f796bc622a
Now that linalg.matmul is in tablegen, "hand write" the Python wrapper that OpDSL used to derive. Similarly, add a Python wrapper for the new linalg.contract op. Required following misc. fixes: 1) make linalg.matmul's parsing and printing consistent w.r.t. whether indexing_maps occurs before or after operands, i.e. per the tests cases it comes _before_. 2) tablegen for linalg.contract did not state it accepted an optional cast attr. 3) In ODS's C++-generating code, expand partial support for `$_builder` access in `Attr::defaultValue` to full support. This enables access to the current `MlirContext` when constructing the default value (as is required when the default value consists of affine maps).
469 lines
22 KiB
Python
469 lines
22 KiB
Python
# RUN: %PYTHON %s | FileCheck %s
|
|
|
|
from mlir.dialects import arith, func, linalg, tensor, memref
|
|
from mlir.dialects.linalg.opdsl.lang import *
|
|
from mlir.ir import *
|
|
|
|
|
|
def run(f):
|
|
print("\nTEST:", f.__name__)
|
|
f()
|
|
return f
|
|
|
|
|
|
# CHECK-LABEL: TEST: testFill
|
|
@run
|
|
def testFill():
|
|
with Context() as ctx, Location.unknown():
|
|
module = Module.create()
|
|
f32 = F32Type.get()
|
|
with InsertionPoint(module.body):
|
|
# CHECK-LABEL: func @fill_tensor
|
|
# CHECK-SAME: %[[OUT:[0-9a-z]+]]: tensor<12x?xf32>
|
|
# CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
|
|
# CHECK-NEXT: %[[RES:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : tensor<12x?xf32>) -> tensor<12x?xf32>
|
|
# CHECK-NEXT: return %[[RES]] : tensor<12x?xf32>
|
|
@func.FuncOp.from_py_func(
|
|
RankedTensorType.get((12, ShapedType.get_dynamic_size()), f32)
|
|
)
|
|
def fill_tensor(out):
|
|
zero = arith.ConstantOp(
|
|
value=FloatAttr.get(f32, 0.0), result=f32
|
|
).result
|
|
return linalg.fill(zero, outs=[out])
|
|
|
|
# CHECK-LABEL: func @fill_buffer
|
|
# CHECK-SAME: %[[OUT:[0-9a-z]+]]: memref<12x?xf32>
|
|
# CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
|
|
# CHECK-NEXT: linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : memref<12x?xf32>)
|
|
# CHECK-NEXT: return
|
|
@func.FuncOp.from_py_func(
|
|
MemRefType.get((12, ShapedType.get_dynamic_size()), f32)
|
|
)
|
|
def fill_buffer(out):
|
|
zero = arith.ConstantOp(
|
|
value=FloatAttr.get(f32, 0.0), result=f32
|
|
).result
|
|
linalg.fill(zero, outs=[out])
|
|
|
|
print(module)
|
|
|
|
|
|
# CHECK-LABEL: TEST: testNamedStructuredOpCustomForm
|
|
@run
|
|
def testNamedStructuredOpCustomForm():
|
|
with Context() as ctx, Location.unknown():
|
|
module = Module.create()
|
|
f32 = F32Type.get()
|
|
with InsertionPoint(module.body):
|
|
|
|
@func.FuncOp.from_py_func(
|
|
RankedTensorType.get((4, 8), f32), RankedTensorType.get((4, 8), f32)
|
|
)
|
|
def named_form(lhs, rhs):
|
|
init_result = tensor.EmptyOp([4, 8], f32)
|
|
# Check for the named form with custom format
|
|
# CHECK: linalg.elemwise_unary
|
|
