Add support for Tensorflow operators log1p, cos, sin (apache#3614)

The patch adds support for Tensorflow operators log1p and cos Tensorflow log1p is described at https://www.tensorflow.org/api_docs/python/tf/math/log1p Tensorflow cos is described at https://www.tensorflow.org/api_docs/python/tf/math/cos Tensorflow sin is described at https://www.tensorflow.org/api_docs/python/tf/math/sin
wweic · Aug 9, 2019 · cddfa3e · cddfa3e
1 parent 1b834d3
commit cddfa3e
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Showing 19 changed files with 212 additions and 5 deletions.
diff --git a/include/tvm/expr_operator.h b/include/tvm/expr_operator.h
@@ -517,7 +517,8 @@ TVM_DECLARE_INTRIN_UNARY(sqrt);
 TVM_DECLARE_INTRIN_UNARY(rsqrt);
 TVM_DECLARE_INTRIN_UNARY(log);
 TVM_DECLARE_INTRIN_UNARY(popcount);
-
+TVM_DECLARE_INTRIN_UNARY(cos);
+TVM_DECLARE_INTRIN_UNARY(sin);
 
 // Implementation details after this
 inline bool is_const(const Expr& x) {

diff --git a/python/tvm/intrin.py b/python/tvm/intrin.py
@@ -258,6 +258,35 @@ def log(x):
     """
     return call_pure_intrin(x.dtype, "log", x)
 
+def cos(x):
+    """Take cos of input x.
+
+    Parameters
+    ----------
+    x : Expr
+        Input argument.
+
+    Returns
+    -------
+    y : Expr
+        The result.
+    """
+    return call_pure_intrin(x.dtype, "cos", x)
+
+def sin(x):
+    """Take sin of input x.
+
+    Parameters
+    ----------
+    x : Expr
+        Input argument.
+
+    Returns
+    -------
+    y : Expr
+        The result.
+    """
+    return call_pure_intrin(x.dtype, "sin", x)
 
 def sqrt(x):
     """Take square root of input x.

diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py
@@ -1301,6 +1301,13 @@ def _impl(inputs, attr, params):
         return _op.prod(inputs[0], int(axis), keepdims=keepdims)
     return _impl
 
+def _log1p():
+    # op description: https://www.tensorflow.org/api_docs/python/tf/math/log1p
+    def _impl(inputs, attr, params):
+        one = tvm.relay.const(1, attr['T'].name)
+        add_out = _get_relay_op('add')(inputs[0], one)
+        return _get_relay_op('log')(add_out)
+    return _impl
 
 # compatible operators that do NOT require any conversion.
 _identity_list = []
@@ -1354,6 +1361,9 @@ def _impl(inputs, attr, params):
     'Less'                              : _broadcast('less'),
     'LessEqual'                         : _broadcast('less_equal'),
     'Log'                               : AttrCvt('log'),
+    'Log1p'                             : _log1p(),
+    'Cos'                               : AttrCvt('cos'),
+    'Sin'                               : AttrCvt('sin'),
     'LogicalAnd'                        : _logical('logical_and'),
     'LogicalOr'                         : _logical('logical_or'),
     'LogicalNot'                        : _logical('logical_not'),

diff --git a/python/tvm/relay/op/_tensor.py b/python/tvm/relay/op/_tensor.py
@@ -25,6 +25,9 @@
 schedule_elemwise = schedule_injective
 
 register_schedule("log", schedule_broadcast)
+register_schedule("log1p", schedule_broadcast)
+register_schedule("cos", schedule_broadcast)
+register_schedule("sin", schedule_broadcast)
 register_schedule("exp", schedule_broadcast)
 register_schedule("sqrt", schedule_broadcast)
 register_schedule("rsqrt", schedule_broadcast)

diff --git a/python/tvm/relay/op/_tensor_grad.py b/python/tvm/relay/op/_tensor_grad.py
@@ -20,7 +20,7 @@
 from ..expr import const
 from .op import register_gradient
 from .transform import collapse_sum_like, broadcast_to_like, where
-from .tensor import exp, negative, power, less
+from .tensor import exp, negative, power, less, cos, sin
 from .tensor import zeros_like, ones_like
 from . import nn as _nn
 
