GPU Allocator




RESIZABLE : TensorRT may call realloc() on this allocation


class tensorrt.IGpuAllocator(self: tensorrt.tensorrt.IGpuAllocator) → None

Application-implemented class for controlling allocation on the GPU.

__init__(self: tensorrt.tensorrt.IGpuAllocator) → None
allocate(self: tensorrt.tensorrt.IGpuAllocator, size: int, alignment: int, flags: int) → capsule

A callback implemented by the application to handle acquisition of GPU memory. If an allocation request of size 0 is made, None should be returned.

If an allocation request cannot be satisfied, None should be returned.

  • size – The size of the memory required.

  • alignment – The required alignment of memory. Alignment will be zero or a power of 2 not exceeding the alignment guaranteed by cudaMalloc. Thus this allocator can be safely implemented with cudaMalloc/cudaFree. An alignment value of zero indicates any alignment is acceptable.

  • flags – Allocation flags. See AllocatorFlag


The address of the allocated memory

free(self: tensorrt.tensorrt.IGpuAllocator, memory: capsule) → None

A callback implemented by the application to handle release of GPU memory.

TensorRT may pass a 0 to this function if it was previously returned by allocate().


memory – The memory address of the memory to release.

reallocate(self: tensorrt.tensorrt.IGpuAllocator, address: capsule, alignment: int, new_size: int) → capsule

A callback implemented by the application to resize an existing allocation.

Only allocations which were allocated with AllocatorFlag.RESIZABLE will be resized.

Options are one of: - resize in place leaving min(old_size, new_size) bytes unchanged and return the original address - move min(old_size, new_size) bytes to a new location of sufficient size and return its address - return nullptr, to indicate that the request could not be fulfilled.

If nullptr is returned, TensorRT will assume that resize() is not implemented, and that the allocation at address is still valid.

This method is made available for use cases where delegating the resize strategy to the application provides an opportunity to improve memory management. One possible implementation is to allocate a large virtual device buffer and progressively commit physical memory with cuMemMap. CU_MEM_ALLOC_GRANULARITY_RECOMMENDED is suggested in this case.

TensorRT may call realloc to increase the buffer by relatively small amounts.

  • address – the address of the original allocation.

  • alignment – The alignment used by the original allocation.

  • new_size – The new memory size required.


The address of the reallocated memory