init

transformers/tests/models/glm4/test_modeling_glm4.py

@@ -0,0 +1,232 @@
+# coding=utf-8
+# 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 Glm4 model."""
+
+import unittest
+
+import pytest
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, Glm4Config, is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_flash_attn,
+    require_torch,
+    require_torch_large_accelerator,
+    require_torch_large_gpu,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Glm4ForCausalLM,
+        Glm4ForSequenceClassification,
+        Glm4ForTokenClassification,
+        Glm4Model,
+    )
+
+
+class Glm4ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        config_class = Glm4Config
+        base_model_class = Glm4Model
+        causal_lm_class = Glm4ForCausalLM
+        sequence_classification_class = Glm4ForSequenceClassification
+        token_classification_class = Glm4ForTokenClassification
+
+
+@require_torch
+class Glm4ModelTest(CausalLMModelTest, unittest.TestCase):
+    model_tester_class = Glm4ModelTester
+    all_model_classes = (
+        (Glm4Model, Glm4ForCausalLM, Glm4ForSequenceClassification, Glm4ForTokenClassification)
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Glm4Model,
+            "text-classification": Glm4ForSequenceClassification,
+            "token-classification": Glm4ForTokenClassification,
+            "text-generation": Glm4ForCausalLM,
+            "zero-shot": Glm4ForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    _is_stateful = True
+    model_split_percents = [0.5, 0.6]
+
+
+@slow
+@require_torch_large_accelerator
+class Glm4IntegrationTest(unittest.TestCase):
+    input_text = ["Hello I am doing", "Hi today"]
+    model_id = "THUDM/GLM-4-9B-0414"
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_model_9b_fp16(self):
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("xpu", 3): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                ],
+                ("cuda", 7): [],
+                ("cuda", 8): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                ],
+            }
+        )
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+
+        model = AutoModelForCausalLM.from_pretrained(self.model_id, dtype=torch.float16).to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+        self.assertEqual(output_text, EXPECTED_TEXT)
+
+    def test_model_9b_bf16(self):
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("xpu", 3): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common mistakes that people make when they are learning English.",
+                ],
+                ("cuda", 7): [],
+                ("cuda", 8): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                ],
+            }
+        )
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+
+        model = AutoModelForCausalLM.from_pretrained(self.model_id, dtype=torch.bfloat16).to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+        self.assertEqual(output_text, EXPECTED_TEXT)
+
+    def test_model_9b_eager(self):
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("xpu", 3): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and who",
+                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                ],
+                ("cuda", 7): [],
+                ("cuda", 8): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                ],
+            }
+        )
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            dtype=torch.bfloat16,
+            attn_implementation="eager",
+        )
+        model.to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+        self.assertEqual(output_text, EXPECTED_TEXT)
+
+    def test_model_9b_sdpa(self):
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("xpu", 3): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common mistakes that people make when they are learning English.",
+                ],
+                ("cuda", 7): [],
+                ("cuda", 8): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                ],
+            }
+        )
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            dtype=torch.bfloat16,
+            attn_implementation="sdpa",
+        )
+        model.to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+        self.assertEqual(output_text, EXPECTED_TEXT)
+
+    @require_flash_attn
+    @require_torch_large_gpu
+    @pytest.mark.flash_attn_test
+    def test_model_9b_flash_attn(self):
+        EXPECTED_TEXTS = Expectations(
+            {
+                ("cuda", 7): [],
+                ("cuda", 8): [
+                    "Hello I am doing a project on the history of the internet and I need to know what the first website was and what",
+                    "Hi today I am going to tell you about the most common disease in the world. This disease is called diabetes",
+                ],
+            }
+        )
+        EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
+
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            dtype=torch.bfloat16,
+            attn_implementation="flash_attention_2",
+        )
+        model.to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        inputs = tokenizer(self.input_text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+
+        self.assertEqual(output_text, EXPECTED_TEXT)