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https://github.com/zebrajr/pytorch.git
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8e99824ce7
Summary: I'm using Python ops in a project and need corresponding Python gradient ops. For my use case, only a subset of the forward op outputs have gradients and only a subset of forward op inputs have gradients. However the current implementation of `GetPythonGradient` forces all grad inputs and outputs to exist. This diff allows one to specify that only a subset of grad inputs / outputs are used when constructing the Python op. I'm not sure if this is up to caffe2 standards, so please push back on style and content as needed. Reviewed By: dzhulgakov Differential Revision: D4897004 fbshipit-source-id: 96fffe8634c51a49b6bce7339a46c6235f7d4bbd
198 lines
7.3 KiB
Python
198 lines
7.3 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core, workspace
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from caffe2.python.core import CreatePythonOperator
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import caffe2.python.hypothesis_test_util as hu
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from hypothesis import given
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import hypothesis.strategies as st
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import numpy as np
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def SubFunctionThatThrowsRuntimeError():
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raise RuntimeError("This is an intentional exception.")
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def MainOpFunctionThatThrowsRuntimeError(inputs, _):
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return SubFunctionThatThrowsRuntimeError()
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class PythonOpTest(hu.HypothesisTestCase):
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@given(x=hu.tensor())
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def test_feed(self, x):
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def f(inputs, _):
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self.assertEqual(x.shape, inputs[0].shape)
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self.assertEqual(type(inputs[0].shape), tuple)
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self.assertEqual(type(inputs[0].data), np.ndarray)
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np.testing.assert_almost_equal(x, inputs[0].data)
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op = CreatePythonOperator(f, ["x"], [])
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workspace.FeedBlob("x", x)
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workspace.RunOperatorOnce(op)
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def test_exception(self):
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op = CreatePythonOperator(MainOpFunctionThatThrowsRuntimeError, [], [])
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with self.assertRaises(RuntimeError):
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workspace.RunOperatorOnce(op)
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@given(x=hu.tensor())
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def test_feed_with_helper_function(self, x):
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def f(inputs, _):
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self.assertEqual(x.shape, inputs[0].shape)
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self.assertEqual(type(inputs[0].shape), tuple)
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self.assertEqual(type(inputs[0].data), np.ndarray)
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np.testing.assert_almost_equal(x, inputs[0].data)
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net = core.Net("test")
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net.Python(f)(["x"], [])
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workspace.FeedBlob("x", x)
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workspace.RunNetOnce(net)
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@given(x=hu.tensor())
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def test_feed_with_gc(self, x):
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def f(inputs, _):
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self.assertEqual(x.shape, inputs[0].shape)
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np.testing.assert_almost_equal(x, inputs[0].data)
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op = CreatePythonOperator(f, ["x"], [])
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workspace.FeedBlob("x", x)
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workspace.RunOperatorOnce(op)
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del f
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workspace.FeedBlob("x", x)
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workspace.RunOperatorOnce(op)
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@given(x=hu.tensor())
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def test_reshape(self, x):
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def f(inputs, outputs):
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outputs[0].reshape(inputs[0].shape)
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self.assertEqual(x.shape, inputs[0].shape)
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self.assertEqual(x.shape, outputs[0].shape)
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outputs[0].data[...] = inputs[0].data
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op = CreatePythonOperator(f, ["x"], ["y"])
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workspace.FeedBlob("x", x)
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workspace.RunOperatorOnce(op)
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y = workspace.FetchBlob("y")
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np.testing.assert_almost_equal(x, y)
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@given(x=hu.tensor())
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def test_workspace_manipulation(self, x):
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"""
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Verify that python op can manipulate workspace directly
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"""
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def f(inputs, outputs, ws):
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fetched = ws.blobs['internal'].fetch()
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np.testing.assert_almost_equal(fetched, x)
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ws = workspace.C.Workspace()
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net = core.Net("test")
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net.GivenTensorFill([], ['internal'], values=x, shape=x.shape)
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net.Python(f, pass_workspace=True)([], [])
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ws.run(net)
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@given(x=hu.tensor())
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def test_caught_exception_doesnt_terminate(self, x):
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def f(inputs, outputs):
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try:
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raise Exception("Exception in handler")
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except Exception:
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pass
