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# RUN: %PYTHON %s
from mlir.dialects import arith, func, linalg
from mlir.dialects.linalg.opdsl.lang import *
from mlir.ir import *
def run(f):
print("\nTEST:", f.__name__)
f()
return f
@run
def test_infer_contraction_dimensions_from_ops():
with Context(), Location.unknown():
module = Module.create()
f32 = F32Type.get()
with InsertionPoint(module.body):
# === Static shapes ===
m, n, k = 4, 4, 4
a_type = RankedTensorType.get((m, k), f32)
b_type = RankedTensorType.get((k, n), f32)
c_type = RankedTensorType.get((m, n), f32)
@func.FuncOp.from_py_func(a_type, b_type, c_type)
def contraction_fn(a, b, c):
zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.0), result=f32)
filled = linalg.fill(zero, outs=[c])
fill_op = filled.owner
assert not linalg.isa_contraction_op(zero.operation)
assert not linalg.isa_contraction_op(fill_op)
assert linalg.infer_contraction_dimensions(fill_op) is None
dim_m = AffineDimExpr.get(0)
dim_n = AffineDimExpr.get(1)
dim_k = AffineDimExpr.get(2)
a_map = AffineMap.get(3, 0, [dim_m, dim_k])
b_map = AffineMap.get(3, 0, [dim_k, dim_n])
c_map = AffineMap.get(3, 0, [dim_m, dim_n])
result = linalg.contract(
a,
b,
outs=(filled,),
indexing_maps=[a_map, b_map, c_map],
)
contraction_op = result.owner
assert linalg.isa_contraction_op(contraction_op)
dims = linalg.infer_contraction_dimensions(contraction_op)
assert dims is not None
# Expect m=[0], n=[1], k=[2] as per standard matmul.
assert list(dims.m) == [0], f"Expected m=[0], got {list(dims.m)}"
assert list(dims.n) == [1], f"Expected n=[1], got {list(dims.n)}"
assert list(dims.k) == [2], f"Expected k=[2], got {list(dims.k)}"
assert (
list(dims.batch) == []
), f"Expected batch=[], got {list(dims.batch)}"
# === Dynamic shape case ===
dyn = ShapedType.get_dynamic_size()
a_dyn_type = RankedTensorType.get((4, dyn), f32)
b_dyn_type = RankedTensorType.get((dyn, 4), f32)
c_type = RankedTensorType.get((4, 4), f32)
@func.FuncOp.from_py_func(a_dyn_type, b_dyn_type, c_type)
def dynamic_contraction_fn(a, b, c):
zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.0), result=f32)
filled = linalg.fill(zero, outs=[c])
dim_m = AffineDimExpr.get(0)
dim_n = AffineDimExpr.get(1)
dim_k = AffineDimExpr.get(2)
a_map = AffineMap.get(3, 0, [dim_m, dim_k])
b_map = AffineMap.get(3, 0, [dim_k, dim_n])
c_map = AffineMap.get(3, 0, [dim_m, dim_n])
result = linalg.contract(
a,
b,
outs=(filled,),
indexing_maps=[a_map, b_map, c_map],
)
contraction_op = result.owner
assert linalg.isa_contraction_op(contraction_op)
dims = linalg.infer_contraction_dimensions(contraction_op)
assert dims is not None
assert list(dims.m) == [0], f"Expected m=[0], got {list(dims.m)}"
assert list(dims.n) == [1], f"Expected n=[1], got {list(dims.n)}"
assert list(dims.k) == [2], f"Expected k=[2], got {list(dims.k)}"
assert (
list(dims.batch) == []
), f"Expected batch=[], got {list(dims.batch)}"
@run
def test_infer_convolution_dimensions_from_ops():
with Context(), Location.unknown():
module = Module.create()
f32 = F32Type.get()
with InsertionPoint(module.body):
# === Static shapes ===
batch, h, w, c_in, kh, kw, c_out = 1, 8, 8, 4, 3, 3, 16
input_type = RankedTensorType.get((batch, h, w, c_in), f32)
filter_type = RankedTensorType.get((kh, kw, c_in, c_out), f32)
output_type = RankedTensorType.get(
(batch, h - kh + 1, w - kw + 1, c_out), f32
)
@func.FuncOp.from_py_func(input_type, filter_type, output_type)
def conv_fn(input, filter, output):
zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.0), result=f32)
filled = linalg.fill(zero, outs=[output])
fill_op = filled.owner
assert not linalg.isa_convolution_op(fill_op)
assert linalg.infer_convolution_dimensions(fill_op) is None
result = linalg.conv_2d_nhwc_hwcf(input, filter, outs=[filled])
conv_op = result.owner
assert linalg.isa_convolution_op(conv_op)
dims = linalg.infer_convolution_dimensions(conv_op)
assert dims is not None
assert list(dims.batch) == [0]
assert list(dims.output_image) == [1, 2]
assert list(dims.output_channel) == [3]