# CHECK-SAME: cast = #linalg.type_fn<cast_signed>
|
|
# CHECK-SAME: fun = #linalg.unary_fn<exp>
|
|
# CHECK-SAME: ins(%{{.*}} : tensor<4x8xf32>) outs(%{{.*}} : tensor<4x8xf32>)
|
|
unary_result = linalg.elemwise_unary(lhs, outs=[init_result.result])
|
|
# CHECK: linalg.elemwise_binary
|
|
# CHECK-SAME: cast = #linalg.type_fn<cast_unsigned>
|
|
# CHECK-SAME: fun = #linalg.binary_fn<mul>
|
|
# CHECK-SAME: ins(%{{.*}}, %{{.*}} : tensor<4x8xf32>, tensor<4x8xf32>) outs(%{{.*}} : tensor<4x8xf32>)
|
|
# CHECK: return
|
|
binary_result = linalg.elemwise_binary(
|
|
lhs,
|
|
rhs,
|
|
outs=[init_result.result],
|
|
fun=BinaryFn.mul,
|
|
cast=TypeFn.cast_unsigned,
|
|
)
|
|
return unary_result, binary_result
|
|
|
|
print(module)
|
|
|
|
|
|
# CHECK-LABEL: TEST: testIdentityRegionOps
|
|
@run
|
|
def testIdentityRegionOps():
|
|
with Context(), Location.unknown():
|
|
module = Module.create()
|
|
f32 = F32Type.get()
|
|
with InsertionPoint(module.body):
|
|
# CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<1x13xf32>
|
|
# CHECK: %[[VAL_1:.*]] = tensor.empty() : tensor<13x1xf32>
|
|
op1 = tensor.EmptyOp([1, 13], f32)
|
|
op2 = tensor.EmptyOp([13, 1], f32)
|
|
# CHECK: %[[VAL_2:.*]] = linalg.transpose ins(%[[VAL_0]] : tensor<1x13xf32>) outs(%[[VAL_1]] : tensor<13x1xf32>) permutation = [1, 0]
|
|
op3 = linalg.TransposeOp(
|
|
result=[RankedTensorType.get((13, 1), f32)],
|
|
input=op1,
|
|
init=op2,
|
|
permutation=[1, 0],
|
|
)
|
|
linalg.fill_builtin_region(op3.operation)
|
|
|
|
# CHECK: %[[VAL_3:.*]] = linalg.transpose ins(%[[VAL_1]] : tensor<13x1xf32>) outs(%[[VAL_0]] : tensor<1x13xf32>) permutation = [1, 0]
|
|
op4 = linalg.transpose(op2, outs=[op1], permutation=[1, 0])
|
|
|
|
# CHECK: func.func @transpose_op(%[[VAL_4:.*]]: memref<1x13xf32>, %[[VAL_5:.*]]: memref<13x1xf32>)
|
|
@func.FuncOp.from_py_func(
|
|
MemRefType.get((1, 13), f32),
|
|
MemRefType.get((13, 1), f32),
|
|
)
|
|
def transpose_op(op1, op2):
|
|
# CHECK: linalg.transpose ins(%[[VAL_4]] : memref<1x13xf32>) outs(%[[VAL_5]] : memref<13x1xf32>) permutation = [1, 0]
|
|
op3 = linalg.TransposeOp(
|
|
result=[],
|
|
input=op1,
|
|
init=op2,
|
|
permutation=[1, 0],
|
|
)
|
|
linalg.fill_builtin_region(op3.operation)
|
|
# CHECK: linalg.transpose ins(%[[VAL_5]] : memref<13x1xf32>) outs(%[[VAL_4]] : memref<1x13xf32>) permutation = [1, 0]
|
|
op4 = linalg.transpose(op2, outs=[op1], permutation=[1, 0])
|
|
|
|
# CHECK: %[[VAL_6:.*]] = tensor.empty() : tensor<16xf32>
|
|
# CHECK: %[[VAL_7:.*]] = tensor.empty() : tensor<16x64xf32>
|
|
op1 = tensor.EmptyOp([16], f32)
|
|
op2 = tensor.EmptyOp([16, 64], f32)
|
|
# CHECK: %[[VAL_8:.*]] = linalg.broadcast ins(%[[VAL_6]] : tensor<16xf32>) outs(%[[VAL_7]] : tensor<16x64xf32>) dimensions = [1]
|
|
op3 = linalg.BroadcastOp(
|
|
result=[RankedTensorType.get((16, 64), f32)],
|
|
input=op1,
|
|
init=op2,
|
|
dimensions=[1],
|
|
)
|
|
linalg.fill_builtin_region(op3.operation)
