TheRock/compiler/amd-llvm/mlir/test/Examples/NVGPU/Ch0.py

# RUN: env SUPPORT_LIB=%mlir_cuda_runtime \
# RUN: sh -c 'if [[ "%mlir_run_cuda_sm90_tests" == "1" ]]; \
# RUN: then %PYTHON %s | FileCheck %s; \
# RUN: else export MLIR_NVDSL_PRINT_IR=1; \
# RUN: %PYTHON %s | FileCheck %s --check-prefix=DUMPIR; fi'


# ===----------------------------------------------------------------------===//
#  Chapter 0 : Hello World
# ===----------------------------------------------------------------------===//
#
# This program demonstrates Hello World:
#   1. Build MLIR function with arguments
#   2. Build MLIR GPU kernel
#   3. Print from a GPU thread
#   4. Pass arguments, JIT compile and run the MLIR function
#
# ===----------------------------------------------------------------------===//


from mlir.dialects import gpu
from tools.nvdsl import *


# 1. The decorator generates a MLIR func.func.
# Everything inside the Python function becomes the body of the func.
# The decorator also translates `alpha` to an `index` type.
@NVDSL.mlir_func
def main(alpha):
    # 2. The decorator generates a MLIR gpu.launch.
    # Everything inside the Python function becomes the body of the gpu.launch.
    # This allows for late outlining of the GPU kernel, enabling optimizations
    # like constant folding from host to device.
    @NVDSL.mlir_gpu_launch(grid=(1, 1, 1), block=(4, 1, 1))
    def kernel():
        tidx = gpu.thread_id(gpu.Dimension.x)
        # + operator generates arith.addi
        myValue = alpha + tidx
        # Print from a GPU thread
        gpu.printf("GPU thread %llu has %llu\n", tidx, myValue)

    # 3. Call the GPU kernel
    kernel()


alpha = 100
# 4. The `mlir_func` decorator JIT compiles the IR and executes the MLIR function.
main(alpha)

# CHECK: GPU thread 0 has 100
# CHECK: GPU thread 1 has 101
# CHECK: GPU thread 2 has 102
# CHECK: GPU thread 3 has 103

# DUMPIR:   func.func @main(%arg0: index) attributes {llvm.emit_c_interface} {
# DUMPIR:     %[[C0_I32:.*]] = arith.constant 0 : i32
# DUMPIR:     %[[C1:.*]] = arith.constant 1 : index
# DUMPIR:     %[[C1_0:.*]] = arith.constant 1 : index
# DUMPIR:     %[[C1_1:.*]] = arith.constant 1 : index
# DUMPIR:     %[[C4:.*]] = arith.constant 4 : index
# DUMPIR:     %[[C1_2:.*]] = arith.constant 1 : index
# DUMPIR:     %[[C1_3:.*]] = arith.constant 1 : index
# DUMPIR:     gpu.launch blocks(%arg1, %arg2, %arg3) in (%arg7 = %[[C1]], %arg8 = %[[C1_0]], %arg9 = %[[C1_1]]) threads(%arg4, %arg5, %arg6) in (%arg10 = %[[C4]], %arg11 = %[[C1_2]], %arg12 = %[[C1_3]]) dynamic_shared_memory_size %[[C0_I32]] {
# DUMPIR:       %[[TIDX:.*]] = gpu.thread_id x
# DUMPIR:       %[[MYVAL:.*]] = arith.addi %arg0, %[[TIDX]] : index
# DUMPIR:       gpu.printf "GPU thread %llu has %llu\0A", %[[TIDX]], %[[MYVAL]] : index, index
# DUMPIR:       gpu.terminator
# DUMPIR:     }
# DUMPIR:     return
# DUMPIR:   }