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transformers/tests/models/glm/test_modeling_glm.py

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+# Copyright 2024 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 Glm model."""
+
+import unittest
+
+import pytest
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, GlmConfig, is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    require_flash_attn,
+    require_torch,
+    require_torch_large_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        GlmForCausalLM,
+        GlmForSequenceClassification,
+        GlmForTokenClassification,
+        GlmModel,
+    )
+
+
+@require_torch
+class GlmModelTester(CausalLMModelTester):
+    config_class = GlmConfig
+    if is_torch_available():
+        base_model_class = GlmModel
+        causal_lm_class = GlmForCausalLM
+        sequence_class = GlmForSequenceClassification
+        token_class = GlmForTokenClassification
+
+
+@require_torch
+class GlmModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (GlmModel, GlmForCausalLM, GlmForSequenceClassification, GlmForTokenClassification)
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": GlmModel,
+            "text-classification": GlmForSequenceClassification,
+            "token-classification": GlmForTokenClassification,
+            "text-generation": GlmForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_headmasking = False
+    test_pruning = False
+    model_tester_class = GlmModelTester
+
+
+@slow
+@require_torch_large_accelerator
+class GlmIntegrationTest(unittest.TestCase):
+    input_text = ["Hello I am doing", "Hi today"]
+    model_id = "THUDM/glm-4-9b"
+    revision = "refs/pr/15"
+
+    def test_model_9b_fp16(self):
+        EXPECTED_TEXTS = [
+            "Hello I am doing a project on the history of the internetSolution:\n\nStep 1: Introduction\nThe history of the",
+            "Hi today I am going to show you how to make a simple and easy to make a DIY paper flower.",
+        ]
+
+        model = AutoModelForCausalLM.from_pretrained(self.model_id, dtype=torch.float16, revision=self.revision).to(
+            torch_device
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id, revision=self.revision)
+        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_TEXTS)
+
+    def test_model_9b_bf16(self):
+        EXPECTED_TEXTS = [
+            "Hello I am doing a project on the history of the internetSolution:\n\nStep 1: Introduction\nThe history of the",
+            "Hi today I am going to show you how to make a simple and easy to make a DIY paper flower.",
+        ]
+
+        model = AutoModelForCausalLM.from_pretrained(self.model_id, dtype=torch.bfloat16, revision=self.revision).to(
+            torch_device
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id, revision=self.revision)
+        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_TEXTS)
+
+    def test_model_9b_eager(self):
+        expected_texts = Expectations({
+            (None, None): [
+                "Hello I am doing a project on the history of the internetSolution:\n\nStep 1: Introduction\nThe history of the",
+                "Hi today I am going to show you how to make a simple and easy to make a DIY paper flower.",
+            ],
+            ("cuda", 8): [
+                'Hello I am doing a project on the history of the internetSolution:\n\nStep 1: Introduction\nThe history of the',
+                'Hi today I am going to show you how to make a simple and easy to make a DIY paper lantern.',
+            ],
+            ("rocm", (9, 5)) : [
+                "Hello I am doing a project on the history of the internetSolution:\n\nStep 1: Introduction\nThe history of the",
+                "Hi today I am going to show you how to make a simple and easy to make a paper airplane. First",
+            ]
+        })  # fmt: skip
+        EXPECTED_TEXTS = expected_texts.get_expectation()
+
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            dtype=torch.bfloat16,
+            attn_implementation="eager",
+            revision=self.revision,
+        )
+        model.to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id, revision=self.revision)
+        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_TEXTS)
+
+    def test_model_9b_sdpa(self):
+        EXPECTED_TEXTS = [
+            "Hello I am doing a project on the history of the internetSolution:\n\nStep 1: Introduction\nThe history of the",
+            "Hi today I am going to show you how to make a simple and easy to make a DIY paper flower.",
+        ]
+
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            dtype=torch.bfloat16,
+            attn_implementation="sdpa",
+            revision=self.revision,
+        )
+        model.to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id, revision=self.revision)
+        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_TEXTS)
+
+    @require_flash_attn
+    @pytest.mark.flash_attn_test
+    def test_model_9b_flash_attn(self):
+        EXPECTED_TEXTS = [
+            "Hello I am doing a project on the history of the internetSolution:\n\nStep 1: Introduction\nThe history of the",
+            "Hi today I am going to show you how to make a simple and easy to make a DIY paper flower.",
+        ]
+
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            dtype=torch.bfloat16,
+            attn_implementation="flash_attention_2",
+            revision=self.revision,
+        )
+        model.to(torch_device)
+
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id, revision=self.revision)
+        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_TEXTS)