scripts: add allocate_vram script

Allocates the specified amount of VRAM, or allocates enough VRAM such that you have the specified amount of VRAM free.

Useful to simulate an environment with a specific amount of VRAM.

scripts/allocate_vram.py

@@ -0,0 +1,63 @@
+import argparse
+
+import torch
+
+
+def display_vram_usage():
+    """Displays the total, allocated, and free VRAM on the current CUDA device."""
+
+    assert torch.cuda.is_available(), "CUDA is not available"
+    device = torch.device("cuda")
+
+    total_vram = torch.cuda.get_device_properties(device).total_memory
+    allocated_vram = torch.cuda.memory_allocated(device)
+    free_vram = total_vram - allocated_vram
+
+    print(f"Total VRAM: {total_vram / (1024 * 1024 * 1024):.2f} GB")
+    print(f"Allocated VRAM: {allocated_vram / (1024 * 1024 * 1024):.2f} GB")
+    print(f"Free VRAM: {free_vram / (1024 * 1024 * 1024):.2f} GB")
+
+
+def allocate_vram(target_gb: float, target_free: bool = False):
+    """Allocates VRAM on the current CUDA device. After allocation, the script will pause until the user presses Enter
+    or ends the script, at which point the VRAM will be released.
+
+    Args:
+        target_gb (float): Amount of VRAM to allocate in GB.
+        target_free (bool, optional): Instead of allocating <target_gb> VRAM, enough VRAM will be allocated so the system has <target_gb> of VRAM free. For example, if <target_gb> is 2 GB, the script will allocate VRAM until the free VRAM is 2 GB.
+    """
+    assert torch.cuda.is_available(), "CUDA is not available"
+    device = torch.device("cuda")
+
+    if target_free:
+        total_vram = torch.cuda.get_device_properties(device).total_memory
+        free_vram = total_vram - torch.cuda.memory_allocated(device)
+        target_free_bytes = target_gb * 1024 * 1024 * 1024
+        bytes_to_allocate = free_vram - target_free_bytes
+
+        if bytes_to_allocate <= 0:
+            print(f"Already at or below the target free VRAM of {target_gb} GB")
+            return
+    else:
+        bytes_to_allocate = target_gb * 1024 * 1024 * 1024
+
+    # FloatTensor (4 bytes per element)
+    _tensor = torch.empty(int(bytes_to_allocate / 4), dtype=torch.float, device="cuda")
+
+    display_vram_usage()
+
+    input("Press Enter to release VRAM allocation and exit...")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Allocate VRAM for testing purposes. Only works on CUDA devices.")
+    parser.add_argument("target_gb", type=float, help="Amount of VRAM to allocate in GB.")
+    parser.add_argument(
+        "--target-free",
+        action="store_true",
+        help="Instead of allocating <target_gb> VRAM, enough VRAM will be allocated so the system has <target_gb> of VRAM free. For example, if <target_gb> is 2 GB, the script will allocate VRAM until the free VRAM is 2 GB.",
+    )
+
+    args = parser.parse_args()
+
+    allocate_vram(target_gb=args.target_gb, target_free=args.target_free)