From bb88ec515e0729fa8bd160ae5f0822f158735b1a Mon Sep 17 00:00:00 2001
From: Stephan Seitz <stephan.seitz@fau.de>
Date: Wed, 7 Aug 2019 15:42:09 +0200
Subject: [PATCH] Instantiate function class
---
src/pystencils_autodiff/backends/_torch_native.py | 5 ++---
tests/backends/test_torch_native_compilation.py | 4 ++--
2 files changed, 4 insertions(+), 5 deletions(-)
diff --git a/src/pystencils_autodiff/backends/_torch_native.py b/src/pystencils_autodiff/backends/_torch_native.py
index 2fd3c9e..1fab51c 100644
--- a/src/pystencils_autodiff/backends/_torch_native.py
+++ b/src/pystencils_autodiff/backends/_torch_native.py
@@ -139,8 +139,7 @@ def create_autograd_function(autodiff_obj, inputfield_to_tensor_dict, forward_lo
os.mkdir(cache_dir)
# TODO: create function and stuff
- compiled_operation = generate_torch(cache_dir, autodiff_obj, is_cuda,
- dtype)
+ compiled_operation = generate_torch(cache_dir, autodiff_obj, is_cuda, dtype)
field_to_tensor_dict = inputfield_to_tensor_dict
# Allocate output tensor for forward and backward pass
for field in chain(autodiff_obj.forward_output_fields, autodiff_obj.backward_output_fields):
@@ -164,7 +163,7 @@ def create_autograd_function(autodiff_obj, inputfield_to_tensor_dict, forward_lo
cls.saved = None
cls.forward = forward
cls.backward = backward
- return cls
+ return cls()
else:
op = pystencils_autodiff.backends._pytorch.create_autograd_function(autodiff_obj,
inputfield_to_tensor_dict,
diff --git a/tests/backends/test_torch_native_compilation.py b/tests/backends/test_torch_native_compilation.py
index f23fd21..6a7a053 100644
--- a/tests/backends/test_torch_native_compilation.py
+++ b/tests/backends/test_torch_native_compilation.py
@@ -168,7 +168,7 @@ def test_execute_torch():
y_tensor = pystencils_autodiff.torch_tensor_from_field(y, 1, cuda=False)
op_cpp = create_autograd_function(autodiff, {x: x_tensor, y: y_tensor})
- foo = op_cpp.forward(x_tensor)
+ foo = op_cpp.forward()
print(foo)
assert op_cpp is not None
@@ -189,7 +189,7 @@ def test_execute_torch_gpu():
op_cuda = create_autograd_function(autodiff, {x: x_tensor, y: y_tensor})
assert op_cuda is not None
- rtn = op_cuda.forward(y_tensor, x_tensor)
+ rtn = op_cuda.forward()
print(y_tensor)
print(rtn)
--
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