# RUN: %PYTHON %s 2>&1 | FileCheck %s
import ctypes
import sys
from mlir.ir import *
from mlir.dialects import builtin
from mlir.dialects import func
from mlir.dialects import linalg
from mlir.passmanager import *
from mlir.execution_engine import *
from mlir.dialects.linalg.opdsl.lang import *
# Log everything to stderr and flush so that we have a unified stream to match
# errors/info emitted by MLIR to stderr.
def log(*args):
print(*args, file=sys.stderr)
sys.stderr.flush()
elemwise_boiler = """
func.func @main() -> f32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0.0 : f32
%v1 = arith.constant 1.0 : f32
%v2 = arith.constant 2.0 : f32
%lhs = memref.alloc() : memref<f32>
%rhs = memref.alloc() : memref<4x8xf32>
%O0 = memref.alloc() : memref<4x8xf32>
%O1 = memref.alloc() : memref<4x8xf32>
linalg.fill ins(%v1 : f32) outs(%lhs : memref<f32>)
linalg.fill ins(%v2 : f32) outs(%rhs : memref<4x8xf32>)
linalg.fill ins(%v0 : f32) outs(%O0 : memref<4x8xf32>)
linalg.fill ins(%v0 : f32) outs(%O1 : memref<4x8xf32>)
call @elemwise_exp_add_on_buffers(%lhs, %rhs, %O0) :
(memref<f32>, memref<4x8xf32>, memref<4x8xf32>) -> ()
call @elemwise_log_mul_on_buffers(%lhs, %rhs, %O1) :
(memref<f32>, memref<4x8xf32>, memref<4x8xf32>) -> ()
%c0 = arith.constant 0 : index
%res0 = memref.load %O0[%c0, %c0] : memref<4x8xf32>
%res1 = memref.load %O1[%c0, %c0] : memref<4x8xf32>
%0 = arith.addf %res0, %res1 : f32
// TODO: FFI-based solution to allow testing and printing with python code.
return %0 : f32
}
"""
matmul_boiler = """
func.func @main() -> f32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0.0 : f32
%v1 = arith.constant -1 : i8
%v2 = arith.constant 2.0 : f32
%A = memref.alloc() : memref<4x16xi8>
%B = memref.alloc() : memref<16x8xf32>
%C0 = memref.alloc() : memref<4x8xf32>
%C1 = memref.alloc() : memref<4x8xf32>
linalg.fill ins(%v1 : i8) outs(%A : memref<4x16xi8>)
linalg.fill ins(%v2 : f32) outs(%B : memref<16x8xf32>)
linalg.fill ins(%v0 : f32) outs(%C0 : memref<4x8xf32>)
linalg.fill ins(%v0 : f32) outs(%C1 : memref<4x8xf32>)
call @matmul_signed_on_buffers(%A, %B, %C0) :
(memref<4x16xi8>, memref<16x8xf32>, memref<4x8xf32>) -> ()
call @matmul_unsigned_on_buffers(%A, %B, %C1) :
(memref<4x16xi8>, memref<16x8xf32>, memref<4x8xf32>) -> ()
%c0 = arith.constant 0 : index
%res0 = memref.load %C0[%c0, %c0] : memref<4x8xf32>
%res1 = memref.load %C1[%c0, %c0] : memref<4x8xf32>
%0 = arith.addf %res0, %res1 : f32
// TODO: FFI-based solution to allow testing and printing with python code.
return %0 : f32
}
"""
fill_boiler = """
func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%O0 = memref.alloc() : memref<i32>
%O1 = memref.alloc() : memref<16xi32>
%O2 = memref.alloc() : memref<4x16xi32>
%val0 = arith.constant 1.0 : f32
%val1 = arith.constant 2.0 : f32
%val2 = arith.constant 3.0 : f32
call @fill_0d_on_buffers(%val0, %O0) : (f32, memref<i32>) -> ()
call @fill_1d_on_buffers(%val1, %O1) : (f32, memref<16xi32>) -> ()
call @fill_2d_on_buffers(%val2, %O2) : (f32, memref<4x16xi32>) -> ()
%c0 = arith.constant 0 : index
%res0 = memref.load %O0[] : memref<i32>
%c8 = arith.constant 8 : index
%res1 = memref.load %O1[%c8] : memref<16xi32>
%c2 = arith.constant 2 : index
%res2 = memref.load %O2[%c2, %c8] : memref<4x16xi32>
%0 = arith.addi %res0, %res1 : i32
%1 = arith.addi %0, %res2 : i32
// TODO: FFI-based solution to allow testing and printing with python code.