@@ -31,6 +31,18 @@ def log_grad(orig, grad):
     x = orig.args[0]
     return [grad * ones_like(x) / x]
 
+@register_gradient("cos")
+def cos_grad(orig, grad):
+    """Returns [grad * (-sin(x))]"""
+    x = orig.args[0]
+    ones = ones_like(x)
+    return [grad * (-ones * sin(x))]
+
+@register_gradient("sin")
+def sin_grad(orig, grad):
+    """Returns [grad * cos(x)]"""
+    x = orig.args[0]
+    return [grad * cos(x)]
 
 @register_gradient("exp")
 def exp_grad(orig, grad):

diff --git a/python/tvm/relay/op/tensor.py b/python/tvm/relay/op/tensor.py
@@ -46,6 +46,35 @@ def log(data):
     """
     return _make.log(data)
 
+def cos(data):
+    """Compute elementwise cos of data.
+
+    Parameters
+    ----------
+    data : relay.Expr
+        The input data
+
+    Returns
+    -------
+    result : relay.Expr
+        The computed result.
+    """
+    return _make.cos(data)
+
+def sin(data):
+    """Compute elementwise sin of data.
+
+    Parameters
+    ----------
+    data : relay.Expr
+        The input data
+
+    Returns
+    -------
+    result : relay.Expr
+        The computed result.
+    """
+    return _make.sin(data)
 
 def exp(data):
     """Compute elementwise exp of data.

diff --git a/src/codegen/intrin_rule.cc b/src/codegen/intrin_rule.cc
@@ -37,6 +37,12 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.log")
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.tanh")
 .set_body(DispatchExtern<FloatSuffix>);
 
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.cos")
+.set_body(DispatchExtern<FloatSuffix>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.sin")
+.set_body(DispatchExtern<FloatSuffix>);
+
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.sqrt")
 .set_body(DispatchExtern<FloatSuffix>);
 

diff --git a/src/codegen/intrin_rule_cuda.cc b/src/codegen/intrin_rule_cuda.cc
@@ -95,6 +95,12 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.exp")
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.log")
 .set_body(DispatchExtern<CUDAFastMath>);
 
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.cos")
+.set_body(DispatchExtern<CUDAFastMath>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.sin")
+.set_body(DispatchExtern<CUDAFastMath>);
+
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.tanh")
 .set_body(DispatchExtern<CUDAMath>);
 

diff --git a/src/codegen/llvm/intrin_rule_llvm.cc b/src/codegen/llvm/intrin_rule_llvm.cc
@@ -86,6 +86,12 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.llvm.pow")
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.llvm.popcount")
 .set_body(DispatchLLVMPureIntrin<::llvm::Intrinsic::ctpop, 1>);
 
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.llvm.cos")
+.set_body(DispatchLLVMPureIntrin<::llvm::Intrinsic::cos, 1>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.llvm.sin")
+.set_body(DispatchLLVMPureIntrin<::llvm::Intrinsic::sin, 1>);
+
 }  // namespace llvm
 }  // namespace codegen
 }  // namespace tvm

diff --git a/src/codegen/llvm/intrin_rule_nvptx.cc b/src/codegen/llvm/intrin_rule_nvptx.cc
@@ -79,6 +79,12 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.nvptx.pow")
 TVM_REGISTER_GLOBAL("tvm.intrin.rule.nvptx.tanh")
 .set_body(DispatchExternLibDevice);
 
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.nvptx.cos")
+.set_body(DispatchExternLibDevice);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.nvptx.sin")
+.set_body(DispatchExternLibDevice);
+
 }  // namespace llvm
 }  // namespace codegen
 }  // namespace tvm

diff --git a/src/relay/op/tensor/unary.cc b/src/relay/op/tensor/unary.cc
@@ -53,6 +53,28 @@ RELAY_REGISTER_UNARY_OP("log")
 .set_attr<FTVMCompute>("FTVMCompute", RELAY_UNARY_COMPUTE(topi::log));
 
