From fedc0c6f7cd9a69d04aa1bf5374c0533be1f2782 Mon Sep 17 00:00:00 2001 From: Yao Wang Date: Mon, 9 Mar 2020 21:50:25 +0000 Subject: [PATCH] Revert "[topi][relay] add operation tan to TVM (#4938)" This reverts commit d992468d80af816f0413fc43c2ee1c02f7fe19c3. --- docs/frontend/tensorflow.rst | 1 - include/tvm/tir/op.h | 1 - python/tvm/relay/frontend/mxnet.py | 1 - python/tvm/relay/frontend/tensorflow.py | 1 - python/tvm/relay/frontend/tflite.py | 8 -------- python/tvm/relay/op/_tensor.py | 2 -- python/tvm/relay/op/_tensor_grad.py | 7 ------- python/tvm/relay/op/tensor.py | 15 --------------- python/tvm/te/__init__.py | 2 +- python/tvm/tir/__init__.py | 2 +- python/tvm/tir/op.py | 16 ---------------- src/relay/op/tensor/unary.cc | 11 ----------- src/target/intrin_rule.cc | 3 --- src/target/llvm/intrin_rule_llvm.cc | 14 -------------- src/target/llvm/intrin_rule_nvptx.cc | 3 --- src/target/llvm/intrin_rule_rocm.cc | 3 --- src/target/source/intrin_rule_cuda.cc | 19 ------------------- src/tir/ir/expr.cc | 2 +- .../frontend/tensorflow/test_forward.py | 10 ---------- tests/python/frontend/tflite/test_forward.py | 8 -------- tests/python/relay/test_op_grad_level1.py | 1 - tests/python/relay/test_op_level1.py | 1 - tests/python/unittest/test_testing.py | 1 - topi/include/topi/elemwise.h | 1 - topi/python/topi/math.py | 17 ----------------- topi/src/topi.cc | 5 ----- topi/tests/python/test_topi_basic.py | 1 - topi/tests/python/test_topi_math.py | 2 -- 28 files changed, 3 insertions(+), 155 deletions(-) diff --git a/docs/frontend/tensorflow.rst b/docs/frontend/tensorflow.rst index 8a54033a2b09..87341ab6b7c6 100644 --- a/docs/frontend/tensorflow.rst +++ b/docs/frontend/tensorflow.rst @@ -135,7 +135,6 @@ Supported Ops - ConcatV2 - Conv2D - Cos -- Tan - CropAndResize - DecodeJpeg - DepthwiseConv2dNative diff --git a/include/tvm/tir/op.h b/include/tvm/tir/op.h index 0a714d8fc8c8..5172b1496ad2 100644 --- a/include/tvm/tir/op.h +++ b/include/tvm/tir/op.h @@ -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); diff --git a/python/tvm/relay/frontend/mxnet.py b/python/tvm/relay/frontend/mxnet.py index c2bfd751facb..0020a63be625 100644 --- a/python/tvm/relay/frontend/mxnet.py +++ b/python/tvm/relay/frontend/mxnet.py @@ -1696,7 +1696,6 @@ def _get_bias_requantize_scale(_inputs, _data_scale, _kernel_scale): "ones_like", "where", "gather_nd", - "tan", "cos", "sin" ] diff --git a/python/tvm/relay/frontend/tensorflow.py b/python/tvm/relay/frontend/tensorflow.py index f1cb8151eacc..db785ec43405 100644 --- a/python/tvm/relay/frontend/tensorflow.py +++ b/python/tvm/relay/frontend/tensorflow.py @@ -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'), diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py index c2ec4d43bfac..bc51c9138d6b 100644 --- a/python/tvm/relay/frontend/tflite.py +++ b/python/tvm/relay/frontend/tflite.py @@ -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, @@ -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): diff --git a/python/tvm/relay/op/_tensor.py b/python/tvm/relay/op/_tensor.py index 4480849890b5..9f0906b113ba 100644 --- a/python/tvm/relay/op/_tensor.py +++ b/python/tvm/relay/op/_tensor.py @@ -27,7 +27,6 @@ register_broadcast_schedule("log") -register_broadcast_schedule("tan") register_broadcast_schedule("cos") register_broadcast_schedule("sin") register_broadcast_schedule("atan") @@ -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) diff --git a/python/tvm/relay/op/_tensor_grad.py b/python/tvm/relay/op/_tensor_grad.py index 33a193799288..944e51e636f5 100644 --- a/python/tvm/relay/op/_tensor_grad.py +++ b/python/tvm/relay/op/_tensor_grad.py @@ -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))]""" diff --git a/python/tvm/relay/op/tensor.py b/python/tvm/relay/op/tensor.py index 77969185c0a7..ada1f5e85cac 100644 --- a/python/tvm/relay/op/tensor.py +++ b/python/tvm/relay/op/tensor.py @@ -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. diff --git a/python/tvm/te/__init__.py b/python/tvm/te/__init__.py index 3a1a12462344..065cf4e5dbdd 100644 --- a/python/tvm/te/__init__.py +++ b/python/tvm/te/__init__.py @@ -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 diff --git a/python/tvm/tir/__init__.py b/python/tvm/tir/__init__.py index aa5871aa636a..a5c81ac77627 100644 --- a/python/tvm/tir/__init__.py +++ b/python/tvm/tir/__init__.py @@ -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 diff --git a/python/tvm/tir/op.py b/python/tvm/tir/op.py index c5b1a0a1cbb5..a8aef8f85495 100644 --- a/python/tvm/tir/op.py +++ b/python/tvm/tir/op.py @@ -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. diff --git a/src/relay/op/tensor/unary.cc b/src/relay/op/tensor/unary.cc index b9d247361996..1169fa801398 100644 --- a/src/relay/op/tensor/unary.cc +++ b/src/relay/op/tensor/unary.cc @@ -51,17 +51,6 @@ RELAY_REGISTER_UNARY_OP("log") .set_attr("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", RELAY_UNARY_COMPUTE(topi::tan)); - - RELAY_REGISTER_UNARY_OP("cos") .describe(R"code(Returns the cos of input