return %1 : i32
}
"""
fill_rng_boiler = """
func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%O = memref.alloc() : memref<4x16xi32>
%min = arith.constant -1000.0 : f64
%max = arith.constant 1000.0 : f64
%seed = arith.constant 42 : i32
call @fill_rng_on_buffers(%min, %max, %seed, %O) :
(f64, f64, i32, memref<4x16xi32>) -> ()
%c0 = arith.constant 0 : index
%0 = memref.load %O[%c0, %c0] : memref<4x16xi32>
// TODO: FFI-based solution to allow testing and printing with python code.
return %0 : i32
}
"""
conv_boiler = """
func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0 : i32
%v1 = arith.constant 1.0 : f64
%v2 = arith.constant 2.0 : f64
%input = memref.alloc() : memref<1x4x16x1xf64>
%filter = memref.alloc() : memref<2x2x1xf64>
%output = memref.alloc() : memref<1x2x4x1xi32>
linalg.fill ins(%v1 : f64) outs(%input : memref<1x4x16x1xf64>)
linalg.fill ins(%v2 : f64) outs(%filter : memref<2x2x1xf64>)
linalg.fill ins(%v0 : i32) outs(%output : memref<1x2x4x1xi32>)
call @conv_on_buffers(%input, %filter, %output) :
(memref<1x4x16x1xf64>, memref<2x2x1xf64>, memref<1x2x4x1xi32>) -> ()
%c0 = arith.constant 0 : index
%0 = memref.load %output[%c0, %c0, %c0, %c0] : memref<1x2x4x1xi32>
// TODO: FFI-based solution to allow testing and printing with python code.
return %0 : i32
}
"""
pooling_boiler = """
func.func @main() -> i32 attributes {llvm.emit_c_interface} {
%v0 = arith.constant 0 : i32
%v42 = arith.constant 42.0 : f64
%v77 = arith.constant 77.0 : f64
%v-13 = arith.constant -13.0 : f64
%v1 = arith.constant 1.0 : f64
%input = memref.alloc() : memref<1x4x16x1xf64>
%shape = memref.alloc() : memref<2x2xf64>
%output = memref.alloc() : memref<1x2x4x1xi32>
linalg.fill ins(%v1 : f64) outs(%input : memref<1x4x16x1xf64>)
linalg.fill ins(%v1 : f64) outs(%shape : memref<2x2xf64>)
linalg.fill ins(%v0 : i32) outs(%output : memref<1x2x4x1xi32>)
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index
memref.store %v42, %input[%c0, %c0, %c0, %c0] : memref<1x4x16x1xf64>
memref.store %v77, %input[%c0, %c0, %c1, %c0] : memref<1x4x16x1xf64>
memref.store %v-13, %input[%c0, %c1, %c0, %c0] : memref<1x4x16x1xf64>
call @pooling_on_buffers(%input, %shape, %output) :
(memref<1x4x16x1xf64>, memref<2x2xf64>, memref<1x2x4x1xi32>) -> ()
%0 = memref.load %output[%c0, %c0, %c0, %c0] : memref<1x2x4x1xi32>
// TODO: FFI-based solution to allow testing and printing with python code.
return %0 : i32
}
"""
def transform(module, boilerplate):
# TODO: Allow cloning functions from one module to another.
# Atm we have to resort to string concatenation.
ops = module.operation.regions[0].blocks[0].operations
mod = Module.parse("\n".join([str(op) for op in ops]) + boilerplate)
pm = PassManager("builtin.module")
pm.add("func.func(convert-linalg-to-loops)")
pm.add("func.func(lower-affine)")
pm.add("func.func(convert-math-to-llvm)")
pm.add("func.func(convert-scf-to-cf)")
pm.add("func.func(arith-expand)")
pm.add("func.func(memref-expand)")
pm.add("convert-vector-to-llvm")
pm.add("finalize-memref-to-llvm")
pm.add("convert-func-to-llvm")
pm.add("reconcile-unrealized-casts")
pm.run(mod.operation)
return mod
def test_elemwise_builtin():
with Context() as ctx, Location.unknown():
module = Module.create()
f32 = F32Type.get()
i8 = IntegerType.get_signless(8)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((), f32),
MemRefType.get((4, 8), f32),
MemRefType.get((4, 8), f32),
)
def elemwise_exp_add_on_buffers(lhs, rhs, out):
linalg.elemwise_unary(lhs, outs=[out])
linalg.elemwise_binary(out, rhs, outs=[out])
@func.FuncOp.from_py_func(
MemRefType.get((), f32),
MemRefType.get((4, 8), f32),
MemRefType.get((4, 8), f32),
)
def elemwise_log_mul_on_buffers(lhs, rhs, out):
linalg.elemwise_unary(lhs, outs=[out], fun=UnaryFn.log)
linalg.elemwise_binary(out, rhs, outs=[out], fun=BinaryFn.mul)
execution_engine = ExecutionEngine(transform(module, elemwise_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result f32.
