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transformers/tests/models/starcoder2/test_modeling_starcoder2.py

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+# Copyright 2024 BigCode 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 Starcoder2 model."""
+
+import unittest
+
+import pytest
+
+from transformers import Starcoder2Config, is_torch_available
+from transformers.testing_utils import (
+    Expectations,
+    require_bitsandbytes,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        AutoTokenizer,
+        Starcoder2ForCausalLM,
+        Starcoder2ForSequenceClassification,
+        Starcoder2ForTokenClassification,
+        Starcoder2Model,
+    )
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class Starcoder2ModelTester(CausalLMModelTester):
+    config_class = Starcoder2Config
+    if is_torch_available():
+        base_model_class = Starcoder2Model
+        causal_lm_class = Starcoder2ForCausalLM
+        sequence_class = Starcoder2ForSequenceClassification
+        token_class = Starcoder2ForTokenClassification
+
+
+@require_torch
+class Starcoder2ModelTest(CausalLMModelTest, unittest.TestCase):
+    all_model_classes = (
+        (Starcoder2Model, Starcoder2ForCausalLM, Starcoder2ForSequenceClassification, Starcoder2ForTokenClassification)
+        if is_torch_available()
+        else ()
+    )
+    test_headmasking = False
+    test_pruning = False
+    model_tester_class = Starcoder2ModelTester
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": Starcoder2Model,
+            "text-classification": Starcoder2ForSequenceClassification,
+            "token-classification": Starcoder2ForTokenClassification,
+            "text-generation": Starcoder2ForCausalLM,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+
+@slow
+@require_torch_accelerator
+class Starcoder2IntegrationTest(unittest.TestCase):
+    def test_starcoder2_batched_generation_sdpa(self):
+        EXPECTED_TEXT = [
+            "Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
+            "def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
+        ]
+        model_id = "bigcode/starcoder2-7b"
+
+        model = Starcoder2ForCausalLM.from_pretrained(
+            model_id, dtype=torch.float16, device_map="auto", attn_implementation="sdpa"
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        text = ["Hello my name is Younes and", "def hello_world():"]
+        inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT, output_text)
+
+    def test_starcoder2_batched_generation_eager(self):
+        EXPECTED_TEXT = [
+            "Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
+            "def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
+        ]
+        model_id = "bigcode/starcoder2-7b"
+
+        model = Starcoder2ForCausalLM.from_pretrained(
+            model_id, dtype=torch.float16, device_map="auto", attn_implementation="eager"
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        text = ["Hello my name is Younes and", "def hello_world():"]
+        inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT, output_text)
+
+    @require_flash_attn
+    @pytest.mark.flash_attn_test
+    def test_starcoder2_batched_generation_fa2(self):
+        EXPECTED_TEXT = [
+            "Hello my name is Younes and I am a student at the University of Liverpool. I am currently studying for my MSc in Computer Science. I am interested in the field of Machine Learning and I am currently working on",
+            "def hello_world():\n\treturn 'Hello World!'\n\n@app.route('/hello/<name>')\ndef hello_name(name):\n\treturn 'Hello %s!' % name\n\n@app",
+        ]
+        model_id = "bigcode/starcoder2-7b"
+
+        model = Starcoder2ForCausalLM.from_pretrained(
+            model_id, dtype=torch.float16, device_map="auto", attn_implementation="flash_attention_2"
+        )
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        text = ["Hello my name is Younes and", "def hello_world():"]
+        inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT, output_text)
+
+    @require_bitsandbytes
+    def test_starcoder2_batched_generation_4bit(self):
+        expectations = Expectations(
+            {
+                (None, None): [
+                    'Hello my name is Younes and I am a student at the University of Maryland. I am currently working on a project that is related to the topic of "How to make a game". I am currently working on a project',
+                    'def hello_world():\n\treturn "Hello World"\n\n@app.route(\'/hello/<name>\')\ndef hello_name(name):\n\treturn "Hello " + name\n\n@app.route',
+                ],
+                ("cuda", 8): [
+                    "Hello my name is Younes and I am a student at the University of Maryland. I am currently working on a project that is aimed at creating a new way of learning. I am hoping to create a new way of",
+                    'def hello_world():\n\treturn "Hello World"\n\n@app.route(\'/hello/<name>\')\ndef hello_name(name):\n\treturn "Hello " + name\n\n@app.route',
+                ],
+            }
+        )
+        EXPECTED_TEXT = expectations.get_expectation()
+
+        model_id = "bigcode/starcoder2-7b"
+
+        model = Starcoder2ForCausalLM.from_pretrained(model_id, load_in_4bit=True)
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        text = ["Hello my name is Younes and", "def hello_world():"]
+        inputs = tokenizer(text, return_tensors="pt", padding=True).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=40, do_sample=False)
+        output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT, output_text)