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transformers/tests/models/qwen2_moe/test_modeling_qwen2_moe.py

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+# 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 unittest
+
+import pytest
+
+from transformers import AutoTokenizer, Qwen2MoeConfig, is_torch_available, set_seed
+from transformers.testing_utils import (
+    cleanup,
+    require_flash_attn,
+    require_torch,
+    require_torch_gpu,
+    run_first,
+    run_test_using_subprocess,
+    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):
+    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 = 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([[-4.2106, -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, 0:10]
+        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 = 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)
+
+    # 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_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",
+            dtype=torch.float16,
+            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())
+
+    @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 = 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())
+
+        # 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())
+
+        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"
+        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: 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 = 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)