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Revert "[topi][relay] add operation tan to TVM (#4938)" (#5017)
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This reverts commit d992468.
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kevinthesun authored Mar 10, 2020
1 parent a9cfe41 commit c0bc188
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Showing 28 changed files with 3 additions and 155 deletions.
1 change: 0 additions & 1 deletion docs/frontend/tensorflow.rst
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Expand Up @@ -135,7 +135,6 @@ Supported Ops
- ConcatV2
- Conv2D
- Cos
- Tan
- CropAndResize
- DecodeJpeg
- DepthwiseConv2dNative
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1 change: 0 additions & 1 deletion include/tvm/tir/op.h
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Expand Up @@ -515,7 +515,6 @@ TVM_DECLARE_INTRIN_UNARY(sqrt);
TVM_DECLARE_INTRIN_UNARY(rsqrt);
TVM_DECLARE_INTRIN_UNARY(log);
TVM_DECLARE_INTRIN_UNARY(popcount);
TVM_DECLARE_INTRIN_UNARY(tan);
TVM_DECLARE_INTRIN_UNARY(cos);
TVM_DECLARE_INTRIN_UNARY(sin);
TVM_DECLARE_INTRIN_UNARY(atan);
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1 change: 0 additions & 1 deletion python/tvm/relay/frontend/mxnet.py
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Expand Up @@ -1696,7 +1696,6 @@ def _get_bias_requantize_scale(_inputs, _data_scale, _kernel_scale):
"ones_like",
"where",
"gather_nd",
"tan",
"cos",
"sin"
]
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1 change: 0 additions & 1 deletion python/tvm/relay/frontend/tensorflow.py
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Expand Up @@ -1572,7 +1572,6 @@ def _impl(inputs, attr, params):
'LessEqual' : _broadcast('less_equal'),
'Log' : AttrCvt('log'),
'Log1p' : _log1p(),
'Tan' : AttrCvt('tan'),
'Cos' : AttrCvt('cos'),
'Sin' : AttrCvt('sin'),
'LogicalAnd' : _logical('logical_and'),
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8 changes: 0 additions & 8 deletions python/tvm/relay/frontend/tflite.py
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Expand Up @@ -68,7 +68,6 @@ def __init__(self, model, subgraph, exp_tab):
'LOG': self.convert_log,
'SIN': self.convert_sin,
'COS': self.convert_cos,
'TAN': self.convert_tan,
'SQRT': self.convert_sqrt,
'RSQRT': self.convert_rsqrt,
'NEG': self.convert_neg,
Expand Down Expand Up @@ -658,13 +657,6 @@ def convert_sin(self, op):
'TFlite quantized SIN operator is not supported yet.')
return self._convert_unary_elemwise(_op.sin, op)

def convert_tan(self, op):
"""Convert TFLite TAN"""
if self.is_quantized(op):
raise tvm.error.OpNotImplemented(
'TFlite quantized TAN operator is not supported yet.')
return self._convert_unary_elemwise(_op.tan, op)

def convert_cos(self, op):
"""Convert TFLite COS"""
if self.is_quantized(op):
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2 changes: 0 additions & 2 deletions python/tvm/relay/op/_tensor.py
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Expand Up @@ -27,7 +27,6 @@


register_broadcast_schedule("log")
register_broadcast_schedule("tan")
register_broadcast_schedule("cos")
register_broadcast_schedule("sin")
register_broadcast_schedule("atan")
Expand Down Expand Up @@ -215,4 +214,3 @@ def elemwise_shape_func(attrs, inputs, _):
register_shape_func("sqrt", False, elemwise_shape_func)
register_shape_func("negative", False, elemwise_shape_func)
register_shape_func("exp", False, elemwise_shape_func)
register_shape_func("tan", False, elemwise_shape_func)
7 changes: 0 additions & 7 deletions python/tvm/relay/op/_tensor_grad.py
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Expand Up @@ -61,13 +61,6 @@ def log_grad(orig, grad):
return [grad * ones_like(x) / x]


@register_gradient("tan")
def tan_grad(orig, grad):
"""Returns [grad / (cos^2(x))]"""
x = orig.args[0]
return [grad / (cos(x) * cos(x))]


