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Add gradient for log-softmax #4069

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merged 1 commit into from
Oct 7, 2019
Merged

Add gradient for log-softmax #4069

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9 changes: 9 additions & 0 deletions python/tvm/relay/op/_tensor_grad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,6 +305,15 @@ def softmax_grad(orig, grad):
return [(grad - _sum(grad * orig, orig.attrs.axis, True)) * orig]


@register_gradient("nn.log_softmax")
def log_softmax_grad(orig, grad):
"""Gradient of log_softmax"""
x = orig.args[0]
sm = _nn.softmax(x, axis=orig.attrs.axis)
grad = grad / sm
return softmax_grad(sm, grad)


@register_gradient("nn.bias_add")
def bias_add_grad(orig, grad):
"""Returns gradient of bias_add"""
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13 changes: 9 additions & 4 deletions tests/python/relay/test_op_grad_level1.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,7 @@
# specific language governing permissions and limitations
# under the License.
import numpy as np
import pytest

import tvm
from tvm import relay
Expand Down Expand Up @@ -100,7 +101,13 @@ def check_binary_op(opfunc, ref):
def test_softmax_grad():
data = relay.var("data", relay.TensorType((1, 16), "float64"))
fwd_func = relay.Function([data], relay.nn.softmax(data))
check_grad(fwd_func)
check_grad(fwd_func, scale=1)


def test_log_softmax_grad():
data = relay.var("data", relay.TensorType((2, 16), "float64"))
fwd_func = relay.Function([data], relay.nn.log_softmax(data))
check_grad(fwd_func, scale=1)


def test_bias_add_grad():
Expand All @@ -111,6 +118,4 @@ def test_bias_add_grad():


if __name__ == "__main__":
test_unary_op()
test_binary_op()
test_bias_add_grad()
pytest.main([__file__])