example: using nvrtc kernel for aot plugin

examples/dynamo/nvrtc_aot_plugin.py

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+"""
+Minimal reproducible example demonstrating TensorRT fp16 custom_op() issue.
+
+This module shows the bug where torch_tensorrt.dynamo.conversion.plugins.custom_op()
+fails to compile operations that use fp16 (half-precision) tensors.
+
+The issue occurs because the JIT plugin generator doesn't properly declare format
+support for fp16 data types in the generated TensorRT plugin.
+"""
+
+from typing import List, Tuple, Union
+
+import torch
+
+import torch_tensorrt
+
+# CUDA kernel source (NVRTC) used by the torch custom op
+# Note: TensorRT passes args as: inputs, extra_args, outputs
+
+cu_code = """
+// Simple pointwise Sigmoid kernel: f(x) = 1 / (1 + exp(-x))
+extern "C" __global__ void pointwise_sigmoid_kernel_nvrtc(const float* __restrict__ input,
+                                                          const int size,
+                                                          float* __restrict__ output) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (idx < size) {
+        const float x = input[idx];
+        // use fast device intrinsic to avoid headers
+        output[idx] = 1.0f / (1.0f + __expf(-x));
+    }
+}
+"""
+
+# Prepare NVRTC program, kernel, and stream once (simple eager path)
+from cuda.core.experimental import Device as _CudaDevice
+from cuda.core.experimental import LaunchConfig as _LaunchConfig
+from cuda.core.experimental import Program as _CudaProgram
+from cuda.core.experimental import ProgramOptions as _CudaProgramOptions
+from cuda.core.experimental import launch as _cuda_launch
+
+_cuda_device = _CudaDevice()
+_cuda_device.set_current()
+_cuda_stream = _cuda_device.create_stream()
+_program_options = _CudaProgramOptions(
+    std="c++17",
+    arch=f"sm_{_cuda_device.arch}",
+    include_path=["/usr/local/cuda/include"],
+)
+_program = _CudaProgram(cu_code, code_type="c++", options=_program_options)
+_module = _program.compile("ptx", name_expressions=("pointwise_sigmoid_kernel_nvrtc",))
+_kernel = _module.get_kernel("pointwise_sigmoid_kernel_nvrtc")
+
+# Eager torch custom_op implemented using the CUDA kernel above (no Triton)
+
+
+# ============================================================================
+# Custom Op Registration
+# ============================================================================
+
+
+@torch.library.custom_op("pointwise_sigmoid_ops::pointwise_sigmoid", mutates_args=())  # type: ignore[misc]
+def pointwise_sigmoid(X: torch.Tensor) -> torch.Tensor:
+    assert X.is_cuda, "Tensor must be on CUDA device."
+    assert X.dtype == torch.float32, "For this test, expected float32 input"
+
+    Y = torch.empty_like(X)
+    N = int(X.numel())
+
+    block = 256
+
+    grid_x = max(1, (N + block - 1) // block)
+    config = _LaunchConfig(grid=(grid_x), block=(block))
+
+    # Use PyTorch's current stream by wrapping it for cuda.core
+    class _PyTorchStreamWrapper:
+        def __init__(self, pt_stream):
+            self.pt_stream = pt_stream
+
+        def __cuda_stream__(self):
+            stream_id = self.pt_stream.cuda_stream
+            return (0, stream_id)
+
+    pt_stream = torch.cuda.current_stream()
+    s = _cuda_device.create_stream(_PyTorchStreamWrapper(pt_stream))
+
+    # Launch kernel with raw pointers as in cuda.core example
+    # Note: argument order is input, size,   (matching TensorRT's convention)
+
+    _cuda_launch(
+        s,
+        config,
+        _kernel,
+        X.data_ptr(),
+        N,
+        Y.data_ptr(),
+    )
+
+    return Y
+
+
+@torch.library.register_fake("pointwise_sigmoid_ops::pointwise_sigmoid")
+def _(input: torch.Tensor) -> torch.Tensor:
+    """Fake implementation for TorchDynamo tracing of base operation."""
