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new 4720cf8 [RELAY][PYTORCH]isNan, isinf, isfinite, ceil, clamp, round
ops (#5316)
4720cf8 is described below
commit 4720cf8569e5acd6d5f13f95d78c2f4518c67d55
Author: Samuel <siju.sam...@huawei.com>
AuthorDate: Tue Apr 14 15:15:02 2020 +0530
[RELAY][PYTORCH]isNan, isinf, isfinite, ceil, clamp, round ops (#5316)
* [RELAY][PYTORCH]isNan, isinf, isfinite, ceil, clamp, round ops
* Review comments
---
docs/frontend/tensorflow.rst | 1 +
python/tvm/relay/frontend/pytorch.py | 54 ++++++++++++-
python/tvm/relay/op/_tensor.py | 1 +
python/tvm/relay/op/tensor.py | 16 ++++
src/relay/op/tensor/unary.cc | 11 ++-
tests/python/frontend/pytorch/test_forward.py | 112 ++++++++++++++++++++++++++
6 files changed, 193 insertions(+), 2 deletions(-)
diff --git a/docs/frontend/tensorflow.rst b/docs/frontend/tensorflow.rst
index 45db9e4..a158db9 100644
--- a/docs/frontend/tensorflow.rst
+++ b/docs/frontend/tensorflow.rst
@@ -162,6 +162,7 @@ Supported Ops
- Identity
- IsFinite
- IsInf
+- IsNan
- LeakyRelu
- LeftShift
- Less
diff --git a/python/tvm/relay/frontend/pytorch.py
b/python/tvm/relay/frontend/pytorch.py
index 18868cf..38a811d 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -1118,12 +1118,45 @@ def _sqrt():
return _op.tensor.sqrt(data)
return _impl
+
+def _rsqrt():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.tensor.rsqrt(data)
+ return _impl
+
+
+def _ceil():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.ceil(data)
+ return _impl
+
+
+def _clamp():
+ def _impl(inputs, input_types):
+ print(inputs, input_types)
+ data = inputs[0]
+ amin = inputs[1] if inputs[1] else np.finfo(np.float32).min
+ amax = inputs[2] if inputs[2] else np.finfo(np.float32).max
+ return _op.clip(data, amin, amax)
+ return _impl
+
+
def _floor():
def _impl(inputs, input_types):
data = inputs[0]
return _op.floor(data)
return _impl
+
+def _round():
+ def _impl(inputs, input_types):
+ data = inputs[0]
+ return _op.round(data)
+ return _impl
+
+
def _to():
def _impl(inputs, input_types):
data = inputs[0]
@@ -1232,6 +1265,18 @@ def _mm():
return _impl
+def _isfinite():
+ def _impl(inputs, input_types):
+ return _op.isfinite(inputs[0])
+ return _impl
+
+
+def _isnan():
+ def _impl(inputs, input_types):
+ return _op.isnan(inputs[0])
+ return _impl
+
+
def _list_getitem(prelude):
def _impl(inputs, input_types):
return prelude.nth(inputs[0], _wrap_const(inputs[1]))
@@ -1429,7 +1474,11 @@ def _get_convert_map(prelude):
"aten::std" : _std(),
"aten::var" : _variance(),
"aten::sqrt" : _sqrt(),
- 'aten::floor' : _floor(),
+ "aten::rsqrt" : _rsqrt(),
+ "aten::ceil" : _ceil(),
+ "aten::clamp" : _clamp(),
+ "aten::floor" : _floor(),
+ "aten::round" : _round(),
"aten::detach" : _identity(),
"aten::upsample_bilinear2d" : _upsample("bilinear"),
"aten::upsample_nearest2d" :
_upsample("nearest_neighbor"),
@@ -1439,6 +1488,9 @@ def _get_convert_map(prelude):
"aten::le" : _elemwise("less_equal"),
"aten::ge" : _elemwise("greater_equal"),
"aten::ne" : _elemwise("not_equal"),
+ "aten::eq" : _elemwise("equal"),
+ "aten::isfinite" : _isfinite(),
+ "aten::isnan" : _isnan(),
"aten::Bool" : _Bool(),
"aten::Float" : _Float(),
"aten::neg" : _neg(),
diff --git a/python/tvm/relay/op/_tensor.py b/python/tvm/relay/op/_tensor.py
index a607a47..79a623d 100644
--- a/python/tvm/relay/op/_tensor.py
+++ b/python/tvm/relay/op/_tensor.py
@@ -66,6 +66,7 @@ register_broadcast_schedule("less")
register_broadcast_schedule("less_equal")
register_broadcast_schedule("greater")
register_broadcast_schedule("greater_equal")
+register_broadcast_schedule("isnan")
register_broadcast_schedule("isfinite")
register_broadcast_schedule("isinf")
register_injective_schedule("maximum")
diff --git a/python/tvm/relay/op/tensor.py b/python/tvm/relay/op/tensor.py
index 1f481ee..f602407 100644
--- a/python/tvm/relay/op/tensor.py
+++ b/python/tvm/relay/op/tensor.py
@@ -1010,6 +1010,22 @@ def ndarray_size(data, dtype="int32"):
return _make.ndarray_size(data, dtype)
+def isnan(data):
+ """Check nan in input data element-wise.