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op = CreatePythonOperator(f, ["x"], ["y"])
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workspace.FeedBlob("x", x)
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workspace.RunOperatorOnce(op)
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@given(x=hu.tensor(),
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n=st.integers(min_value=1, max_value=20),
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w=st.integers(min_value=1, max_value=20))
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def test_multithreaded_evaluation(self, x, n, w):
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def f(inputs, outputs):
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outputs[0].reshape(inputs[0].shape)
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outputs[0].data[...] = inputs[0].data
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ops = [CreatePythonOperator(f, ["x"], [str(i)]) for i in range(n)]
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net = core.Net("net")
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net.Proto().op.extend(ops)
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net.Proto().type = "dag"
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net.Proto().num_workers = w
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iters = 100
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plan = core.Plan("plan")
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plan.AddStep(core.ExecutionStep("test-step", net, iters))
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workspace.FeedBlob("x", x)
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workspace.RunPlan(plan.Proto().SerializeToString())
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for i in range(n):
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y = workspace.FetchBlob(str(i))
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np.testing.assert_almost_equal(x, y)
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@given(x=hu.tensor(), in_place=st.booleans(), **hu.gcs)
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def test_gradient(self, x, in_place, gc, dc):
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def f(inputs, outputs):
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outputs[0].reshape(inputs[0].shape)
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outputs[0].data[...] = inputs[0].data * 2
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def grad_f(inputs, outputs):
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# Ordering is [inputs, outputs, grad_outputs]
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grad_output = inputs[2]
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grad_input = outputs[0]
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grad_input.reshape(grad_output.shape)
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grad_input.data[...] = grad_output.data * 2
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op = CreatePythonOperator(
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f, ["x"], ["x" if in_place else "y"], grad_f=grad_f)
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self.assertGradientChecks(gc, op, [x], 0, [0])
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self.assertDeviceChecks(dc, op, [x], [0])
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@given(inputs=hu.tensors(n=2), **hu.gcs)
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def test_gradient_multiple(self, inputs, gc, dc):
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(x1, x2) = inputs
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def f(inputs, outputs):
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for idx in [0, 1]:
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self.assertEqual(type(inputs[idx].shape), tuple)
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outputs[idx].reshape(inputs[idx].shape)
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outputs[idx].data[...] = inputs[idx].data * 2
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def grad_f(inputs, outputs):
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# Ordering is [inputs, outputs, grad_outputs]
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self.assertEqual(len(inputs), 6)
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self.assertEqual(len(outputs), 2)
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for (grad_output_idx, grad_input_idx) in [(4, 0), (5, 1)]:
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grad_output = inputs[grad_output_idx]
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grad_input = outputs[grad_input_idx]
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grad_input.reshape(grad_output.shape)
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grad_input.data[...] = grad_output.data * 2
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op = CreatePythonOperator(f, ["x1", "x2"], ["y1", "y2"], grad_f=grad_f)
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for idx in [0, 1]:
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self.assertGradientChecks(gc, op, [x1, x2], idx, [0, 1])
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self.assertDeviceChecks(dc, op, [x1, x2], [0, 1])
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@given(inputs=hu.tensors(n=3), **hu.gcs)
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def test_gradient_multiple_with_indicies(self, inputs, gc, dc):
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(x1, x2, x3) = inputs
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def f(inputs, outputs):
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for idx in [0, 1, 2]:
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self.assertEqual(type(inputs[idx].shape), tuple)
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outputs[idx].reshape(inputs[idx].shape)
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outputs[idx].data[...] = inputs[idx].data * 2
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def grad_f(inputs, outputs):
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# Ordering is [inputs, outputs, grad_outputs]
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self.assertEqual(len(inputs), 8)
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self.assertEqual(len(outputs), 1)
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for (grad_output_idx, grad_input_idx) in [(6, 0)]:
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grad_output = inputs[grad_output_idx]
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grad_input = outputs[grad_input_idx]
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grad_input.reshape(grad_output.shape)
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grad_input.data[...] = grad_output.data * 2
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op = CreatePythonOperator(
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f, ["x1", "x2", "x3"], ["y1", "y2", "y3"],
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grad_f=grad_f,
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grad_output_indices=[0, 2], # Receive grad outputs for y1 and y3
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grad_input_indices=[0] # Produce grad inputs for x1
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)
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self.assertGradientChecks(gc, op, [x1, x2, x3], 0, [0, 2])
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self.assertDeviceChecks(dc, op, [x1, x2, x3], [0, 1, 2])
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