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[static-runtime] update generator for the modified tests; re-run autogen script (#84437)
Test Plan: CI Reviewed By: mikeiovine Differential Revision: D39183148 Pull Request resolved: https://github.com/pytorch/pytorch/pull/84437 Approved by: https://github.com/mikeiovine
This commit is contained in:
Max Podkorytov
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
6363b1b358
commit
7f90606309
@ -1,4 +1,5 @@
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// @lint-ignore-every CLANGTIDY HOWTOEVEN
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// AUTO-GENERATED FROM: torchgen/static_runtime/gen_static_runtime_ops.py
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#include <gtest/gtest.h>
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#include <torch/csrc/jit/runtime/static/impl.h>
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#include <torch/torch.h>
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@ -863,6 +864,38 @@ TEST(StaticRuntime, autogen_clamp_max) {
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_clamp_max_Tensor) {
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const std::string script = R"IR(
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graph(%self: Tensor, %max: Tensor):
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%bias: None = prim::Constant()
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%ret = aten::clamp_max(%self, %max)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto self0 = at::rand({6, 6, 6});
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auto max0 = at::rand({6, 6, 6});
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std::vector<IValue> args{self0, max0};
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testStaticRuntime(
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script,
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args,
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{},
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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auto self1 = at::rand({22, 22, 22});
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auto max1 = at::rand({22, 22, 22});
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std::vector<IValue> args2{self1, max1};
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testStaticRuntime(
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script,
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args,
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args2,
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_clip) {
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const std::string script = R"IR(
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graph(%self: Tensor, %min: int?, %max: int?):
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@ -1531,36 +1564,6 @@ TEST(StaticRuntime, autogen_index_copy) {
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_inverse) {
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const std::string script = R"IR(
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graph(%self: Tensor):
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%bias: None = prim::Constant()
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%ret = aten::inverse(%self)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto self0 = at::rand({6, 6, 6});
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std::vector<IValue> args{self0};
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testStaticRuntime(
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script,
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args,
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{},
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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auto self1 = at::rand({22, 22, 22});
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std::vector<IValue> args2{self1};
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testStaticRuntime(
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script,
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args,
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args2,
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_isin_Tensor_Tensor) {
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const std::string script = R"IR(
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graph(%elements: Tensor, %test_elements: Tensor, %assume_unique: bool, %invert: bool):
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@ -2899,6 +2902,38 @@ TEST(StaticRuntime, autogen_square) {
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_prod) {
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const std::string script = R"IR(
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graph(%self: Tensor, %dtype: int?):
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%bias: None = prim::Constant()
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%ret = aten::prod(%self, %dtype)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto self0 = at::rand({6, 6, 6});
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auto dtype0 = at::ScalarType::Float;
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std::vector<IValue> args{self0, dtype0};
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testStaticRuntime(
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script,
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args,
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{},
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/false);
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auto self1 = at::rand({22, 22, 22});
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auto dtype1 = at::ScalarType::Float;
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std::vector<IValue> args2{self1, dtype1};
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testStaticRuntime(
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script,
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args,
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args2,
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/false);
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}
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TEST(StaticRuntime, autogen_prod_dim_int) {
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const std::string script = R"IR(
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graph(%self: Tensor, %dim: int, %keepdim: bool, %dtype: int?):
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@ -5163,38 +5198,6 @@ TEST(StaticRuntime, autogen_maximum) {
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_max_other) {
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const std::string script = R"IR(
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graph(%self: Tensor, %other: Tensor):
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%bias: None = prim::Constant()
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%ret = aten::max(%self, %other)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto self0 = at::rand({6, 6, 6});
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auto other0 = at::rand({6, 6, 6});
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std::vector<IValue> args{self0, other0};
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testStaticRuntime(
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script,
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args,
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{},
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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auto self1 = at::rand({22, 22, 22});
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auto other1 = at::rand({22, 22, 22});
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std::vector<IValue> args2{self1, other1};
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testStaticRuntime(
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script,
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args,
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args2,
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_minimum) {
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const std::string script = R"IR(
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graph(%self: Tensor, %other: Tensor):
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@ -5507,40 +5510,6 @@ TEST(StaticRuntime, autogen_mse_loss) {
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_l1_loss) {
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const std::string script = R"IR(
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graph(%self: Tensor, %target: Tensor, %reduction: int):
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%bias: None = prim::Constant()
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%ret = aten::l1_loss(%self, %target, %reduction)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto self0 = at::rand({6, 6, 6});
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auto target0 = at::rand({6, 6, 6});
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auto reduction0 = 1;
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std::vector<IValue> args{self0, target0, reduction0};
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testStaticRuntime(
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script,
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args,
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{},
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/false);
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auto self1 = at::rand({22, 22, 22});
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auto target1 = at::rand({22, 22, 22});
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auto reduction1 = 1;
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std::vector<IValue> args2{self1, target1, reduction1};
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testStaticRuntime(
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script,
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args,
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args2,
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/false);
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}
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TEST(StaticRuntime, autogen_multi_margin_loss) {
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const std::string script = R"IR(
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graph(%self: Tensor, %target: Tensor, %p: int, %margin: int, %weight: Tensor?, %reduction: int):
