tokens that is above the size of the sliding window input_ids = [1] + [306, 338] * 2048 model = self.get_model() input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) EXPECTED_TEXT_COMPLETION = "To be or not to be: the role of the cell cycle in the regulation of apoptosis.\nThe cell cycle is a highly" prompt = "To be or not to" tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Base", use_fast=False) input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text) @slow def test_speculative_generation(self): EXPECTED_TEXT_COMPLETION = ( "To be or not to be: the role of the liver in the pathogenesis of obesity and type 2 diabetes.\nThe" ) prompt = "To be or not to" tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Base", use_fast=False) model = self.get_model() assistant_model = model input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs set_seed(0) generated_ids = model.generate( input_ids, max_new_tokens=20, do_sample=True, temperature=0.3, assistant_model=assistant_model ) text = tokenizer.decode(generated_ids[0], skip_special_tokens=True) self.assertEqual(EXPECTED_TEXT_COMPLETION, text)