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Posted to commits@tvm.apache.org by ma...@apache.org on 2020/09/22 02:59:34 UTC
[incubator-tvm] branch master updated: [Torch] Clean up usage of
try ... infer_value() ... except (#6504)
This is an automated email from the ASF dual-hosted git repository.
masahi pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/master by this push:
new 0448858 [Torch] Clean up usage of try ... infer_value() ... except (#6504)
0448858 is described below
commit 044885860842e6c9936b45ab4edbd23f0e3c727b
Author: masahi <ma...@gmail.com>
AuthorDate: Tue Sep 22 11:59:00 2020 +0900
[Torch] Clean up usage of try ... infer_value() ... except (#6504)
* clean up infer value usage
* try silence pylint
* remove unused variable
* make on_failuare optional
* make on_success optional True
Co-authored-by: masa <ma...@pop-os.localdomain>
---
python/tvm/relay/frontend/common.py | 17 ++++++++++
python/tvm/relay/frontend/pytorch.py | 62 ++++++++++++++++--------------------
2 files changed, 44 insertions(+), 35 deletions(-)
diff --git a/python/tvm/relay/frontend/common.py b/python/tvm/relay/frontend/common.py
index e4d605a..027d6bd 100644
--- a/python/tvm/relay/frontend/common.py
+++ b/python/tvm/relay/frontend/common.py
@@ -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)
diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 9ceb9fc..c667b04 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -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
@@ -36,6 +36,7 @@ from .. import transform
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
@@ -185,11 +186,8 @@ def _arange():
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
@@ -305,10 +303,7 @@ def _slice():
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])
@@ -329,10 +324,9 @@ def _slice():
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]
@@ -457,10 +451,7 @@ def _topk():
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]
@@ -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)
@@ -1346,12 +1337,11 @@ def _reshape():
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)
@@ -2312,13 +2302,15 @@ def _interpolate():
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]