Add gradient for log-softmax (#4069)

diff --git a/python/tvm/relay/op/_tensor_grad.py b/python/tvm/relay/op/_tensor_grad.py
index fe22f45..3a82e46 100644 (file)
--- a/python/tvm/relay/op/_tensor_grad.py
+++ b/python/tvm/relay/op/_tensor_grad.py
@@ -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"""

diff --git a/tests/python/relay/test_op_grad_level1.py b/tests/python/relay/test_op_grad_level1.py
index ba35265..114bda0 100644 (file)
--- a/tests/python/relay/test_op_grad_level1.py
+++ b/tests/python/relay/test_op_grad_level1.py
@@ -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
@@ -100,7 +101,13 @@ def test_binary_op():
 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():
@@ -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__])