|
|
|
|
# CHECK: %[[VAL_9:.*]] = tensor.empty() : tensor<64xf32>
|
|
op4 = tensor.EmptyOp([64], f32)
|
|
# CHECK: %[[VAL_10:.*]] = linalg.broadcast ins(%[[VAL_9]] : tensor<64xf32>) outs(%[[VAL_7]] : tensor<16x64xf32>) dimensions = [0]
|
|
op5 = linalg.broadcast(op4, outs=[op2], dimensions=[0])
|
|
|
|
# CHECK: func.func @broadcast_op(%[[VAL_11:.*]]: memref<16xf32>, %[[VAL_12:.*]]: memref<16x64xf32>, %[[VAL_13:.*]]: memref<64xf32>)
|
|
@func.FuncOp.from_py_func(
|
|
MemRefType.get((16,), f32),
|
|
MemRefType.get((16, 64), f32),
|
|
MemRefType.get((64,), f32),
|
|
)
|
|
def broadcast_op(op1, op2, op3):
|
|
# CHECK: linalg.broadcast ins(%[[VAL_11]] : memref<16xf32>) outs(%[[VAL_12]] : memref<16x64xf32>) dimensions = [1]
|
|
op4 = linalg.BroadcastOp(
|
|
result=[],
|
|
input=op1,
|
|
init=op2,
|
|
dimensions=[1],
|
|
)
|
|
linalg.fill_builtin_region(op4.operation)
|
|
# CHECK: linalg.broadcast ins(%[[VAL_13]] : memref<64xf32>) outs(%[[VAL_12]] : memref<16x64xf32>) dimensions = [0]
|
|
op5 = linalg.broadcast(op3, outs=[op2], dimensions=[0])
|
|
|
|
print(module)
|
|
|
|
|
|
# CHECK-LABEL: TEST: testGenericOp
|
|
@run
|
|
def testGenericOp():
|
|
with Context(), Location.unknown():
|
|
module = Module.create()
|
|
f32 = F32Type.get()
|
|
memref_t = MemRefType.get([10, 10], f32)
|
|
with InsertionPoint(module.body):
|
|
id_map_1 = AffineMap.get_identity(2)
|
|
# CHECK: %[[VAL_0:.*]] = tensor.empty() : tensor<16x16xf32>
|
|
# CHECK: %[[VAL_1:.*]] = tensor.empty() : tensor<16x16xf32>
|
|
x = tensor.empty((16, 16), f32)
|
|
y = tensor.empty((16, 16), f32)
|
|
|
|
# CHECK: %[[VAL_2:.*]] = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%[[VAL_0]] : tensor<16x16xf32>) outs(%[[VAL_1]] : tensor<16x16xf32>) {
|
|
# CHECK: ^bb0(%in: f32, %out: f32):
|
|
# CHECK: linalg.yield %in : f32
|
|
# CHECK: } -> tensor<16x16xf32>
|
|
@linalg.generic(
|
|
[x],
|
|
[y],
|
|
[id_map_1, id_map_1],
|
|
[linalg.IteratorType.parallel, linalg.IteratorType.parallel],
|
|
)
|
|
def f(a, b):
|
|
assert isinstance(a, Value)
|
|
assert isinstance(a.type, F32Type)
|
|
assert isinstance(b, Value)
|
|
assert isinstance(b.type, F32Type)
|
|
return a
|
|
|
|
assert isinstance(f, Value)
|
|
assert isinstance(f.type, RankedTensorType)
|
|
|
|
# CHECK: %[[VAL_3:.*]] = tensor.empty() : tensor<16x16x16xf32>
|
|
z = tensor.empty((16, 16, 16), f32)
|
|
|
|
minor_id = AffineMap.get_minor_identity(3, 2)
|
|
id_map_2 = AffineMap.get_identity(3)
|
|
|
|
# CHECK: %[[VAL_4:.+]]:2 = linalg.generic {indexing_maps = [#map1, #map2, #map2], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[VAL_0]] : tensor<16x16xf32>) outs(%[[VAL_3]], %[[VAL_3]] : tensor<16x16x16xf32>, tensor<16x16x16xf32>) {
|
|
# CHECK: ^bb0(%in: f32, %out: f32, %out_1: f32):
|
|
# CHECK: linalg.yield %in, %out : f32, f32
|
|
# CHECK: } -> (tensor<16x16x16xf32>, tensor<16x16x16xf32>)
|
|
@linalg.generic(
|
|
[x],
|
|
[z, z],
