[Mlir-commits] [mlir] [mlir][sparse] remove sparse output python example (PR #66298)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Wed Sep 13 15:02:57 PDT 2023
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir-core
<details>
<summary>Changes</summary>
Rationale:
This was actually just a pure "string based" test
with very little actual python usage. The output
sparse tensor was handled via the deprecated
convertFromMLIRSparseTensor method.
--
Full diff: https://github.com/llvm/llvm-project/pull/66298.diff
2 Files Affected:
- (removed) mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py (-108)
- (removed) mlir/test/Integration/Dialect/SparseTensor/python/tools/np_to_sparse_tensor.py (-81)
<pre>
diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py b/mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py
deleted file mode 100644
index a41bde1ee2d34b7..000000000000000
--- a/mlir/test/Integration/Dialect/SparseTensor/python/test_elementwise_add_sparse_output.py
+++ /dev/null
@@ -1,108 +0,0 @@
-# RUN: env SUPPORT_LIB=%mlir_c_runner_utils %PYTHON %s | FileCheck %s
-
-import ctypes
-import numpy as np
-import os
-import sys
-
-from mlir import ir
-from mlir import runtime as rt
-from mlir.dialects import sparse_tensor as st
-from mlir.dialects import builtin
-from mlir.dialects.linalg.opdsl import lang as dsl
-
-_SCRIPT_PATH = os.path.dirname(os.path.abspath(__file__))
-sys.path.append(_SCRIPT_PATH)
-from tools import np_to_sparse_tensor as test_tools
-from tools import sparse_compiler
-
-# TODO: Use linalg_structured_op to generate the kernel after making it to
-# handle sparse tensor outputs.
-_KERNEL_STR = """
-#DCSR = #sparse_tensor.encoding<{
- lvlTypes = [ "compressed", "compressed" ]
-}>
-
-#trait_add_elt = {
- indexing_maps = [
- affine_map<(i,j) -> (i,j)>, // A
- affine_map<(i,j) -> (i,j)>, // B
- affine_map<(i,j) -> (i,j)> // X (out)
- ],
- iterator_types = ["parallel", "parallel"],
- doc = "X(i,j) = A(i,j) + B(i,j)"
-}
-
-func.func @sparse_add_elt(
- %arga: tensor<3x4xf64, #DCSR>, %argb: tensor<3x4xf64, #DCSR>) -> tensor<3x4xf64, #DCSR> {
- %argx = bufferization.alloc_tensor() : tensor<3x4xf64, #DCSR>
- %0 = linalg.generic #trait_add_elt
- ins(%arga, %argb: tensor<3x4xf64, #DCSR>, tensor<3x4xf64, #DCSR>)
- outs(%argx: tensor<3x4xf64, #DCSR>) {
- ^bb(%a: f64, %b: f64, %x: f64):
- %1 = arith.addf %a, %b : f64
- linalg.yield %1 : f64
- } -> tensor<3x4xf64, #DCSR>
- return %0 : tensor<3x4xf64, #DCSR>
-}
-
-func.func @main(%ad: tensor<3x4xf64>, %bd: tensor<3x4xf64>) -> tensor<3x4xf64, #DCSR>
- attributes { llvm.emit_c_interface } {
- %a = sparse_tensor.convert %ad : tensor<3x4xf64> to tensor<3x4xf64, #DCSR>
- %b = sparse_tensor.convert %bd : tensor<3x4xf64> to tensor<3x4xf64, #DCSR>
- %0 = call @sparse_add_elt(%a, %b) : (tensor<3x4xf64, #DCSR>, tensor<3x4xf64, #DCSR>) -> tensor<3x4xf64, #DCSR>
- return %0 : tensor<3x4xf64, #DCSR>
-}
-"""
-
-
-def _run_test(support_lib, kernel):
- """Compiles, runs and checks results."""
- compiler = sparse_compiler.SparseCompiler(
- options="", opt_level=2, shared_libs=[support_lib]
- )
- module = ir.Module.parse(kernel)
- engine = compiler.compile_and_jit(module)
-
- # Set up numpy inputs and buffer for output.
- a = np.array(
- [[1.1, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 6.6, 0.0]], np.float64
- )
- b = np.array(
- [[1.1, 0.0, 0.0, 2.8], [0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0]], np.float64
- )
-
- mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
- mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
-
- # The sparse tensor output is a pointer to pointer of char.
