# 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 Qwen2MoE model.""" import gc import unittest import pytest from transformers import AutoTokenizer, Qwen2MoeConfig, is_torch_available, set_seed from transformers.testing_utils import ( backend_empty_cache, require_bitsandbytes, require_flash_attn, require_torch, require_torch_gpu, require_torch_sdpa, slow, torch_device, ) if is_torch_available(): import torch from transformers import ( Qwen2MoeForCausalLM, Qwen2MoeForQuestionAnswering, Qwen2MoeForSequenceClassification, Qwen2MoeForTokenClassification, Qwen2MoeModel, ) from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester class Qwen2MoeModelTester(CausalLMModelTester): config_class = Qwen2MoeConfig if is_torch_available(): base_model_class = Qwen2MoeModel causal_lm_class = Qwen2MoeForCausalLM sequence_class = Qwen2MoeForSequenceClassification token_class = Qwen2MoeForTokenClassification question_answering_class = Qwen2MoeForQuestionAnswering @require_torch class Qwen2MoeModelTest(CausalLMModelTest, unittest.TestCase): all_model_classes = ( ( Qwen2MoeModel, Qwen2MoeForCausalLM, Qwen2MoeForSequenceClassification, Qwen2MoeForTokenClassification, Qwen2MoeForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": Qwen2MoeModel, "text-classification": Qwen2MoeForSequenceClassification, "token-classification": Qwen2MoeForTokenClassification, "text-generation": Qwen2MoeForCausalLM, "question-answering": Qwen2MoeForQuestionAnswering, } if is_torch_available() else {} ) test_headmasking = False test_pruning = False test_all_params_have_gradient = False model_tester_class = Qwen2MoeModelTester # 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="Qwen2Moe 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 = Qwen2MoeForCausalLM(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()) @require_torch class Qwen2MoeIntegrationTest(unittest.TestCase): @slow def test_model_a2_7b_logits(self): input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338] model = Qwen2MoeForCausalLM.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", device_map="auto") 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([[-4.2125, -3.6416, -4.9136, -4.3005, -4.9938, -3.4393, -3.5195, -4.1621]]) 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([2.3013, -0.6595, -0.1389, -1.4095, -1.7381, -1.7609, -2.0449, -2.4289, -3.0271, -2.1351, -0.6568, -4.6012, -1.9102, -0.7475, -3.1377, 4.6904, 7.1936, 7.0991, 6.4414, 6.1720, 6.2617, 5.8751, 5.6997, 5.6011, 5.5828, -3.9505, -0.5384, -0.3392, 1.2445, 2.0714]) # fmt: skip print(out[0, 0, :30]) torch.testing.assert_close(out[0, 0, :30], EXPECTED_SLICE, rtol=1e-4, atol=1e-4) del model backend_empty_cache(torch_device) gc.collect() @slow def test_model_a2_7b_generation(self): EXPECTED_TEXT_COMPLETION = """To be or not to be, that is the question. This is the question that has been asked by many people over the""" prompt = "To be or not to" tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", use_fast=False) model = Qwen2MoeForCausalLM.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", device_map="auto") 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) del model backend_empty_cache(torch_device) gc.collect() @require_bitsandbytes @slow @require_flash_attn @pytest.mark.flash_attn_test def test_model_a2_7b_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 = Qwen2MoeForCausalLM.from_pretrained( "Qwen/Qwen1.5-MoE-A2.7B", 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()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) del assistant_model del model backend_empty_cache(torch_device) gc.collect() @slow @require_torch_sdpa def test_model_a2_7b_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 = Qwen2MoeForCausalLM.from_pretrained( "Qwen/Qwen1.5-MoE-A2.7B", device_map="auto", attn_implementation="sdpa", ) 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()) # Assisted generation assistant_model = model assistant_model.generation_config.num_assistant_tokens = 2 assistant_model.generation_config.num_assistant_tokens_schedule = "constant" generated_ids = assistant_model.generate(input_ids, max_new_tokens=4, temperature=0) self.assertEqual(EXPECTED_OUTPUT_TOKEN_IDS, generated_ids[0][-2:].tolist()) del assistant_model backend_empty_cache(torch_device) gc.collect() EXPECTED_TEXT_COMPLETION = """To be or not to be, that is the question. This is the question that has been asked by many people over the""" prompt = "To be or not to" tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", 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, that is the question.\nThe answer is to be, of course. But what does it" ) prompt = "To be or not to" tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B", use_fast=False) model = Qwen2MoeForCausalLM.from_pretrained( "Qwen/Qwen1.5-MoE-A2.7B", device_map="auto", torch_dtype=torch.float16 ) assistant_model = Qwen2MoeForCausalLM.from_pretrained( "Qwen/Qwen1.5-MoE-A2.7B", device_map="auto", torch_dtype=torch.float16 ) 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) del model backend_empty_cache(torch_device) gc.collect()