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121 lines
4.8 KiB
Python
121 lines
4.8 KiB
Python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch Llama4 model."""
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import unittest
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from transformers import is_torch_available
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from transformers.testing_utils import (
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require_read_token,
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require_torch_large_gpu,
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slow,
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torch_device,
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)
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if is_torch_available():
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import torch
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from transformers import (
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Llama4ForConditionalGeneration,
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Llama4Processor,
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)
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@slow
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@require_torch_large_gpu
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@require_read_token
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class Llama4IntegrationTest(unittest.TestCase):
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model_id = "ll-re/Llama-4-17B-Omni-Instruct"
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# This variable is used to determine which CUDA device are we using for our runners (A10 or T4)
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# Depending on the hardware we get different logits / generations
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cuda_compute_capability_major_version = None
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@classmethod
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def setUpClass(cls):
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if is_torch_available() and torch.cuda.is_available():
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# 8 is for A100 / A10 and 7 for T4
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cls.cuda_compute_capability_major_version = torch.cuda.get_device_capability()[0]
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cls.model = Llama4ForConditionalGeneration.from_pretrained(
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"ll-re/Llama-4-17B-Omni-Instruct", device_map="auto", torch_dtype=torch.float32
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)
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def setUp(self):
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self.processor = Llama4Processor.from_pretrained("ll-re/Llama-4-17B-Omni-Instruct", padding_side="left")
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url = "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png"
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self.messages = [
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{"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
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{
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"role": "user",
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"content": [
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{"type": "image", "url": url},
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{"type": "text", "text": "What is shown in this image?"},
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],
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},
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]
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def test_model_17b_16e_fp16(self):
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EXPECTED_TEXT = [
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"The capital of France is Paris, which is located in the north-central part of the country. Paris is known for its iconic landmarks such as the",
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"Roses are red, violets are blue, and this poem is about you. Roses are red, violets are blue, and I love",
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]
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messages = [
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{"role": "user", "content": "Who are you?"},
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]
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inputs = self.processor.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
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).to(torch_device)
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output = self.model.generate(**inputs, max_new_tokens=100)
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output_text = self.processor.batch_decode(output, skip_special_tokens=True)
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print(output_text)
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self.assertEqual(output_text, EXPECTED_TEXT)
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def test_model_17b_16e_batch(self):
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messages_2 = [
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{"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]},
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"url": "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
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},
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{"type": "image", "url": "https://www.ilankelman.org/stopsigns/australia.jpg"},
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{"type": "text", "text": "Are these images identical?"},
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],
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},
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]
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inputs = self.processor.apply_chat_template(
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[self.messages, messages_2],
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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padding=True,
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add_generation_prompt=True,
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).to(torch_device)
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output = self.model.generate(**inputs, max_new_tokens=30, do_sample=False)
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output_text = self.processor.batch_decode(output, skip_special_tokens=True)
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EXPECTED_TEXTS = [
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'user\nYou are a helpful assistant.\n\n\n\n\n\nWhat is shown in this image?\nmodel\nCertainly! \n\nThe image shows a brown cow standing on a sandy beach with clear turquoise water and a blue sky in the background. It looks like',
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"user\nYou are a helpful assistant.\n\n\n\n\n\n\n\n\n\nAre these images identical?\nmodel\nNo, these images are not identical. \n\nHere's a breakdown of the differences:\n\n* **Image 1:** Shows a cow"
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] # fmt: skip
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self.assertEqual(output_text, EXPECTED_TEXTS)
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