- out = ctypes.c_char(0)
- mem_out = ctypes.pointer(ctypes.pointer(out))
-
- # Invoke the kernel.
- engine.invoke("main", mem_a, mem_b, mem_out)
-
- # Retrieve and check the result.
- rank, nse, shape, values, indices = test_tools.sparse_tensor_to_coo_tensor(
- support_lib, mem_out[0], np.float64
- )
-
- # CHECK: PASSED
- if np.allclose(values, [2.2, 2.8, 6.6]) and np.allclose(
- indices, [[0, 0], [0, 3], [2, 2]]
- ):
- print("PASSED")
- else:
- quit("FAILURE")
-
-
-def test_elementwise_add():
- # Obtain path to runtime support library.
- support_lib = os.getenv("SUPPORT_LIB")
- assert support_lib is not None, "SUPPORT_LIB is undefined"
- assert os.path.exists(support_lib), f"{support_lib} does not exist"
- with ir.Context() as ctx, ir.Location.unknown():
- _run_test(support_lib, _KERNEL_STR)
-
-
-test_elementwise_add()
diff --git a/mlir/test/Integration/Dialect/SparseTensor/python/tools/np_to_sparse_tensor.py b/mlir/test/Integration/Dialect/SparseTensor/python/tools/np_to_sparse_tensor.py
deleted file mode 100644
index 785d42cadbbe9f1..000000000000000
--- a/mlir/test/Integration/Dialect/SparseTensor/python/tools/np_to_sparse_tensor.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
-# See https://llvm.org/LICENSE.txt for license information.
-# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-
-# This file contains functions to process sparse tensor outputs.
-
-import ctypes
-import functools
-import numpy as np
-
-
- at functools.lru_cache()
-def _get_c_shared_lib(lib_name: str):
- """Loads and returns the requested C shared library.
-
- Args:
- lib_name: A string representing the C shared library.
-
- Returns:
- The C shared library.
-
- Raises:
- OSError: If there is any problem in loading the shared library.
- ValueError: If the shared library doesn't contain the needed routine.
- """
- # This raises OSError exception if there is any problem in loading the shared
- # library.
- c_lib = ctypes.CDLL(lib_name)
-
- try:
- c_lib.convertFromMLIRSparseTensorF64.restype = ctypes.c_void_p
- except Exception as e:
- raise ValueError(
- "Missing function convertFromMLIRSparseTensorF64 from "
- f"the C shared library: {e} "
- ) from e
-
- return c_lib
-
-
-def sparse_tensor_to_coo_tensor(support_lib, sparse, dtype):
- """Converts a sparse tensor to COO-flavored format.
-
- Args:
- support_lib: A string for the supporting C shared library.
- sparse: A ctypes.pointer to the sparse tensor descriptor.
- dtype: The numpy data type for the tensor elements.
-
- Returns:
- A tuple that contains the following values:
- rank: An integer for the rank of the tensor.
- nse: An integer for the number of non-zero values in the tensor.
- shape: A 1D numpy array of integers, for the shape of the tensor.
- values: A 1D numpy array, for the non-zero values in the tensor.
- indices: A 2D numpy array of integers, representing the indices for the
- non-zero values in the tensor.
-
- Raises:
- OSError: If there is any problem in loading the shared library.
- ValueError: If the shared library doesn't contain the needed routine.
- """
- c_lib = _get_c_shared_lib(support_lib)
-
- rank = ctypes.c_ulonglong(0)
- nse = ctypes.c_ulonglong(0)
- shape = ctypes.POINTER(ctypes.c_ulonglong)()
- values = ctypes.POINTER(np.ctypeslib.as_ctypes_type(dtype))()
- indices = ctypes.POINTER(ctypes.c_ulonglong)()
- c_lib.convertFromMLIRSparseTensorF64(
- sparse,
- ctypes.byref(rank),
- ctypes.byref(nse),
- ctypes.byref(shape),
- ctypes.byref(values),
- ctypes.byref(indices),
- )
- # Convert the returned values to the corresponding numpy types.
- shape = np.ctypeslib.as_array(shape, shape=[rank.value])
- values = np.ctypeslib.as_array(values, shape=[nse.value])
- indices = np.ctypeslib.as_array(indices, shape=[nse.value, rank.value])
- return rank, nse, shape, values, indices
</pre>
</details>
https://github.com/llvm/llvm-project/pull/66298
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