array, computed element-wise. diff --git a/src/target/intrin_rule.cc b/src/target/intrin_rule.cc index ade3bbdf9304..7e9ac71bb753 100644 --- a/src/target/intrin_rule.cc +++ b/src/target/intrin_rule.cc @@ -40,9 +40,6 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.log") TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.tanh") .set_body(DispatchExtern); -TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.tan") -.set_body(DispatchExtern); - TVM_REGISTER_GLOBAL("tvm.intrin.rule.default.cos") .set_body(DispatchExtern); diff --git a/src/target/llvm/intrin_rule_llvm.cc b/src/target/llvm/intrin_rule_llvm.cc index 6c5a9cd079af..758b0afc22ff 100644 --- a/src/target/llvm/intrin_rule_llvm.cc +++ b/src/target/llvm/intrin_rule_llvm.cc @@ -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(); - 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>); diff --git a/src/target/llvm/intrin_rule_nvptx.cc b/src/target/llvm/intrin_rule_nvptx.cc index 6d41d132724b..6f7a89cebd97 100644 --- a/src/target/llvm/intrin_rule_nvptx.cc +++ b/src/target/llvm/intrin_rule_nvptx.cc @@ -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); diff --git a/src/target/llvm/intrin_rule_rocm.cc b/src/target/llvm/intrin_rule_rocm.cc index 4e6a661f298d..31b7bf19419b 100644 --- a/src/target/llvm/intrin_rule_rocm.cc +++ b/src/target/llvm/intrin_rule_rocm.cc @@ -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); diff --git a/src/target/source/intrin_rule_cuda.cc b/src/target/source/intrin_rule_cuda.cc index 849e098d4ad2..aed6c86e965b 100644 --- a/src/target/source/intrin_rule_cuda.cc +++ b/src/target/source/intrin_rule_cuda.cc @@ -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()) { @@ -113,9 +97,6 @@ TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.erf") TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.log") .set_body(DispatchExtern); -TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.tan") -.set_body(DispatchExtern); - TVM_REGISTER_GLOBAL("tvm.intrin.rule.cuda.cos") .set_body(DispatchExtern); diff --git a/src/tir/ir/expr.cc b/src/tir/ir/expr.cc index 7572f8d6bbb0..07cae5e2c746 100644 --- a/src/tir/ir/expr.cc +++ b/src/tir/ir/expr.cc @@ -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 }; diff --git a/tests/python/frontend/tensorflow/test_forward.py b/tests/python/frontend/tensorflow/test_forward.py index 3d033d368c19..fae39e6b0183 100644 --- a/tests/python/frontend/tensorflow/test_forward.py +++ b/tests/python/frontend/tensorflow/test_forward.py @@ -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) @@ -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() diff --git a/tests/python/frontend/tflite/test_forward.py b/tests/python/frontend/tflite/test_forward.py index 28216fcd674a..147839398d37 100644 --- a/tests/python/frontend/tflite/test_forward.py +++ b/tests/python/frontend/tflite/test_forward.py @@ -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 # ---- @@ -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 diff --git a/tests/python/relay/test_op_grad_level1.py b/tests/python/relay/test_op_grad_level1.py index 0579441166ae..0eb1cec916b6 100644 --- a/tests/python/relay/test_op_grad_level1.py +++ b/tests/python/relay/test_op_grad_level1.py @@ -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) diff --git a/tests/python/relay/test_op_level1.py b/tests/python/relay/test_op_level1.py index c58ff0f1aaba..0fa07499193a 100644 --- a/tests/python/relay/test_op_level1.py +++ b/tests/python/relay/test_op_level1.py @@ -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) diff --git a/tests/python/unittest/test_testing.py b/tests/python/unittest/test_testing.py index cfa13845e4e8..ecf520d251f1 100644 --- a/tests/python/unittest/test_testing.py +++ b/tests/python/unittest/test_testing.py @@ -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 diff --git a/topi/include/topi/elemwise.h b/topi/include/topi/elemwise.h index 26107ea78d27..3c0822f2b00e 100644 --- a/topi/include/topi/elemwise.h +++ b/topi/include/topi/elemwise.h @@ -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); diff --git a/topi/python/topi/math.py b/topi/python/topi/math.py index 3eda88ad8c11..4a63c4535289 100644 --- a/topi/python/topi/math.py +++ b/topi/python/topi/math.py @@ -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. diff --git a/topi/src/topi.cc b/topi/src/topi.cc index add01c20a443..75517b818f45 100644 --- a/topi/src/topi.cc +++ b/topi/src/topi.cc @@ -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]); diff --git a/topi/tests/python/test_topi_basic.py b/topi/tests/python/test_topi_basic.py index 13f1463da7ff..83f0469dc00f 100644 --- a/topi/tests/python/test_topi_basic.py +++ b/topi/tests/python/test_topi_basic.py @@ -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") diff --git a/topi/tests/python/test_topi_math.py b/topi/tests/python/test_topi_math.py index 3e58518ed4fe..30a0f44aad57 100644 --- a/topi/tests/python/test_topi_math.py +++ b/topi/tests/python/test_topi_math.py @@ -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)