[GPU] Add basic GPU support and example

examples/BuddyGPU/.gitignore

@@ -1,3 +1,4 @@
 log.mlir
 log.ll
 log.s
+matmul-cubin.mlir

examples/BuddyGPU/README.md

@@ -0,0 +1,40 @@
+# Buddy GPU Example
+This example demonstrates how to use the Buddy GPU to run a simple single-kernel program.
+
+## Matmul
+The example program is a simple matrix multiplication kernel. The linalg definition is in the `matmul.mlir` file. 
+A transform sequence is in `transform.mlir` to optimize this kernel and prepare it for execution on the GPU.
+The `matmul-cubin.mlir` provides a lowered file, in case the pipeline is not working.
+
+Run the following command to compile and run the program:
+```
+  make buddy-gpu-matmul
+  python run-module-gpu.py --source matmul.mlir --target matmul-cubin.mlir --llvm_dir ../../llvm
+```
+
+The result should be:
+```
+[[502.9141  499.7761  511.35623 ... 500.9083  505.25574 511.03818]
+ [499.57034 494.8066  506.427   ... 492.7868  497.22513 509.95612]
+ [511.2017  516.017   513.631   ... 515.5991  515.6389  521.8318 ]
+ ...
+ [496.2721  496.3155  506.08054 ... 502.36798 505.94202 516.3577 ]
+ [512.06866 505.80127 518.81934 ... 510.64966 510.10333 531.85364]
+ [501.23514 500.17123 505.71808 ... 496.4447  500.5735  514.4204 ]]
+[[503.26013 500.11093 511.70193 ... 501.24622 505.60373 511.38376]
+ [499.89877 495.13043 506.762   ... 493.1151  497.5555  510.29483]
+ [511.54883 516.35547 513.9717  ... 515.944   515.9865  522.1828 ]
+ ...
+ [496.59937 496.63785 506.41483 ... 502.70337 506.27927 516.6994 ]
+ [512.4154  506.1411  519.17175 ... 510.9929  510.45322 532.2152 ]
+ [501.57388 500.5093  506.06213 ... 496.7807  500.91638 514.77124]]
+MLIR equal to NumPy? True
+```
+
+As the tensorcore doesn't support fp32 computation, the operands are converted to tf32, hence the result is not exactly the same as the PyTorch result. 
+
+### Profiling
+You need to install nsight compute first.
+```
+ncu -o profile-result --set full python run-module-gpu.py --source matmul.mlir --target matmul-cubin.mlir --llvm_dir ../../llvm
+```

examples/BuddyGPU/makefile

@@ -1,8 +1,22 @@
 #!/bin/bash
 BUDDY_OPT := ../../build/bin/buddy-opt
+MLIR_OPT := ../../llvm/build/bin/mlir-opt
+MLIR_TRANSLATE := ../../llvm/build/bin/mlir-translate
+MLIR_CPU_RUNNER := ../../llvm/build/bin/mlir-cpu-runner
+LLC := ../../llvm/build/bin/llc
 
 buddy-gpu-matmul-lower:
 	@${BUDDY_OPT} matmul.mlir \
 		-transform-preload-library="transform-library-paths=transform.mlir" \
 		-transform-interpreter="entry-point=codegen" \
 		-o log.mlir
+
+buddy-gpu-matmul:
+	@${BUDDY_OPT} matmul.mlir -transform-preload-library="transform-library-paths=transform.mlir" -transform-interpreter="entry-point=codegen" | \
+	${BUDDY_OPT} --pass-pipeline='builtin.module(func.func(nvgpu-optimize-shared-memory))' | \
+	${BUDDY_OPT} -arith-expand -eliminate-empty-tensors -empty-tensor-to-alloc-tensor -linalg-bufferize -convert-linalg-to-affine-loops -affine-loop-fusion -affine-parallelize -lower-affine -canonicalize -func-bufferize -arith-bufferize -tensor-bufferize -buffer-deallocation -finalizing-bufferize -canonicalize | \
+	${BUDDY_OPT} -gpu-launch-sink-index-computations -canonicalize -legalize-shmem-outlining -canonicalize | \
+	${BUDDY_OPT} -convert-memcpy-to-gpu -gpu-async-region -canonicalize | \
+	${BUDDY_OPT} -convert-scf-to-cf -memref-expand -finalize-memref-to-llvm -convert-arith-to-llvm --convert-vector-to-llvm -convert-gpu-to-nvvm='has-redux=1' | \
+	${BUDDY_OPT} -llvm-request-c-wrappers -canonicalize -cse -sccp | \
+	${MLIR_OPT} --test-lower-to-nvvm="cubin-chip=sm_80 cubin-features=+ptx71 cubin-format=fatbin" -o matmul-cubin.mlir

