def make_sdfg(node, parent_state, parent_sdfg):
# Get metadata from parent SDFG
((edge_a, outer_array_a, shape_a, strides_a), (edge_b, outer_array_b,
shape_b, strides_b),
cdata) = _get_matmul_operands(node, parent_state, parent_sdfg)
outedge = parent_state.out_edges(node)[0]
cdesc = parent_sdfg.arrays[outedge.data.data]
bopt = _get_batchmm_opts(shape_a, strides_a, shape_b, strides_b,
cdesc.shape, cdesc.strides)
if shape_a[-1] != shape_b[-2]:
raise SyntaxError('Matrix sizes must match')
if bopt:
shape_c = (bopt['b'], shape_a[-2], shape_b[-1])
else:
shape_c = (shape_a[-2], shape_b[-1])
dtype_a = outer_array_a.dtype.type
dtype_b = outer_array_b.dtype.type
dtype_c = cdesc.dtype.type
if outer_array_a.storage != outer_array_b.storage:
raise ValueError("Input matrices must have same storage")
storage = outer_array_a.storage
# Create replacement SDFG
sdfg = dace.SDFG(node.label + "_sdfg")
_, array_a = sdfg.add_array("_a",
shape_a,
dtype_a,
strides=strides_a,
storage=storage)
_, array_b = sdfg.add_array("_b",
shape_b,
dtype_b,
strides=strides_b,
storage=storage)
_, array_c = sdfg.add_array("_c",
shape_c,
dtype_c,
strides=cdata[-1],
storage=storage)
# Add an initialization state
init_state = sdfg.add_state()
init_state.add_mapped_tasklet(
'batched_matmul_init',
{'_o%d' % i: '0:%s' % symstr(d)
for i, d in enumerate(shape_c)}, {},
'out = 0', {
'out':
dace.Memlet.simple(
'_c', ','.join(['_o%d' % i for i in range(len(shape_c))]))
},
external_edges=True)
state = sdfg.add_state_after(init_state, node.label + "_state")
state.add_mapped_tasklet(
'_BatchedBatchedMatMult_', {
'__i%d' % i: '0:%s' % s
for i, s in enumerate([
bopt['b'], array_a.shape[-2], array_b.shape[-1],
array_a.shape[-1]
])
}, {
'__a':
dace.Memlet.simple("_a", ('__i1, __i3' if len(array_a.shape)
== 2 else '__i0, __i1, __i3')),
'__b':
dace.Memlet.simple("_b", ('__i3, __i2' if len(array_b.shape)
== 2 else '__i0, __i3, __i2'))
},
'__c = __a * __b', {
'__c':
dace.Memlet.simple(
"_c", '__i0, __i1, __i2', wcr_str='lambda x, y: x + y')
},
external_edges=True)
return sdfg
def _matmult(visitor, sdfg: SDFG, state: SDFGState, op1: str, op2: str):
from dace.libraries.blas.nodes.matmul import MatMul # Avoid import loop
arr1 = sdfg.arrays[op1]
arr2 = sdfg.arrays[op2]
if len(arr1.shape) > 1 and len(arr2.shape) > 1: # matrix * matrix
if len(arr1.shape) > 3 or len(arr2.shape) > 3:
raise SyntaxError(
'Matrix multiplication of tensors of dimensions > 3 '
'not supported')
if arr1.shape[-1] != arr2.shape[-2]:
raise SyntaxError('Matrix dimension mismatch %s != %s' %
(arr1.shape[-1], arr2.shape[-2]))
from dace.libraries.blas.nodes.matmul import _get_batchmm_opts
# Determine batched multiplication
bopt = _get_batchmm_opts(arr1.shape, arr1.strides, arr2.shape,
arr2.strides, None, None)
if bopt:
output_shape = (bopt['b'], arr1.shape[-2], arr2.shape[-1])
else:
output_shape = (arr1.shape[-2], arr2.shape[-1])
elif len(arr1.shape) == 2 and len(arr2.shape) == 1: # matrix * vector
if arr1.shape[1] != arr2.shape[0]:
raise SyntaxError("Number of matrix columns {} must match"
"size of vector {}.".format(
arr1.shape[1], arr2.shape[0]))
output_shape = (arr1.shape[0], )
elif len(arr1.shape) == 1 and len(arr2.shape) == 1: # vector * vector
if arr1.shape[0] != arr2.shape[0]:
raise SyntaxError("Vectors in vector product must have same size: "
"{} vs. {}".format(arr1.shape[0], arr2.shape[0]))
output_shape = (1, )
else: # Dunno what this is, bail
raise SyntaxError(
"Cannot multiply arrays with shapes: {} and {}".format(
arr1.shape, arr2.shape))
type1 = arr1.dtype.type
type2 = arr2.dtype.type
restype = dace.DTYPE_TO_TYPECLASS[np.result_type(type1, type2).type]
op3, arr3 = sdfg.add_temp_transient(output_shape, restype, arr1.storage)
acc1 = state.add_read(op1)
acc2 = state.add_read(op2)
acc3 = state.add_write(op3)
tasklet = MatMul('_MatMult_', restype)
state.add_node(tasklet)
state.add_edge(acc1, None, tasklet, '_a', dace.Memlet.from_array(op1, arr1))
state.add_edge(acc2, None, tasklet, '_b', dace.Memlet.from_array(op2, arr2))
state.add_edge(tasklet, '_c', acc3, None, dace.Memlet.from_array(op3, arr3))
return op3