# Arguments must be passed as pointers.
c_float_p = ctypes.c_float * 1
res = c_float_p(-1.0)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# elemwise_exp_add_on_buffers: exp(1.0) + 2.0 = 4.71828182846
# elemwise_log_mul_on_buffers: log(1.0) * 2.0 = 0.0
# CHECK: RESULT: 4.71828
test_elemwise_builtin()
def test_elemwise_generic():
with Context() as ctx, Location.unknown():
module = Module.create()
f32 = F32Type.get()
i8 = IntegerType.get_signless(8)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((), f32),
MemRefType.get((4, 8), f32),
MemRefType.get((4, 8), f32),
)
def elemwise_exp_add_on_buffers(lhs, rhs, out):
linalg.elemwise_unary(lhs, outs=[out], emit_generic=True)
linalg.elemwise_binary(out, rhs, outs=[out], emit_generic=True)
@func.FuncOp.from_py_func(
MemRefType.get((), f32),
MemRefType.get((4, 8), f32),
MemRefType.get((4, 8), f32),
)
def elemwise_log_mul_on_buffers(lhs, rhs, out):
linalg.elemwise_unary(
lhs, outs=[out], fun=UnaryFn.log, emit_generic=True
)
linalg.elemwise_binary(
out, rhs, outs=[out], fun=BinaryFn.mul, emit_generic=True
)
execution_engine = ExecutionEngine(transform(module, elemwise_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result f32.
# Arguments must be passed as pointers.
c_float_p = ctypes.c_float * 1
res = c_float_p(-1.0)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# elemwise_exp_add_on_buffers: exp(1.0) + 2.0 = 4.71828182846
# elemwise_log_mul_on_buffers: log(1.0) * 2.0 = 0.0
# CHECK: RESULT: 4.71828
test_elemwise_generic()
def test_matmul_builtin():
with Context() as ctx, Location.unknown():
module = Module.create()
f32 = F32Type.get()
i8 = IntegerType.get_signless(8)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((4, 16), i8),
MemRefType.get((16, 8), f32),
MemRefType.get((4, 8), f32),
)
def matmul_signed_on_buffers(lhs, rhs, out):
linalg.matmul(lhs, rhs, outs=[out])
@func.FuncOp.from_py_func(
MemRefType.get((4, 16), i8),
MemRefType.get((16, 8), f32),
MemRefType.get((4, 8), f32),
)
def matmul_unsigned_on_buffers(lhs, rhs, out):
linalg.matmul(lhs, rhs, outs=[out], cast=TypeFn.cast_unsigned)
execution_engine = ExecutionEngine(transform(module, matmul_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result f32.
# Arguments must be passed as pointers.
c_float_p = ctypes.c_float * 1
res = c_float_p(-1.0)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# matmul_signed_on_buffers: -1 * 2.0 * 16 = -32
# matmul_unsigned_on_buffers: (2^8-1) * 2.0 * 16 = 8160
# CHECK: RESULT: 8128
test_matmul_builtin()
def test_matmul_generic():
with Context() as ctx, Location.unknown():
module = Module.create()
f32 = F32Type.get()
i8 = IntegerType.get_signless(8)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((4, 16), i8),
MemRefType.get((16, 8), f32),
MemRefType.get((4, 8), f32),
)
def matmul_signed_on_buffers(lhs, rhs, out):
linalg.matmul(lhs, rhs, outs=[out], emit_generic=True)
@func.FuncOp.from_py_func(
MemRefType.get((4, 16), i8),
MemRefType.get((16, 8), f32),
MemRefType.get((4, 8), f32),
)
def matmul_unsigned_on_buffers(lhs, rhs, out):
linalg.matmul(
lhs, rhs, outs=[out], cast=TypeFn.cast_unsigned, emit_generic=True
)
execution_engine = ExecutionEngine(transform(module, matmul_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result f32.
