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[API] Add support for getting and setting tensor slices #404

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24 changes: 21 additions & 3 deletions python/heterocl/api.py
Original file line number Diff line number Diff line change
Expand Up @@ -446,16 +446,34 @@ def print_val(val):
stage = Stage.get_current()
if isinstance(val, (Scalar, _expr.Expr, numbers.Number)):
stage.emit(_make.Print([val], get_format(val) + "\n"))
elif isinstance(val, TensorSlice) \
and sum(isinstance(x, slice) for x in val.indices) > 0:
nshape = len(val.tensor.shape)
startdim = len(val.indices) - sum(isinstance(x, slice) for x in val.indices)
ndim = nshape - startdim
args = ["print_"+str(n) for n in range(0, ndim)]
ivs = [_IterVar((0, val._shape[n]), args[n-startdim], 0) for n in range(startdim, nshape)]
if ndim >= 1:
import builtins
stage.emit(print_tensor(val, ivs, ndim-1, ndim))
stage.emit(_make.Print([], "\n"))
else:
stage.emit(_make.Print([val], get_format(val) + "\n"))
elif isinstance(val, TensorSlice) \
and len(val.indices) == len(val.tensor.shape):
stage.emit(_make.Print([val], get_format(val) + "\n"))
else: # we are dealing with tensors
else:
nshape = len(val.tensor.shape)
ndim = nshape
if isinstance(val, TensorSlice):
ndim = nshape - len(val.indices)
args = ["print_"+str(n) for n in range(0, ndim)]
ivs = [_IterVar((0, val.tensor.shape[nshape-n-1]), args[n], 0) \
startdim = len(val.indices)
args = ["print_"+str(n) for n in range(startdim, nshape)]
ivs = [_IterVar((0, val.tensor.shape[n]), args[n-startdim], 0) \
for n in range(startdim, nshape)]
else:
args = ["print_"+str(n) for n in range(0, ndim)]
ivs = [_IterVar((0, val.tensor.shape[n]), args[n], 0) \
for n in range(0, ndim)]
import builtins
stage.emit(print_tensor(val, ivs, ndim-1, ndim))
Expand Down
2 changes: 1 addition & 1 deletion python/heterocl/compute_api.py
Original file line number Diff line number Diff line change
Expand Up @@ -445,7 +445,7 @@ def copy(tensor, name=None, dtype=None):
B3 = hcl.copy(nA, "B3")
"""
name = get_name("copy", name)
if isinstance(tensor, Tensor):
if isinstance(tensor, (Tensor, TensorSlice)):
return compute(
tensor.shape,
lambda *args: tensor[args],
Expand Down
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