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[Torch] Clean up usage of try ... infer_value() ... except (apache#6504)
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* clean up infer value usage

* try silence pylint

* remove unused variable

* make on_failuare optional

* make on_success optional True

Co-authored-by: masa <[email protected]>
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2 people authored and Tushar Dey committed Oct 13, 2020
1 parent c4e01bd commit cac279e
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Showing 2 changed files with 44 additions and 35 deletions.
17 changes: 17 additions & 0 deletions python/tvm/relay/frontend/common.py
Original file line number Diff line number Diff line change
Expand Up @@ -563,6 +563,23 @@ def infer_value_simulated(input_val, params):
return output_value


def try_infer_value(val, on_success=None, on_failure=None):
"""Try running infer_value on the input val, and if successful, return the inferred value or
pass it to on_success callback if provided. Otherwise, run on_failure callback if it is
provided, or return the input val as output. In each case, the second return value
indicates whether infer_value has succeeded or not.
"""
try:
ret = infer_value(val, {}).asnumpy()
if on_success:
return on_success(ret), True
return ret, True
except Exception:
if on_failure:
return on_failure(), False
return val, False


def new_var(name_hint, type_annotation=None, shape=None, dtype="float32"):
return _expr.var(name_hint, type_annotation, shape, dtype)

Expand Down
62 changes: 27 additions & 35 deletions python/tvm/relay/frontend/pytorch.py
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
# under the License.
# pylint: disable=import-self, too-many-lines, len-as-condition, no-else-return, unused-variable, too-many-nested-blocks
# pylint: disable=consider-iterating-dictionary, invalid-name, unused-argument, unused-variable, broad-except
# pylint: disable=import-outside-toplevel, simplifiable-if-expression, unnecessary-comprehension
# pylint: disable=import-outside-toplevel, simplifiable-if-expression, cell-var-from-loop, unnecessary-lambda
"""PT: PyTorch frontend."""
import itertools
import logging
Expand All @@ -36,6 +36,7 @@
from .common import AttrCvt, get_relay_op
from .common import infer_shape as _infer_shape
from .common import infer_value as _infer_value
from .common import try_infer_value
from .common import infer_value_simulated as _infer_value_simulated
from .common import infer_type as _infer_type
from ..prelude import Prelude, StaticTensorArrayOps
Expand Down Expand Up @@ -185,11 +186,8 @@ def _impl(inputs, input_types):
def _get_value(val, dtype):
# dtype is a tvm dtype
if isinstance(val, _expr.Expr):
try:
ret = _infer_value(_op.cast(val, dtype), {}).asnumpy()
ret = _expr.const(ret, dtype)
except Exception:
ret = _op.cast(val, dtype)
inp = _op.cast(val, dtype)
ret, _ = try_infer_value(inp, lambda ret: _expr.const(ret, dtype))
else:
ret = _create_typed_const(val, dtype)
return ret
Expand Down Expand Up @@ -305,10 +303,7 @@ def _impl(inputs, input_types):
dim = int(inputs[1])
stride = int(inputs[4])
if isinstance(inputs[2], _expr.Call):
try:
begin[dim] = np.asscalar(_infer_value(inputs[2], {}).asnumpy().astype(np.int))
except Exception:
begin[dim] = inputs[2]
begin[dim], _ = try_infer_value(inputs[2], lambda ret: np.asscalar(ret.astype(np.int)))
else:
begin[dim] = int(inputs[2])

Expand All @@ -329,10 +324,9 @@ def _impl(inputs, input_types):
target_end = int(inputs[3])
else:
if isinstance(inputs[3], _expr.Expr):
try:
target_end = np.asscalar(_infer_value(inputs[3], {}).asnumpy().astype(np.int))
except Exception:
target_end = inputs[3]
target_end, _ = try_infer_value(
inputs[3], lambda ret: np.asscalar(ret.astype(np.int))
)
else:
target_end = inputs[3]

Expand Down Expand Up @@ -457,10 +451,7 @@ def _impl(inputs, input_types):
sort = bool(inputs[4])

if isinstance(inputs[1], _expr.Expr):
try:
k = _infer_value(inputs[1], {}).asnumpy().tolist()
except Exception:
k = inputs[1]
k, _ = try_infer_value(inputs[1], lambda ret: ret.tolist())
else:
k = inputs[1]

Expand Down Expand Up @@ -546,15 +537,15 @@ def _full_impl(data, fill_value, dtype):
size.append(dim)
new_shape.append(dim)
else:
try:
dim = int(_infer_value(dim, {}).asnumpy())
dim, success = try_infer_value(dim, lambda ret: int(ret), lambda: 0)
new_shape.append(dim)

if success:
if isinstance(size, list):
size.append(dim)
new_shape.append(dim)
except Exception:
else:
size = None
need_reshape = True
new_shape.append(0)
else:
if isinstance(size, list):
size.append(dim)
Expand Down Expand Up @@ -1346,12 +1337,11 @@ def _impl(inputs, input_types):
if isinstance(s, _expr.Constant):
tmp_shape.append(int(s.data.asnumpy()))
elif isinstance(s, _expr.Expr):
try:
dim = int(_infer_value(s, {}).asnumpy())
tmp_shape.append(dim)
except Exception:
dim, success = try_infer_value(s, lambda ret: int(ret))
tmp_shape.append(dim)

if not success:
is_dyn = True
tmp_shape.append(s)
else:
tmp_shape.append(s)

Expand Down Expand Up @@ -2312,13 +2302,15 @@ def _impl(inputs, input_types):
if isinstance(inputs[1], _expr.Expr):
out_size = inputs[1]
elif isinstance(inputs[1], list):
try:
infer_res = [_infer_value(size, {}) for size in inputs[1]]
out_size = [np.asscalar(res.asnumpy().astype(np.int)) for res in infer_res]
except Exception:
h = _op.expand_dims(inputs[1][0], axis=0)
w = _op.expand_dims(inputs[1][1], axis=0)
out_size = _op.concatenate([h, w], axis=0)
out_size = []
for i in [0, 1]:
size, _ = try_infer_value(
inputs[1][i],
lambda ret: ret.astype(np.int),
lambda: _op.expand_dims(inputs[1][i], axis=0),
)
out_size.append(size)
out_size = _op.concatenate(out_size, axis=0)

data = inputs[0]
align_corners = inputs[4]
Expand Down

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