assert list(dims.filter_loop) == [4, 5]
assert list(dims.input_channel) == [6]
assert list(dims.depth) == []
assert list(dims.strides) == [1, 1]
assert list(dims.dilations) == [1, 1]
# === Dynamic shapes ===
dyn = ShapedType.get_dynamic_size()
dyn_input_type = RankedTensorType.get((batch, dyn, dyn, c_in), f32)
dyn_output_type = RankedTensorType.get((batch, dyn, dyn, c_out), f32)
@func.FuncOp.from_py_func(dyn_input_type, filter_type, dyn_output_type)
def dyn_conv_fn(input, filter, output):
zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.0), result=f32)
filled = linalg.fill(zero, outs=[output])
result = linalg.conv_2d_nhwc_hwcf(input, filter, outs=[filled])
conv_op = result.owner
assert linalg.isa_convolution_op(conv_op)
dims = linalg.infer_convolution_dimensions(conv_op)
assert dims is not None
assert list(dims.batch) == [0]
assert list(dims.output_image) == [1, 2]
assert list(dims.output_channel) == [3]
assert list(dims.filter_loop) == [4, 5]
assert list(dims.input_channel) == [6]
assert list(dims.depth) == []
assert list(dims.strides) == [1, 1]
assert list(dims.dilations) == [1, 1]
@run
def test_get_indexing_maps_attr():
with Context(), Location.unknown():
module = Module.create()
f32 = F32Type.get()
with InsertionPoint(module.body):
a_type = RankedTensorType.get((4, 8), f32)
b_type = RankedTensorType.get((8, 16), f32)
c_type = RankedTensorType.get((4, 16), f32)
dim_m = AffineDimExpr.get(0)
dim_n = AffineDimExpr.get(1)
dim_k = AffineDimExpr.get(2)
a_map = AffineMap.get(3, 0, [dim_m, dim_k])
b_map = AffineMap.get(3, 0, [dim_k, dim_n])
c_map = AffineMap.get(3, 0, [dim_m, dim_n])
@func.FuncOp.from_py_func(a_type, b_type, c_type)
def matmul_func(a, b, c):
zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.0), result=f32)
assert not linalg.get_indexing_maps(
zero.operation
), "Expected no indexing_maps on non-linalg op"
init = linalg.fill(zero, outs=[c])
fill_op = init.owner
fill_maps = linalg.get_indexing_maps(fill_op)
assert fill_maps is not None
assert len(fill_maps) == 2
# The fill op should have maps like (d0, d1) -> () and (d0, d1).
fill_input_map = fill_maps[0].value
fill_output_map = fill_maps[1].value
assert fill_input_map == AffineMap.get(2, 0, [])
assert fill_output_map == AffineMap.get(2, 0, [dim_m, dim_n])
result = linalg.matmul(a, b, outs=(init,))
matmul_op = result.owner
matmul_maps = linalg.get_indexing_maps(matmul_op)
assert matmul_maps is not None
assert len(matmul_maps) == 3
maps = [map_attr.value for map_attr in matmul_maps]
assert maps[0] == a_map
assert maps[1] == b_map
assert maps[2] == c_map
@run
def test_infer_contraction_dimensions_from_maps():
with Context(), Location.unknown():
module = Module.create()
with InsertionPoint(module.body):
# === Test valid contraction (matmul) ===
dim_m = AffineDimExpr.get(0)
dim_n = AffineDimExpr.get(1)
dim_k = AffineDimExpr.get(2)
a_map = AffineMap.get(3, 0, [dim_m, dim_k])
b_map = AffineMap.get(3, 0, [dim_k, dim_n])
c_map = AffineMap.get(3, 0, [dim_m, dim_n])
dims = linalg.infer_contraction_dimensions_from_maps([a_map, b_map, c_map])
assert dims is not None
# Expect m=[0], n=[1], k=[2] as per standard matmul.
assert list(dims.m) == [0], f"Expected m=[0], got {list(dims.m)}"
assert list(dims.n) == [1], f"Expected n=[1], got {list(dims.n)}"
assert list(dims.k) == [2], f"Expected k=[2], got {list(dims.k)}"
assert list(dims.batch) == [], f"Expected batch=[], got {list(dims.batch)}"
# === Test invalid input (wrong number of maps) ===
invalid_dims = linalg.infer_contraction_dimensions_from_maps([a_map, b_map])
assert invalid_dims is None
# === Test element-wise operation ===
dim_i = AffineDimExpr.get(0)
dim_j = AffineDimExpr.get(1)
elementwise_map = AffineMap.get(2, 0, [dim_i, dim_j])
elementwise_dims = linalg.infer_contraction_dimensions_from_maps(
[elementwise_map, elementwise_map, elementwise_map]
)
assert elementwise_dims is not None
assert len(elementwise_dims.m) == 0
assert len(elementwise_dims.n) == 0
assert len(elementwise_dims.k) == 0
assert list(elementwise_dims.batch) == [0, 1]
|