examples/BuddyGPU/run-module-gpu.py

@@ -0,0 +1,147 @@
+# ===- run-module-gpu.py --------------------------------------------------===//
+#
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+#
+# ===----------------------------------------------------------------------===//
+#
+#  This file is a script to test whether the specified MLIR module on the GPU
+#  calculates the same result as NumPy.
+#
+# ===----------------------------------------------------------------------===//
+import mlir.ir as ir
+import mlir.dialects.func as func
+import mlir.dialects.memref as memref
+from mlir.passmanager import *
+from mlir.execution_engine import *
+from mlir import runtime as rt
+from mlir.ir import *
+import numpy as np
+import ctypes
+import ml_dtypes
+import argparse as ap
+
+
+def to_numpy(element_type: str) -> np.dtype:
+    match element_type:
+        case "f16":
+            return np.float16
+        case "f32":
+            return np.float32
+        case "f64":
+            return np.float64
+        case "i8":
+            return np.int8
+        case "i16":
+            return np.int16
+        case "i32":
+            return np.int32
+        case "i64":
+            return np.int64
+        case "bf16":
+            return np.dtype("bfloat16")
+        case _:
+            raise ValueError(f"Unsupported type: {element_type}")
+
+
+def new_ranked_memref_descriptor(nparray: np.ndarray):
+    if nparray.dtype == "bfloat16":
+        ctp = rt.F16
+    else:
+        ctp = rt.as_ctype(nparray.dtype)
+
+    if nparray.ndim == 0:
+        x = rt.make_zero_d_memref_descriptor(ctp)()
+        x.allocated = nparray.ctypes.data
+        x.aligned = nparray.ctypes.data_as(ctypes.POINTER(ctp))
+        x.offset = ctypes.c_longlong(0)
+        return x
+
+    x = rt.make_nd_memref_descriptor(nparray.ndim, ctp)()
+    nbytes = nparray.nbytes
+    buffer = ctypes.create_string_buffer(nbytes)
+    ctypes.memmove(buffer, nparray.ctypes.data, nbytes)
+    x.allocated = ctypes.cast(buffer, ctypes.c_void_p).value
+    x.aligned = ctypes.cast(buffer, ctypes.POINTER(ctp))
+    x.offset = ctypes.c_longlong(0)
+    x.shape = nparray.ctypes.shape
+
+    # Numpy uses byte quantities to express strides, MLIR OTOH uses the
+    # torch abstraction which specifies strides in terms of elements.
+    strides_ctype_t = ctypes.c_longlong * nparray.ndim
+    x.strides = strides_ctype_t(
+        *[x // nparray.itemsize for x in nparray.strides]
+    )
+    return x
+
+
+def get_memref_descriptors(args: list[Type]):
+    memref_ptrs = []
+    for arg in args:
+        elem_type = to_numpy(str(arg.element_type))
+        np_arg = np.random.rand(*arg.shape).astype(elem_type)
+        memref_ptrs.append(
+            ctypes.pointer(ctypes.pointer(new_ranked_memref_descriptor(np_arg)))
+        )
+    return memref_ptrs
+
+
+def test(source, target, llvm_dir):
+    with Context() as ctx:
+        file = open(source, "r")
+        module: Module = Module.parse(file.read())
+        funcOp: func.FuncOp = (
+            module.operation.regions[0].blocks[0].operations[0]
+        )
+        funcName = str(funcOp.name).replace('"', "")
+        assert isinstance(funcOp, func.FuncOp)
+        args_type: list[Type] = [arg.type for arg in funcOp.arguments]
+        res_type = funcOp.type.results
+
+        file = open(target, "r")
+        # newModule = lower_to_llvm_cpu(module)
+        newModule = Module.parse(file.read())
+        memref_ptrs = get_memref_descriptors(res_type + args_type)
+
+        engine = ExecutionEngine(
+            newModule,
+            shared_libs=[
+                "/usr/lib/libomp.so",
+                llvm_dir + "/build/lib/libmlir_c_runner_utils.so",
+                llvm_dir + "/build/lib/libmlir_async_runtime.so",
+                llvm_dir + "/build/lib/libmlir_runner_utils.so",
+                llvm_dir + "/build/lib/libmlir_cuda_runtime.so",
+            ],
+            opt_level=3,
+        )
+        engine.invoke(funcName, *memref_ptrs)
+        out = rt.ranked_memref_to_numpy(memref_ptrs[0][0])
+        if str(res_type[0].element_type) == "bf16":
+            print("Running on BF16 mode, skipping numpy comparison.")
+        else:
+            print(out)
+            input1 = rt.ranked_memref_to_numpy(memref_ptrs[1][0])
+            input2 = rt.ranked_memref_to_numpy(memref_ptrs[2][0])
+            numpy_out = np.matmul(input1, input2)
+            print(numpy_out)
+            print(
+                f"MLIR equal to NumPy? {np.allclose(out, numpy_out,rtol=1e-03, atol=1e-03)}"
+            )
+
+
+if __name__ == "__main__":
+    parser = ap.ArgumentParser()
+    parser.add_argument("--source", type=str, required=True)
+    parser.add_argument("--target", type=str, required=True)
+    parser.add_argument("--llvm_dir", type=str, required=True)
+    args = parser.parse_args()
+    test(args.source, args.target, args.llvm_dir)