|
|
[minor_id, id_map_2, id_map_2],
|
|
[
|
|
linalg.IteratorType.parallel,
|
|
linalg.IteratorType.parallel,
|
|
linalg.IteratorType.parallel,
|
|
],
|
|
)
|
|
def g(a, b, c):
|
|
assert isinstance(a, Value)
|
|
assert isinstance(a.type, F32Type)
|
|
assert isinstance(b, Value)
|
|
assert isinstance(b.type, F32Type)
|
|
assert isinstance(c, Value)
|
|
assert isinstance(c.type, F32Type)
|
|
return a, b
|
|
|
|
assert isinstance(g, OpResultList)
|
|
assert len(g) == 2
|
|
assert isinstance(g[0].type, RankedTensorType)
|
|
assert isinstance(g[1].type, RankedTensorType)
|
|
|
|
# CHECK: %[[VAL_5:.*]] = memref.alloc() : memref<10x10xf32>
|
|
# CHECK: %[[VAL_6:.*]] = memref.alloc() : memref<10x10xf32>
|
|
xx = memref.alloc(memref_t, [], [])
|
|
yy = memref.alloc(memref_t, [], [])
|
|
|
|
# CHECK: linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel", "parallel"]} ins(%[[VAL_5]] : memref<10x10xf32>) outs(%[[VAL_6]] : memref<10x10xf32>) {
|
|
# CHECK: ^bb0(%in: f32, %out: f32):
|
|
# CHECK: linalg.yield %in : f32
|
|
# CHECK: }
|
|
@linalg.generic(
|
|
[xx],
|
|
[yy],
|
|
[id_map_1, id_map_1],
|
|
[linalg.IteratorType.parallel, linalg.IteratorType.parallel],
|
|
)
|
|
def f(a, b):
|
|
assert isinstance(a, Value)
|
|
assert isinstance(a.type, F32Type)
|
|
assert isinstance(b, Value)
|
|
assert isinstance(b.type, F32Type)
|
|
return a
|
|
|
|
module.operation.verify()
|
|
print(module)
|
|
|
|
|
|
# CHECK-LABEL: TEST: testMatmulOp
|
|
@run
|
|
def testMatmulOp():
|
|
with Context(), Location.unknown():
|
|
module = Module.create()
|
|
f32 = F32Type.get()
|
|
with InsertionPoint(module.body):
|
|
a_shape = (4, 8)
|
|
b_shape = (8, 12)
|
|
b_transposed_shape = (12, 8)
|
|
c_shape = (4, 12)
|
|
|
|
dimM = ir.AffineDimExpr.get(0)
|
|
dimN = ir.AffineDimExpr.get(1)
|
|
dimK = ir.AffineDimExpr.get(2)
|
|
|
|
# CHECK: #[[$A_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
|
|
# CHECK: #[[$BTrans_MAP:.*]] = affine_map<(d0, d1, d2) -> (d1, d2)>
|
|
# CHECK: #[[$C_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
a_map = ir.AffineMap.get(3, 0, [dimM, dimK])
|
|
b_map = ir.AffineMap.get(3, 0, [dimK, dimN])
|
|
c_map = ir.AffineMap.get(3, 0, [dimM, dimN])
|
|
b_transposed_map = ir.AffineMap.get(3, 0, [dimN, dimK])
|
|
|
|
# CHECK: func.func @matmul_op(
|
|
@func.FuncOp.from_py_func(
|
|
# CHECK-SAME: %[[A:.*]]: tensor<4x8xf32>,
|
|
RankedTensorType.get(a_shape, f32),
|
|
# CHECK-SAME: %[[Amem:.*]]: memref<4x8xf32>,
|
|
MemRefType.get(a_shape, f32),
|
|
# CHECK-SAME: %[[B:.*]]: tensor<8x12xf32>,
|
|
RankedTensorType.get(b_shape, f32),
|
|
# CHECK-SAME: %[[Bmem:.*]]: memref<8x12xf32>,
|
|
MemRefType.get(b_shape, f32),
|
|
# CHECK-SAME: %[[BTrans:.*]]: tensor<12x8xf32>,
|
|
RankedTensorType.get(b_transposed_shape, f32),
|
|
# CHECK-SAME: %[[BTransmem:.*]]: memref<12x8xf32>,
|
|
MemRefType.get(b_transposed_shape, f32),
|
|
# CHECK-SAME: %[[C:.*]]: tensor<4x12xf32>,
|
