diff --git a/tests/models/qwen2_moe/test_modeling_qwen2_moe.py b/tests/models/qwen2_moe/test_modeling_qwen2_moe.py index 782b7ec0f198..02eb4dfb4643 100644 --- a/tests/models/qwen2_moe/test_modeling_qwen2_moe.py +++ b/tests/models/qwen2_moe/test_modeling_qwen2_moe.py @@ -13,18 +13,18 @@ # 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, + cleanup, require_flash_attn, require_torch, require_torch_gpu, + run_first, + run_test_using_subprocess, slow, torch_device, ) @@ -145,31 +145,45 @@ def test_load_balancing_loss(self): @require_torch class Qwen2MoeIntegrationTest(unittest.TestCase): + model = None + + @classmethod + def get_model(cls): + if cls.model is None: + cls.model = Qwen2MoeForCausalLM.from_pretrained( + "Qwen/Qwen1.5-MoE-A2.7B", device_map="auto", dtype=torch.float16 + ) + return cls.model + + @classmethod + def tearDownClass(cls): + if cls.model is not None: + del cls.model + cleanup(torch_device, gc_collect=True) + + def tearDown(self): + cleanup(torch_device, gc_collect=True) + @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") + 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([[-4.2125, -3.6416, -4.9136, -4.3005, -4.9938, -3.4393, -3.5195, -4.1621]]) + EXPECTED_MEAN = torch.tensor([[-4.2106, -3.6411, -4.9111, -4.2840, -4.9950, -3.4438, -3.5262, -4.1624]]) 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() + # slicing logits[0, 0, 0:10] + EXPECTED_SLICE = torch.tensor([2.3008, -0.6777, -0.1287, -1.4043, -1.7393, -1.7627, -2.0547, -2.4414, -3.0332, -2.1406]) # fmt: skip + torch.testing.assert_close(out[0, 0, :10], EXPECTED_SLICE, rtol=1e-4, atol=1e-4) @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") + model = self.get_model() input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs @@ -177,22 +191,21 @@ def test_model_a2_7b_generation(self): 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 + # run this test as the first test within this class and run with a separate process + # (to avoid potential CPU memory issue caused by `device_map="auto"`.) + @run_first + @run_test_using_subprocess @slow @require_flash_attn @pytest.mark.flash_attn_test - def test_model_a2_7b_long_prompt(self): + def test_model_a2_7b_long_prompt_flash_attn(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, + dtype=torch.float16, attn_implementation="flash_attention_2", ) input_ids = torch.tensor([input_ids]).to(model.model.embed_tokens.weight.device) @@ -206,21 +219,12 @@ def test_model_a2_7b_long_prompt(self): 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 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", - ) + 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()) @@ -232,10 +236,7 @@ def test_model_a2_7b_long_prompt_sdpa(self): 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() + cleanup(torch_device, gc_collect=True) 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" @@ -251,14 +252,12 @@ def test_model_a2_7b_long_prompt_sdpa(self): @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" + "To be or not to be, that is the question. Whether 'tis nobler in the mind to suffer the sl" ) 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", dtype=torch.float16) - assistant_model = Qwen2MoeForCausalLM.from_pretrained( - "Qwen/Qwen1.5-MoE-A2.7B", device_map="auto", dtype=torch.float16 - ) + assistant_model = model input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.model.embed_tokens.weight.device) # greedy generation outputs @@ -268,7 +267,3 @@ def test_speculative_generation(self): ) 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()