@register_gradient("cos")
def cos_grad(orig, grad):
"""Returns [grad * (-sin(x))]"""
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15 changes: 0 additions & 15 deletions python/tvm/relay/op/tensor.py
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Expand Up @@ -47,21 +47,6 @@ def log(data):
"""
return _make.log(data)

def tan(data):
"""Compute elementwise tan of data.
Parameters
----------
data : relay.Expr
The input data
Returns
-------
result : relay.Expr
The computed result.
"""
return _make.tan(data)

def cos(data):
"""Compute elementwise cos of data.
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2 changes: 1 addition & 1 deletion python/tvm/te/__init__.py
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Expand Up @@ -19,7 +19,7 @@
"""
# expose all operators in tvm tir.op
from tvm.tir import any, all, min_value, max_value, trace
from tvm.tir import exp, erf, tanh, sigmoid, log, tan, cos, sin, atan, sqrt, rsqrt, floor, ceil
from tvm.tir import exp, erf, tanh, sigmoid, log, cos, sin, atan, sqrt, rsqrt, floor, ceil
from tvm.tir import trunc, abs, round, nearbyint, isnan, power, popcount, fmod, if_then_else
from tvm.tir import div, indexdiv, indexmod, truncdiv, truncmod, floordiv, floormod
from tvm.tir import comm_reducer, min, max, sum
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2 changes: 1 addition & 1 deletion python/tvm/tir/__init__.py
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Expand Up @@ -33,7 +33,7 @@

from .op import call_packed, call_pure_intrin, call_intrin, call_pure_extern, call_extern
from .op import call_llvm_intrin, all, any, min_value, max_value, trace
from .op import exp, erf, tanh, sigmoid, log, tan, cos, sin, atan, sqrt, rsqrt, floor, ceil
from .op import exp, erf, tanh, sigmoid, log, cos, sin, atan, sqrt, rsqrt, floor, ceil
from .op import trunc, abs, round, nearbyint, isnan, power, popcount, fmod, if_then_else
from .op import div, indexdiv, indexmod, truncdiv, truncmod, floordiv, floormod
from .op import comm_reducer, min, max, sum
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16 changes: 0 additions & 16 deletions python/tvm/tir/op.py
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Expand Up @@ -393,22 +393,6 @@ def log(x):
"""
return call_pure_intrin(x.dtype, "log", x)

def tan(x):
"""Take tan of input x.
Parameters
----------
x : PrimExpr
Input argument.
Returns
-------
y : PrimExpr
The result.
"""
return call_pure_intrin(x.dtype, "tan", x)


def cos(x):
"""Take cos of input x.
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11 changes: 0 additions & 11 deletions src/relay/op/tensor/unary.cc
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Expand Up @@ -51,17 +51,6 @@ RELAY_REGISTER_UNARY_OP("log")
.set_attr<FTVMCompute>("FTVMCompute", RELAY_UNARY_COMPUTE(topi::log));


RELAY_REGISTER_UNARY_OP("tan")
.describe(R"code(Returns the tan of input array, computed element-wise.
.. math::
Y = tan(X)
)code" TVM_ADD_FILELINE)
.set_support_level(1)
.set_attr<FTVMCompute>("FTVMCompute", RELAY_UNARY_COMPUTE(topi::tan));


RELAY_REGISTER_UNARY_OP("cos")
.describe(R"code(Returns the cos of input array, computed element-wise.
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3 changes: 0 additions & 3 deletions src/target/intrin_rule.cc
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Expand Up @@ -40,9 +40,6 @@ 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.tan")
.set_body(DispatchExtern<FloatSuffix>);

TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.cos")
.set_body(DispatchExtern<FloatSuffix>);

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14 changes: 0 additions & 14 deletions src/target/llvm/intrin_rule_llvm.cc
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Expand Up @@ -91,20 +91,6 @@ 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.tan")
.set_body([](const TVMArgs& targs, TVMRetValue* rv) {
PrimExpr e = targs[0];
const tir::CallNode* call = e.as<tir::CallNode>();
CHECK(call != nullptr);
const PrimExpr& x = call->args[0];
PrimExpr sin_x = tir::CallNode::make(
x.dtype(), "sin", {x}, tir::CallNode::PureIntrinsic);
PrimExpr cos_x = tir::CallNode::make(
x.dtype(), "cos", {x}, tir::CallNode::PureIntrinsic);
PrimExpr tan_x = sin_x / cos_x;
*rv = tan_x;
});

TVM_REGISTER_GLOBAL("tvm.intrin.rule.llvm.cos")
.set_body(DispatchLLVMPureIntrin<::llvm::Intrinsic::cos, 1>);