+
+ Parameters
+ ----------
+ data : relay.Expr
+ The input data
+
+ Returns
+ -------
+ result : relay.Expr
+ The computed result.
+ """
+ return _make.isnan(data)
+
+
def isfinite(data):
"""Compute element-wise finiteness of data.
diff --git a/src/relay/op/tensor/unary.cc b/src/relay/op/tensor/unary.cc
index 4cca8b0..10da11d 100644
--- a/src/relay/op/tensor/unary.cc
+++ b/src/relay/op/tensor/unary.cc
@@ -426,6 +426,15 @@ ElemwiseArbitraryLayout)
.set_support_level(10)
.set_attr<FTVMCompute>("FTVMCompute", NdarraySizeCompute);
+RELAY_REGISTER_UNARY_OP("isnan")
+.describe(R"code(Returns whether the input contains any NaN, computed
element-wise.
+.. math::
+ isnan(x)
+)code" TVM_ADD_FILELINE)
+.set_support_level(3)
+.add_type_rel("IdentityCompRel", IdentityCompRel)
+.set_attr<FTVMCompute>("FTVMCompute", RELAY_UNARY_COMPUTE(topi::isnan));
+
RELAY_REGISTER_UNARY_OP("isfinite")
.describe(R"code(Returns the finiteness of input, computed element-wise.
.. math::
@@ -438,7 +447,7 @@ RELAY_REGISTER_UNARY_OP("isfinite")
RELAY_REGISTER_UNARY_OP("isinf")
.describe(R"code(Returns the infiniteness of input, computed element-wise.
.. math::
- isfinite(x)
+ isinf(x)
)code" TVM_ADD_FILELINE)
.set_support_level(3)
.add_type_rel("IdentityCompRel", IdentityCompRel)
diff --git a/tests/python/frontend/pytorch/test_forward.py
b/tests/python/frontend/pytorch/test_forward.py
index 91e14c6..d9d280f 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -1441,6 +1441,110 @@ def test_forward_variance():
verify_model(Variance5().float().eval(), input_data=input_data)
+
+def test_forward_isfinite():
+ torch.set_grad_enabled(False)
+
+ class IsFinite1(Module):
+ def forward(self, *args):
+ return torch.isfinite(args[0])
+
+ input_data = torch.tensor([1, float('inf'), 2, float('-inf'),
float('nan')]).float()
+ verify_model(IsFinite1().float().eval(), input_data=input_data)
+
+
+def test_forward_isnan():
+ torch.set_grad_enabled(False)
+
+ class IsNan1(Module):
+ def forward(self, *args):
+ return torch.isnan(args[0])
+
+ input_data = torch.tensor([1, float('inf'), 2, float('-inf'),
float('nan')]).float()
+ verify_model(IsNan1().float().eval(), input_data=input_data)
+
+
+def test_forward_isinf():
+ torch.set_grad_enabled(False)
+
+ class IsInf1(Module):
+ def forward(self, *args):
+ return torch.isinf(args[0])
+
+ input_data = torch.tensor([1, float('inf'), 2, float('-inf'),
float('nan')]).float()
+ verify_model(IsInf1().float().eval(), input_data=input_data)
+
+
+def test_forward_rsqrt():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class Rsqrt1(Module):
+ def forward(self, *args):
+ return torch.rsqrt(args[0])
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(Rsqrt1().float().eval(), input_data=input_data)
+
+
+def test_forward_ceil():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class Ceil1(Module):
+ def forward(self, *args):
+ return torch.ceil(args[0])
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(Ceil1().float().eval(), input_data=input_data)
+
+
+def test_forward_clamp():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class Clamp1(Module):
+ def forward(self, *args):
+ return torch.clamp(args[0], min=-0.5, max=0.5)
+
+ class Clamp2(Module):
+ def forward(self, *args):
+ return torch.clamp(args[0], min=-0.3)
+
+ class Clamp3(Module):
+ def forward(self, *args):
+ return torch.clamp(args[0], max=1.0)
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(Clamp1().float().eval(), input_data=input_data)
+ verify_model(Clamp2().float().eval(), input_data=input_data)
+ verify_model(Clamp3().float().eval(), input_data=input_data)
+
+
+def test_forward_floor():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class Floor1(Module):
+ def forward(self, *args):
+ return torch.floor(args[0])
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(Floor1().float().eval(), input_data=input_data)
+
+
+def test_forward_round():
+ torch.set_grad_enabled(False)
+ input_shape = [1, 3, 10, 10]
+
+ class Round1(Module):
+ def forward(self, *args):
+ return torch.round(args[0])
+
+ input_data = torch.rand(input_shape).float()
+ verify_model(Round1().float().eval(), input_data=input_data)
+
+
if __name__ == "__main__":
# Single operator tests
test_forward_add()
@@ -1497,6 +1601,14 @@ if __name__ == "__main__":
test_forward_expand()
test_forward_pow()
test_forward_abs()
+ test_forward_rsqrt()
+ test_forward_ceil()
+ test_forward_clamp()
+ test_forward_floor()
+ test_forward_round()
+ test_forward_isfinite()
+ test_forward_isnan()
+ test_forward_isinf()
test_forward_arange()
test_forward_chunk()
test_forward_split()