|
RankedTensorType.get(c_shape, f32),
|
|
# CHECK-SAME: %[[Cmem:.*]]: memref<4x12xf32>)
|
|
MemRefType.get(c_shape, f32),
|
|
)
|
|
def matmul_op(A, Amem, B, Bmem, Btransposed, Btransposedmem, C, Cmem):
|
|
# CHECK: linalg.matmul ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
res = linalg.MatmulOp(
|
|
result_tensors=(C.type,),
|
|
inputs=(A, B),
|
|
outputs=(C,),
|
|
)
|
|
linalg.fill_builtin_region(res.operation)
|
|
# CHECK: linalg.matmul ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
res = linalg.matmul(A, B, outs=(C,))
|
|
|
|
# CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
res = linalg.MatmulOp(
|
|
result_tensors=(C.type,),
|
|
inputs=(A, Btransposed),
|
|
outputs=(C,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
linalg.fill_builtin_region(res.operation)
|
|
# CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
res = linalg.matmul(
|
|
A,
|
|
Btransposed,
|
|
outs=(C,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
|
|
# And now with memrefs...
|
|
|
|
# CHECK: linalg.matmul ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
res = linalg.MatmulOp(
|
|
result_tensors=[],
|
|
inputs=(Amem, Bmem),
|
|
outputs=(Cmem,),
|
|
)
|
|
linalg.fill_builtin_region(res.operation)
|
|
# CHECK: linalg.matmul ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
linalg.matmul(Amem, Bmem, outs=(Cmem,))
|
|
|
|
# CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
res = linalg.MatmulOp(
|
|
result_tensors=[],
|
|
inputs=(Amem, Btransposedmem),
|
|
outputs=(Cmem,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
linalg.fill_builtin_region(res.operation)
|
|
# CHECK: linalg.matmul indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
linalg.matmul(
|
|
Amem,
|
|
Btransposedmem,
|
|
outs=(Cmem,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
|
|
print(module)
|
|
|
|
|
|
# CHECK-LABEL: TEST: testContractOp
|
|
@run
|
|
def testContractOp():
|
|
with Context(), Location.unknown():
|
|
module = Module.create()
|
|
f32 = F32Type.get()
|
|
with InsertionPoint(module.body):
|
|
a_shape = (4, 8)
|
|
b_shape = (8, 12)
|
|
b_transposed_shape = (12, 8)
|
|
c_shape = (4, 12)
|
|
|
|
dimM = ir.AffineDimExpr.get(0)
|
|
dimN = ir.AffineDimExpr.get(1)
|
|
dimK = ir.AffineDimExpr.get(2)
|
|
|
|
# CHECK: #[[$A_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
|
|
# CHECK: #[[$B_MAP:.*]] = affine_map<(d0, d1, d2) -> (d2, d1)>
|
|
# CHECK: #[[$C_MAP:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
|
|
# CHECK: #[[$BTrans_MAP:.*]] = affine_map<(d0, d1, d2) -> (d1, d2)>
|
|
a_map = ir.AffineMap.get(3, 0, [dimM, dimK])
|
|
b_map = ir.AffineMap.get(3, 0, [dimK, dimN])
|
|
c_map = ir.AffineMap.get(3, 0, [dimM, dimN])
|
|
b_transposed_map = ir.AffineMap.get(3, 0, [dimN, dimK])
|
|
|
|
# CHECK: func.func @matmul_as_contract_op(
|
|
@func.FuncOp.from_py_func(
|
|
# CHECK-SAME: %[[A:.*]]: tensor<4x8xf32>,
|
|