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3 changes: 0 additions & 3 deletions src/target/llvm/intrin_rule_nvptx.cc
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Expand Up @@ -81,9 +81,6 @@ 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.tan")
.set_body(DispatchExternLibDevice);

TVM_REGISTER_GLOBAL("tvm.intrin.rule.nvptx.cos")
.set_body(DispatchExternLibDevice);

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3 changes: 0 additions & 3 deletions src/target/llvm/intrin_rule_rocm.cc
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Expand Up @@ -80,9 +80,6 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.rocm.pow")
TVM_REGISTER_GLOBAL("tvm.intrin.rule.rocm.tanh")
.set_body(DispatchExternOCML);

TVM_REGISTER_GLOBAL("tvm.intrin.rule.rocm.tan")
.set_body(DispatchExternOCML);

TVM_REGISTER_GLOBAL("tvm.intrin.rule.rocm.cos")
.set_body(DispatchExternOCML);

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19 changes: 0 additions & 19 deletions src/target/source/intrin_rule_cuda.cc
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Expand Up @@ -54,22 +54,6 @@ struct CUDAFastMath : public CUDAMath {
}
};

struct CUDAFastMathTan : public CUDAMath {
std::string operator()(DataType t, std::string name) const {
if (t.lanes() == 1 && t.is_float()) {
switch (t.bits()) {
case 64: return name;
// `__tanf` seems to produce some values too deviant from numpy tan version.
// So, let's use just `tanf` instead.
case 32: return name + 'f';
case 16: LOG(FATAL) << "cuda tan unsupported for float16";
default: return "";
}
}
return "";
}
};

struct CUDAPopcount {
std::string operator()(DataType t, std::string name) const {
if (t.lanes() == 1 && t.is_uint()) {
Expand Down Expand Up @@ -113,9 +97,6 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.erf")
TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.log")
.set_body(DispatchExtern<CUDAFastMath>);

TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.tan")
.set_body(DispatchExtern<CUDAFastMathTan>);

TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.cos")
.set_body(DispatchExtern<CUDAFastMath>);

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2 changes: 1 addition & 1 deletion src/tir/ir/expr.cc
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Expand Up @@ -229,7 +229,7 @@ PrimExpr LetNode::make(Var var, PrimExpr value, PrimExpr body) {

const char* CallNode::vectorizable_intrinsics[] = {
"floor", "ceil", "sign", "trunc", "fabs", "round", "exp", "tanh", "sqrt",
"log", "sin", "cos", "pow", "tan", tir::CallNode::shift_left, tir::CallNode::shift_right,
"log", "sin", "cos", "pow", tir::CallNode::shift_left, tir::CallNode::shift_right,
tir::CallNode::likely, tir::CallNode::popcount
};

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10 changes: 0 additions & 10 deletions tests/python/frontend/tensorflow/test_forward.py
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Expand Up @@ -2624,15 +2624,6 @@ def test_forward_cos():
compare_tf_with_tvm([np_data], ['in_data:0'], 'cos:0')


def test_forward_tan():
"""test operator tan """
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.tan(in_data, name="tan")
compare_tf_with_tvm([np_data], ['in_data:0'], 'tan:0')


def test_forward_sin():
"""test operator sin """
np_data = np.random.uniform(1, 100, size=(2, 3, 5)).astype(np.float32)
Expand Down Expand Up @@ -3036,7 +3027,6 @@ def test_forward_add_n():
test_forward_sign()
test_forward_log()
test_forward_log1p()
test_forward_tan()
test_forward_cos()
test_forward_sin()
test_forward_negative()
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8 changes: 0 additions & 8 deletions tests/python/frontend/tflite/test_forward.py
Original file line number Diff line number Diff line change
Expand Up @@ -723,13 +723,6 @@ def _test_cos(data):
""" One iteration of cos """
return _test_unary_elemwise(math_ops.cos, data)
#######################################################################
# Tan
# ---

def _test_tan(data):
""" One iteration of tan """
return _test_unary_elemwise(math_ops.tan, data)
#######################################################################
# Sqrt
# ----

Expand Down Expand Up @@ -779,7 +772,6 @@ def test_all_unary_elemwise():
if package_version.parse(tf.VERSION) >= package_version.parse('1.14.0'):
_test_forward_unary_elemwise(_test_ceil)
_test_forward_unary_elemwise(_test_cos)
_test_forward_unary_elemwise(_test_tan)

