chore: import upstream snapshot with attribution

tests/model_executor/model_loader/fastsafetensors_loader/test_fastsafetensors_loader.py

@@ -0,0 +1,28 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+
+from vllm import SamplingParams
+from vllm.platforms import current_platform
+
+test_model = "openai-community/gpt2"
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+# Create a sampling params object.
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95, seed=0)
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="fastsafetensors requires NVIDIA/AMD GPUs",
+)
+def test_model_loader_download_files(vllm_runner):
+    with vllm_runner(test_model, load_format="fastsafetensors") as llm:
+        deserialized_outputs = llm.generate(prompts, sampling_params)
+        assert deserialized_outputs

tests/model_executor/model_loader/fastsafetensors_loader/test_weight_utils.py

@@ -0,0 +1,49 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import glob
+import tempfile
+
+import huggingface_hub.constants
+import pytest
+import torch
+
+from vllm.model_executor.model_loader.weight_utils import (
+    download_weights_from_hf,
+    fastsafetensors_weights_iterator,
+    safetensors_weights_iterator,
+)
+from vllm.platforms import current_platform
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda_alike(),
+    reason="fastsafetensors requires NVIDIA/AMD GPUs",
+)
+@pytest.mark.parametrize("queue_size", [0, 1])
+def test_fastsafetensors_model_loader(monkeypatch, queue_size):
+    monkeypatch.setenv("VLLM_FASTSAFETENSORS_QUEUE_SIZE", str(queue_size))
+    with tempfile.TemporaryDirectory() as tmpdir:
+        huggingface_hub.constants.HF_HUB_OFFLINE = False
+        download_weights_from_hf(
+            "openai-community/gpt2", allow_patterns=["*.safetensors"], cache_dir=tmpdir
+        )
+        safetensors = glob.glob(f"{tmpdir}/**/*.safetensors", recursive=True)
+        assert len(safetensors) > 0
+
+        fastsafetensors_tensors = {}
+        hf_safetensors_tensors = {}
+
+        for name, tensor in fastsafetensors_weights_iterator(safetensors, True):
+            fastsafetensors_tensors[name] = tensor
+
+        for name, tensor in safetensors_weights_iterator(safetensors, True):
+            hf_safetensors_tensors[name] = tensor
+
+        assert len(fastsafetensors_tensors) == len(hf_safetensors_tensors)
+
+        for name, fastsafetensors_tensor in fastsafetensors_tensors.items():
+            fastsafetensors_tensor = fastsafetensors_tensor.to("cpu")
+            assert fastsafetensors_tensor.dtype == hf_safetensors_tensors[name].dtype
+            assert fastsafetensors_tensor.shape == hf_safetensors_tensors[name].shape
+            assert torch.all(fastsafetensors_tensor.eq(hf_safetensors_tensors[name]))