RankedTensorType.get(a_shape, f32),
|
|
# CHECK-SAME: %[[Amem:.*]]: memref<4x8xf32>,
|
|
MemRefType.get(a_shape, f32),
|
|
# CHECK-SAME: %[[B:.*]]: tensor<8x12xf32>,
|
|
RankedTensorType.get(b_shape, f32),
|
|
# CHECK-SAME: %[[Bmem:.*]]: memref<8x12xf32>,
|
|
MemRefType.get(b_shape, f32),
|
|
# CHECK-SAME: %[[BTrans:.*]]: tensor<12x8xf32>,
|
|
RankedTensorType.get(b_transposed_shape, f32),
|
|
# CHECK-SAME: %[[BTransmem:.*]]: memref<12x8xf32>,
|
|
MemRefType.get(b_transposed_shape, f32),
|
|
# CHECK-SAME: %[[C:.*]]: tensor<4x12xf32>,
|
|
RankedTensorType.get(c_shape, f32),
|
|
# CHECK-SAME: %[[Cmem:.*]]: memref<4x12xf32>)
|
|
MemRefType.get(c_shape, f32),
|
|
)
|
|
def matmul_as_contract_op(
|
|
A, Amem, B, Bmem, Btransposed, Btransposedmem, C, Cmem
|
|
):
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
op4 = linalg.ContractOp(
|
|
result_tensors=(C.type,),
|
|
inputs=(A, B),
|
|
outputs=(C,),
|
|
indexing_maps=[a_map, b_map, c_map],
|
|
)
|
|
linalg.fill_builtin_region(op4.operation)
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[B]] : tensor<4x8xf32>, tensor<8x12xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
op5 = linalg.contract(
|
|
A, B, outs=(C,), indexing_maps=[a_map, b_map, c_map]
|
|
)
|
|
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
op4 = linalg.ContractOp(
|
|
result_tensors=(C.type,),
|
|
inputs=(A, Btransposed),
|
|
outputs=(C,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
linalg.fill_builtin_region(op4.operation)
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[A]], %[[BTrans]] : tensor<4x8xf32>, tensor<12x8xf32>) outs(%[[C]] : tensor<4x12xf32>)
|
|
op5 = linalg.contract(
|
|
A,
|
|
Btransposed,
|
|
outs=(C,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
# And now with memrefs...
|
|
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
op4 = linalg.ContractOp(
|
|
result_tensors=[],
|
|
inputs=(Amem, Bmem),
|
|
outputs=(Cmem,),
|
|
indexing_maps=[a_map, b_map, c_map],
|
|
)
|
|
linalg.fill_builtin_region(op4.operation)
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$B_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[Bmem]] : memref<4x8xf32>, memref<8x12xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
linalg.contract(
|
|
Amem, Bmem, outs=(Cmem,), indexing_maps=[a_map, b_map, c_map]
|
|
)
|
|
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
op4 = linalg.ContractOp(
|
|
result_tensors=[],
|
|
inputs=(Amem, Btransposedmem),
|
|
outputs=(Cmem,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
linalg.fill_builtin_region(op4.operation)
|
|
# CHECK: linalg.contract indexing_maps = [#[[$A_MAP]], #[[$BTrans_MAP]], #[[$C_MAP]]] ins(%[[Amem]], %[[BTransmem]] : memref<4x8xf32>, memref<12x8xf32>) outs(%[[Cmem]] : memref<4x12xf32>)
|
|
linalg.contract(
|
|
Amem,
|
|
Btransposedmem,
|
|
outs=(Cmem,),
|
|
indexing_maps=[a_map, b_transposed_map, c_map],
|
|
)
|
|
|
|
print(module)
|