#######################################################################
# Element-wise
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1 change: 0 additions & 1 deletion tests/python/relay/test_op_grad_level1.py
Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,6 @@ def check_single_op(opfunc, ref):
(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)),
(tvm.relay.tan, lambda x: 1.0 / (np.cos(x) ** 2)),
(tvm.relay.atan, lambda x: 1 / (1 + np.power(x, 2.0)))]:
check_single_op(opfunc, ref)

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1 change: 0 additions & 1 deletion tests/python/relay/test_op_level1.py
Original file line number Diff line number Diff line change
Expand Up @@ -76,7 +76,6 @@ def check_single_op(opfunc, ref, dtype):
(relay.nn.relu, relu),
(tvm.relay.cos, np.cos),
(tvm.relay.sin, np.sin),
(tvm.relay.tan, np.tan),
(tvm.relay.atan, np.arctan)]:
for dtype in ['float16', 'float32']:
check_single_op(opfunc, ref, dtype)
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1 change: 0 additions & 1 deletion tests/python/unittest/test_testing.py
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,6 @@ def test_check_numerical_grads():
lambda x: (np.sign(np.sin(1/x)), np.zeros_like(x)),
lambda x: (x*np.sin(1/x), np.sin(1/x) - np.cos(1/x)/x),
lambda x: (np.sin(1/x), - np.cos(1/x)/(x*x)),
lambda x: (np.tan(x), 1.0 / (np.cos(x) * np.cos(x))),
]

# Avoid values too close to 0 since singularities of our functions are there
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1 change: 0 additions & 1 deletion topi/include/topi/elemwise.h
Original file line number Diff line number Diff line change
Expand Up @@ -55,7 +55,6 @@ TOPI_DECLARE_UNARY_OP(round);
TOPI_DECLARE_UNARY_OP(trunc);
TOPI_DECLARE_UNARY_OP(abs);
TOPI_DECLARE_UNARY_OP(cos);
TOPI_DECLARE_UNARY_OP(tan);
TOPI_DECLARE_UNARY_OP(sin);
TOPI_DECLARE_UNARY_OP(atan);
TOPI_DECLARE_UNARY_OP(isnan);
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17 changes: 0 additions & 17 deletions topi/python/topi/math.py
Original file line number Diff line number Diff line change
Expand Up @@ -109,23 +109,6 @@ def tanh(x):
return te.compute(x.shape, lambda *i: te.tanh(x(*i)))


@tvm.te.tag_scope(tag=tag.ELEMWISE)
def tan(x):
"""Take tan of input x.
Parameters
----------
x : tvm.te.Tensor
Input argument.
Returns
-------
y : tvm.te.Tensor
The result.
"""
return te.compute(x.shape, lambda *i: te.tan(x(*i)))


@tvm.te.tag_scope(tag=tag.ELEMWISE)
def cos(x):
"""Take cos of input x.
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5 changes: 0 additions & 5 deletions topi/src/topi.cc
Original file line number Diff line number Diff line change
Expand Up @@ -175,11 +175,6 @@ TVM_REGISTER_GLOBAL("topi.erf")
*rv = erf(args[0]);
});

TVM_REGISTER_GLOBAL("topi.tan")
.set_body([](TVMArgs args, TVMRetValue *rv) {
*rv = tan(args[0]);
});

TVM_REGISTER_GLOBAL("topi.cos")
.set_body([](TVMArgs args, TVMRetValue *rv) {
*rv = cos(args[0]);
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1 change: 0 additions & 1 deletion topi/tests/python/test_topi_basic.py
Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,6 @@ def test_apply(func, name):
test_apply(topi.rsqrt, "rsqrt")
test_apply(topi.sin, "sin")
test_apply(topi.cos, "cos")
test_apply(topi.tan, "tan")
test_apply(topi.atan, "atan")


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2 changes: 0 additions & 2 deletions topi/tests/python/test_topi_math.py
Original file line number Diff line number Diff line change
Expand Up @@ -127,8 +127,6 @@ def check_device(device):
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.tan, "tan", np.tan, -2.0*np.pi, 2.0*np.pi, dtype='float32')
test_apply(topi.tan, "tan", np.tan, -2.0*np.pi, 2.0*np.pi, dtype='float64')
test_apply(topi.sin, "sin", np.sin, -2.0*np.pi, 2.0*np.pi)
test_apply(topi.erf, "erf", scipy.special.erf, -.1, .1, dtype="float32")
test_isnan(-100, 100)
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