# Arguments must be passed as pointers.
c_float_p = ctypes.c_float * 1
res = c_float_p(-1.0)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# matmul_signed_on_buffers = -1 * 2.0 * 16 = -32
# matmul_unsigned_on_buffers = (2^8-1) * 2.0 * 16 = 8160
# CHECK: RESULT: 8128
test_matmul_generic()
def test_fill_builtin():
with Context() as ctx, Location.unknown():
module = Module.create()
f32 = F32Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(f32, MemRefType.get([], i32))
def fill_0d_on_buffers(value, out):
linalg.fill(value, outs=[out])
@func.FuncOp.from_py_func(f32, MemRefType.get([16], i32))
def fill_1d_on_buffers(value, out):
linalg.fill(value, outs=[out])
@func.FuncOp.from_py_func(f32, MemRefType.get([4, 16], i32))
def fill_2d_on_buffers(value, out):
linalg.fill(value, outs=[out])
execution_engine = ExecutionEngine(transform(module, fill_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# CHECK: RESULT: 6
test_fill_builtin()
def test_fill_generic():
with Context() as ctx, Location.unknown():
module = Module.create()
f32 = F32Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(f32, MemRefType.get([], i32))
def fill_0d_on_buffers(value, out):
linalg.fill(value, outs=[out], emit_generic=True)
@func.FuncOp.from_py_func(f32, MemRefType.get([16], i32))
def fill_1d_on_buffers(value, out):
linalg.fill(value, outs=[out], emit_generic=True)
@func.FuncOp.from_py_func(f32, MemRefType.get([4, 16], i32))
def fill_2d_on_buffers(value, out):
linalg.fill(value, outs=[out], emit_generic=True)
execution_engine = ExecutionEngine(transform(module, fill_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# CHECK: RESULT: 6
test_fill_generic()
def test_fill_rng_builtin():
with Context() as ctx, Location.unknown():
module = Module.create()
f64 = F64Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(f64, f64, i32, MemRefType.get((4, 16), i32))
def fill_rng_on_buffers(min, max, seed, out):
linalg.fill_rng_2d(min, max, seed, outs=[out])
execution_engine = ExecutionEngine(transform(module, fill_rng_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# CHECK: RESULT: -480
test_fill_rng_builtin()
def test_fill_rng_generic():
with Context() as ctx, Location.unknown():
module = Module.create()
f64 = F64Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(f64, f64, i32, MemRefType.get((4, 16), i32))
def fill_rng_on_buffers(min, max, seed, out):
linalg.fill_rng_2d(min, max, seed, outs=[out], emit_generic=True)
execution_engine = ExecutionEngine(transform(module, fill_rng_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# CHECK: RESULT: -480
test_fill_rng_generic()
def test_max_pooling_builtin():
with Context() as ctx, Location.unknown():
module = Module.create()
f64 = F64Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((1, 4, 16, 1), f64),
MemRefType.get((2, 2), f64),
MemRefType.get((1, 2, 4, 1), i32),
)
def pooling_on_buffers(input, shape, output):
linalg.pooling_nhwc_max(
input, shape, outs=[output], strides=[2, 4], dilations=[1, 2]
)
execution_engine = ExecutionEngine(transform(module, pooling_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# 77 is not selected due to the dilation 2 in the second dimension.
# CHECK: RESULT: 42
test_max_pooling_builtin()
def test_max_pooling_generic():
with Context() as ctx, Location.unknown():
module = Module.create()
f64 = F64Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((1, 4, 16, 1), f64),
MemRefType.get((2, 2), f64),
MemRefType.get((1, 2, 4, 1), i32),
)
def pooling_on_buffers(input, shape, output):
linalg.pooling_nhwc_max(
input,
shape,
outs=[output],
strides=[2, 4],
dilations=[1, 2],
emit_generic=True,
)
execution_engine = ExecutionEngine(transform(module, pooling_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# 77 is not selected due to the dilation 2 in the second dimension.
# CHECK: RESULT: 42
test_max_pooling_generic()
def test_min_pooling_builtin():
with Context() as ctx, Location.unknown():
module = Module.create()
f64 = F64Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((1, 4, 16, 1), f64),
MemRefType.get((2, 2), f64),
MemRefType.get((1, 2, 4, 1), i32),
)
# Set the strides and use the default dilations.
def pooling_on_buffers(input, shape, output):
linalg.pooling_nhwc_min(input, shape, outs=[output], strides=[2, 4])
execution_engine = ExecutionEngine(transform(module, pooling_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# CHECK: RESULT: -13
test_min_pooling_builtin()
def test_min_pooling_generic():
with Context() as ctx, Location.unknown():
module = Module.create()
f64 = F64Type.get()
i32 = IntegerType.get_signless(32)
with InsertionPoint(module.body):
@func.FuncOp.from_py_func(
MemRefType.get((1, 4, 16, 1), f64),
MemRefType.get((2, 2), f64),
MemRefType.get((1, 2, 4, 1), i32),
)
# Set the strides and use the default dilations.
def pooling_on_buffers(input, shape, output):
linalg.pooling_nhwc_min(
input, shape, outs=[output], strides=[2, 4], emit_generic=True
)
execution_engine = ExecutionEngine(transform(module, pooling_boiler))
# TODO: FFI-based solution to allow testing and printing with python code.
# Prepare arguments: one result i32.
# Arguments must be passed as pointers.
c_int_p = ctypes.c_int * 1
res = c_int_p(-1)
execution_engine.invoke("main", res)
log("RESULT: ", res[0])
# CHECK: RESULT: -13
test_min_pooling_generic()
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