 
+RELAY_REGISTER_UNARY_OP("cos")
+.describe(R"code(Returns the cos of input array, computed element-wise.
+
+.. math::
+   Y = cos(X)
+
+)code" TVM_ADD_FILELINE)
+.set_support_level(1)
+.set_attr<FTVMCompute>("FTVMCompute", RELAY_UNARY_COMPUTE(topi::cos));
+
+
+RELAY_REGISTER_UNARY_OP("sin")
+.describe(R"code(Returns the sin of input array, computed element-wise.
+
+.. math::
+   Y = sin(X)
+
+)code" TVM_ADD_FILELINE)
+.set_support_level(1)
+.set_attr<FTVMCompute>("FTVMCompute", RELAY_UNARY_COMPUTE(topi::sin));
+
+
 RELAY_REGISTER_UNARY_OP("exp")
 .describe(R"code(Returns the exp input array, computed element-wise.
 

diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py
@@ -1869,6 +1869,30 @@ def test_forward_log():
     tf.log(in_data, name="log")
     compare_tf_with_tvm([np_data], ['in_data:0'], 'log:0')
 
+def test_forward_log1p():
+    """test operator Log1p """
+    np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
+    tf.reset_default_graph()
+    in_data = tf.placeholder(tf.float32, (2, 3, 5), name="in_data")
+    tf.log1p(in_data, name="log1p")
+    compare_tf_with_tvm([np_data], ['in_data:0'], 'log1p:0')
+
+def test_forward_cos():
+    """test operator cos """
+    np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
+    tf.reset_default_graph()
+    in_data = tf.placeholder(tf.float32, (2, 3, 5), name="in_data")
+    tf.cos(in_data, name="cos")
+    compare_tf_with_tvm([np_data], ['in_data:0'], 'cos:0')
+
+def test_forward_sin():
+    """test operator sin """
+    np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
+    tf.reset_default_graph()
+    in_data = tf.placeholder(tf.float32, (2, 3, 5), name="in_data")
+    tf.sin(in_data, name="sin")
+    compare_tf_with_tvm([np_data], ['in_data:0'], 'sin:0')
+
 def test_forward_negative():
     """test tf operator Neg """
     np_data = np.random.uniform(-100, 255, size=(224, 224, 3)).astype(np.float32)
@@ -2159,6 +2183,9 @@ def test_placeholder():
     test_forward_pow_exp()
     test_forward_sign()
     test_forward_log()
+    test_forward_log1p()
+    test_forward_cos()
+    test_forward_sin()
     test_forward_negative()
     test_forward_divide()
     test_forward_abs()

diff --git a/tests/python/relay/test_op_grad_level1.py b/tests/python/relay/test_op_grad_level1.py
@@ -56,7 +56,9 @@ def check_single_op(opfunc, ref):
                         (tvm.relay.tanh, lambda x: 1 - np.tanh(x) * np.tanh(x)),
                         (tvm.relay.sqrt, lambda x: 0.5 * np.power(x, -0.5)),
                         (tvm.relay.abs, lambda x: np.where(x < 0, -np.ones_like(x), np.ones_like(x))),
-                        (relay.nn.relu, lambda x: np.where(x < 0, np.zeros_like(x), np.ones_like(x)))]:
+                        (relay.nn.relu, lambda x: np.where(x < 0, np.zeros_like(x), np.ones_like(x))),
+                        (tvm.relay.cos, lambda x: -1.0 * np.sin(x)),
+                        (tvm.relay.sin, lambda x: np.cos(x))]:
         check_single_op(opfunc, ref)
 