+    return torch.empty_like(input)
+
+
+# ============================================================================
+# TensorRT Wrapper with custom_op() - THIS FAILS WITH FP16
+# ============================================================================
+
+import tensorrt.plugin as trtp
+from cuda.core.experimental import Device, LaunchConfig, Program, ProgramOptions
+
+
+@trtp.register("pointwise_sigmoid_ops::pointwise_sigmoid")
+def sigmoid_plugin_desc(input: trtp.TensorDesc) -> Tuple[trtp.TensorDesc]:
+    return (input.like(),)
+
+
+@trtp.autotune("pointwise_sigmoid_ops::pointwise_sigmoid")
+def sigmoid_autotune(
+    input: trtp.TensorDesc,
+    outputs: Tuple[trtp.TensorDesc],
+) -> List[trtp.AutoTuneCombination]:
+    # Match float32 path; add FP16 if you want both
+    return [trtp.AutoTuneCombination("FP32, FP32", "LINEAR")]
+
+
+@trtp.aot_impl("pointwise_sigmoid_ops::pointwise_sigmoid")
+def sigmoid_aot_nvrtc_impl(
+    input: trtp.TensorDesc,
+    outputs: Tuple[trtp.TensorDesc],
+    tactic: int,
+) -> Tuple[
+    Union[str, bytes], Union[str, bytes], trtp.KernelLaunchParams, trtp.SymExprs
+]:
+
+    compiled_kernel = _module.code.decode("utf-8")
+    print(type(compiled_kernel))
+    print(compiled_kernel)
+
+    # import pdb; pdb.set_trace()
+
+    N = input.shape_expr.numel()
+    launch_params = trtp.KernelLaunchParams()
+    block = 256
+    launch_params.grid_x = trtp.cdiv(N, block)
+    launch_params.block_x = block
+    launch_params.shared_mem = 0
+
+    extra_args = trtp.SymIntExprs(1)
+    extra_args[0] = trtp.SymInt32(N)
+
+    return (
+        "pointwise_sigmoid_kernel_nvrtc",
+        compiled_kernel,
+        launch_params,
+        extra_args,
+    )
+
+
+torch_tensorrt.dynamo.conversion.plugins.generate_plugin_converter(
+    "pointwise_sigmoid_ops::pointwise_sigmoid",
+    supports_dynamic_shapes=True,
+    requires_output_allocator=False,
+)
+
+
+# ============================================================================
+# Test Model
+# ============================================================================
+
+
+class PointwiseSigmoidModel_WithTRTWrapper(torch.nn.Module):
+    """
+    Test model that uses the TRT wrapper with custom_op() registration.
+
+    When compiled with torch_tensorrt.compile() using fp16 inputs, this will
+    fail with: "could not find any supported formats consistent with input/output
+    data types"
+    """
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+
+        z = torch.ops.pointwise_sigmoid_ops.pointwise_sigmoid(input)
+        return z
+
+
+if __name__ == "__main__":
+    model = PointwiseSigmoidModel_WithTRTWrapper().to("cuda").eval()
+    input = torch.randn(1, 1024, device="cuda", dtype=torch.float32)
+
+    print(torch.sigmoid(input))
+
+    print(model(input))
+
+    with torch_tensorrt.logging.debug():
+        trt_inputs = [input]
+        model_trt = torch_tensorrt.compile(
+            model,
+            inputs=trt_inputs,
+            enabled_precisions={torch.float32},
+            min_block_size=1,
+        )
+        print("Model compiled successfully!")
+        print("Running inference with compiled model...")
+        print("Compiled model output:")
+        print(model_trt(input))
+        print("Original model output:")
+        print(model(input))
+        with torch.no_grad():
+            for i in range(10):
+                res = model_trt(input)
+                assert torch.allclose(
+                    res, model(input), rtol=1e-2, atol=1e-2
+                ), "Results do not match!"
+
+    # print("Inference successful!")