mirror of
https://github.com/vllm-project/vllm.git
synced 2026-06-06 00:16:14 +00:00
Chris Leonard
77 lines
3.0 KiB
Plaintext
77 lines
3.0 KiB
Plaintext
#include <torch/csrc/stable/tensor.h>
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#include <torch/csrc/stable/ops.h>
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#include <torch/csrc/stable/accelerator.h>
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#include <torch/headeronly/core/ScalarType.h>
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#include <torch/csrc/stable/device.h>
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#include <torch/csrc/stable/c/shim.h>
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#include <torch/headeronly/version.h>
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#include <cuda_runtime.h>
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#include <array>
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#include <optional>
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// This function assumes that `cpu_tensor` is a CPU tensor,
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// and that UVA (Unified Virtual Addressing) is enabled.
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torch::stable::Tensor get_cuda_view_from_cpu_tensor(
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torch::stable::Tensor& cpu_tensor) {
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STD_TORCH_CHECK(cpu_tensor.device().is_cpu(), "Input tensor must be on CPU");
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const auto dtype = cpu_tensor.scalar_type();
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const auto layout = cpu_tensor.layout();
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const torch::stable::Device cuda_dev(torch::headeronly::DeviceType::CUDA);
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// handle empty tensor
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if (cpu_tensor.numel() == 0) {
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return torch::stable::empty(cpu_tensor.sizes(), dtype, layout, cuda_dev);
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}
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std::array<StableIValue, 2> is_pinned_stack{
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torch::stable::detail::from(cpu_tensor),
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torch::stable::detail::from(std::nullopt)};
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TORCH_ERROR_CODE_CHECK(torch_call_dispatcher(
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"aten::is_pinned", "", is_pinned_stack.data(), TORCH_ABI_VERSION));
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if (torch::stable::detail::to<bool>(is_pinned_stack[0])) {
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// If CPU tensor is pinned, directly get the device pointer.
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void* host_ptr = const_cast<void*>(cpu_tensor.mutable_data_ptr());
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void* device_ptr = nullptr;
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cudaError_t err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
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STD_TORCH_CHECK(err == cudaSuccess, "cudaHostGetDevicePointer failed: ",
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cudaGetErrorString(err));
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return torch::stable::from_blob(
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device_ptr, cpu_tensor.sizes(), cpu_tensor.strides(), cuda_dev, dtype,
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[base = cpu_tensor](void*) {}); // keep cpu tensor alive
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}
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// If CPU tensor is not pinned, allocate a new pinned memory buffer.
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torch::stable::Tensor contiguous_cpu = torch::stable::contiguous(cpu_tensor);
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size_t nbytes = contiguous_cpu.numel() * contiguous_cpu.element_size();
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void* host_ptr = nullptr;
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cudaError_t err = cudaHostAlloc(&host_ptr, nbytes, cudaHostAllocMapped);
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if (err != cudaSuccess) {
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STD_TORCH_CHECK(false, "cudaHostAlloc failed: ", cudaGetErrorString(err));
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}
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err = cudaMemcpy(host_ptr, contiguous_cpu.const_data_ptr(), nbytes,
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cudaMemcpyDefault);
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if (err != cudaSuccess) {
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cudaFreeHost(host_ptr);
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STD_TORCH_CHECK(false, "cudaMemcpy failed: ", cudaGetErrorString(err));
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}
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void* device_ptr = nullptr;
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err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
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if (err != cudaSuccess) {
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cudaFreeHost(host_ptr);
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STD_TORCH_CHECK(
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false, "cudaHostGetDevicePointer failed: ", cudaGetErrorString(err));
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}
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auto deleter = [host_ptr](void*) { cudaFreeHost(host_ptr); };
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return torch::stable::from_blob(device_ptr, contiguous_cpu.sizes(),
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contiguous_cpu.strides(), cuda_dev,
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contiguous_cpu.scalar_type(), deleter);
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}
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