 

diff --git a/tests/python/relay/test_op_level1.py b/tests/python/relay/test_op_level1.py
@@ -71,7 +71,9 @@ def check_single_op(opfunc, ref):
                         (tvm.relay.rsqrt, rsqrt),
                         (tvm.relay.sigmoid, sigmoid),
                         (tvm.relay.tanh, np.tanh),
-                        (relay.nn.relu, relu)]:
+                        (relay.nn.relu, relu),
+                        (tvm.relay.cos, np.cos),
+                        (tvm.relay.sin, np.sin)]:
         check_single_op(opfunc, ref)
 
 

diff --git a/topi/include/topi/elemwise.h b/topi/include/topi/elemwise.h
@@ -54,6 +54,8 @@ TOPI_DECLARE_UNARY_OP(ceil);
 TOPI_DECLARE_UNARY_OP(round);
 TOPI_DECLARE_UNARY_OP(trunc);
 TOPI_DECLARE_UNARY_OP(abs);
+TOPI_DECLARE_UNARY_OP(cos);
+TOPI_DECLARE_UNARY_OP(sin);
 
 /*
  * \brief Fast_tanh_float implementation from Eigen

diff --git a/topi/python/topi/math.py b/topi/python/topi/math.py
@@ -90,6 +90,37 @@ def tanh(x):
     """
     return tvm.compute(x.shape, lambda *i: tvm.tanh(x(*i)))
 
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def cos(x):
+    """Take cos of input x.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    return tvm.compute(x.shape, lambda *i: tvm.cos(x(*i)))
+
+@tvm.tag_scope(tag=tag.ELEMWISE)
+def sin(x):
+    """Take sin of input x.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        Input argument.
+
+    Returns
+    -------
+    y : tvm.Tensor
+        The result.
+    """
+    return tvm.compute(x.shape, lambda *i: tvm.sin(x(*i)))
 
 @tvm.tag_scope(tag=tag.ELEMWISE)
 def floor(x):
@@ -206,7 +237,6 @@ def log(x):
     """
     return tvm.compute(x.shape, lambda *i: tvm.log(x(*i)))
 
-
 @tvm.tag_scope(tag=tag.ELEMWISE)
 def sqrt(x):
     """Take square root of input x.

diff --git a/topi/src/topi.cc b/topi/src/topi.cc
@@ -148,6 +148,16 @@ TVM_REGISTER_GLOBAL("topi.exp")
   *rv = exp(args[0]);
   });
 
+TVM_REGISTER_GLOBAL("topi.cos")
+.set_body([](TVMArgs args, TVMRetValue *rv) {
+  *rv = cos(args[0]);
+  });
+
+TVM_REGISTER_GLOBAL("topi.sin")
+.set_body([](TVMArgs args, TVMRetValue *rv) {
+  *rv = sin(args[0]);
+  });
+
 TVM_REGISTER_GLOBAL("topi.tanh")
 .set_body([](TVMArgs args, TVMRetValue *rv) {
   *rv = tanh(args[0]);

diff --git a/topi/tests/python/test_topi_basic.py b/topi/tests/python/test_topi_basic.py
@@ -41,6 +41,8 @@ def test_apply(func, name):
     test_apply(topi.log, "log")
     test_apply(topi.sqrt, "sqrt")
     test_apply(topi.rsqrt, "rsqrt")
+    test_apply(topi.sin, "sin")
+    test_apply(topi.cos, "cos")
 
 
 if __name__ == "__main__":

diff --git a/topi/tests/python/test_topi_math.py b/topi/tests/python/test_topi_math.py
@@ -84,6 +84,8 @@ def check_device(device):
     test_apply(topi.log, "log", np.log, 0, 100)
     test_apply(topi.sqrt, "sqrt", np.sqrt, 0, 100)
     test_apply(topi.rsqrt, "rsqrt", lambda x: np.ones_like(x) / np.sqrt(x), 0, 100, skip_name_check=True)
+    test_apply(topi.cos, "cos", np.cos, -2.0*np.pi, 2.0*np.pi)
+    test_apply(topi.sin, "sin", np.sin, -2.0*np.pi, 2.0*np.pi)
 
 
 def test_cast():