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@ -6230,8 +6199,8 @@ TEST(StaticRuntime, autogen_adaptive_max_pool2d_backward) {
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return (%cloned)
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)IR";
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auto grad_output0 = at::randint(-3, 2, {2, 2, 2}, at::kFloat);
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auto self0 = at::randint(-3, 2, {2, 2, 2}, at::kFloat);
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auto grad_output0 = at::rand({2, 2, 2}, at::kFloat);
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auto self0 = at::rand({2, 2, 2}, at::kFloat);
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auto indices0 = at::randint(0, 1, {2, 2, 2}, at::kLong);
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std::vector<IValue> args{grad_output0, self0, indices0};
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testStaticRuntime(
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@ -6242,8 +6211,8 @@ TEST(StaticRuntime, autogen_adaptive_max_pool2d_backward) {
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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auto grad_output1 = at::randint(-3, 3, {3, 3, 3}, at::kFloat);
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auto self1 = at::randint(-3, 2, {3, 3, 3}, at::kFloat);
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auto grad_output1 = at::rand({3, 3, 3}, at::kFloat);
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auto self1 = at::rand({3, 3, 3}, at::kFloat);
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auto indices1 = at::randint(0, 1, {3, 3, 3}, at::kLong);
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std::vector<IValue> args2{grad_output1, self1, indices1};
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testStaticRuntime(
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@ -6264,8 +6233,8 @@ TEST(StaticRuntime, autogen_adaptive_max_pool3d_backward) {
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return (%cloned)
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)IR";
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auto grad_output0 = at::randint(-3, 2, {2, 2, 2, 2}, at::kFloat);
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auto self0 = at::randint(-3, 2, {2, 2, 2, 2}, at::kFloat);
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auto grad_output0 = at::rand({2, 2, 2, 2}, at::kFloat);
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auto self0 = at::rand({2, 2, 2, 2}, at::kFloat);
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auto indices0 = at::randint(0, 1, {2, 2, 2, 2}, at::kLong);
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std::vector<IValue> args{grad_output0, self0, indices0};
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testStaticRuntime(
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@ -6276,8 +6245,8 @@ TEST(StaticRuntime, autogen_adaptive_max_pool3d_backward) {
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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auto grad_output1 = at::randint(-3, 3, {3, 3, 3, 3}, at::kFloat);
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auto self1 = at::randint(-3, 2, {3, 3, 3, 3}, at::kFloat);
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auto grad_output1 = at::rand({3, 3, 3, 3}, at::kFloat);
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auto self1 = at::rand({3, 3, 3, 3}, at::kFloat);
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auto indices1 = at::randint(0, 1, {3, 3, 3, 3}, at::kLong);
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std::vector<IValue> args2{grad_output1, self1, indices1};
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testStaticRuntime(
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@ -7521,15 +7490,15 @@ TEST(StaticRuntime, autogen_linalg_cross) {
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TEST(StaticRuntime, autogen_linalg_det) {
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const std::string script = R"IR(
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graph(%self: Tensor):
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graph(%A: Tensor):
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%bias: None = prim::Constant()
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%ret = aten::linalg_det(%self)
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%ret = aten::linalg_det(%A)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto self0 = at::rand({6, 6, 6});
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std::vector<IValue> args{self0};
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auto A0 = at::rand({6, 6, 6});
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std::vector<IValue> args{A0};
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testStaticRuntime(
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script,
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args,
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@ -7538,8 +7507,8 @@ TEST(StaticRuntime, autogen_linalg_det) {
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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auto self1 = at::rand({22, 22, 22});
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std::vector<IValue> args2{self1};
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auto A1 = at::rand({22, 22, 22});
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std::vector<IValue> args2{A1};
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testStaticRuntime(
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script,
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args,
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@ -7612,10 +7581,40 @@ TEST(StaticRuntime, autogen_linalg_eigvals) {
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}
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TEST(StaticRuntime, autogen_linalg_inv) {
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const std::string script = R"IR(
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graph(%A: Tensor):
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%bias: None = prim::Constant()
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%ret = aten::linalg_inv(%A)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto A0 = at::rand({6, 6, 6});
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std::vector<IValue> args{A0};
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testStaticRuntime(
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script,
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args,
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{},
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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auto A1 = at::rand({22, 22, 22});
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std::vector<IValue> args2{A1};
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testStaticRuntime(
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script,
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args,
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args2,
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/*use_allclose=*/false,
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/*use_equalnan=*/false,
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/*check_resize=*/true);
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}
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TEST(StaticRuntime, autogen_inverse) {
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const std::string script = R"IR(
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graph(%self: Tensor):
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%bias: None = prim::Constant()
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%ret = aten::linalg_inv(%self)
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%ret = aten::inverse(%self)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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@ -7705,18 +7704,18 @@ TEST(StaticRuntime, autogen_outer) {
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/*check_resize=*/true);
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}
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// Disabling the test because JIT alias analysis does not support linalg_svdvals at this point.
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TEST(StaticRuntime, DISABLED_autogen_linalg_svdvals) {
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TEST(StaticRuntime, autogen_linalg_svdvals) {
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const std::string script = R"IR(
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graph(%A: Tensor):
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graph(%A: Tensor, %driver: str?):
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%bias: None = prim::Constant()
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%ret = aten::linalg_svdvals(%A)
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%ret = aten::linalg_svdvals(%A, %driver)
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%cloned = aten::clone(%ret, %bias)
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return (%cloned)
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)IR";
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auto A0 = at::rand({6, 6, 6});
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std::vector<IValue> args{A0};
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auto driver0 = "floor";
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std::vector<IValue> args{A0, driver0};
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testStaticRuntime(
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script,
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args,
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@ -7726,7 +7725,8 @@ TEST(StaticRuntime, DISABLED_autogen_linalg_svdvals) {
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/*check_resize=*/true);
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auto A1 = at::rand({22, 22, 22});
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std::vector<IValue> args2{A1};
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auto driver1 = "floor";
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std::vector<IValue> args2{A1, driver1};
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testStaticRuntime(
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script,
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args,
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