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Add Op UnitTest for floor_divide (PaddlePaddle#1387)
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* fix

* add broadcast case
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@enkilee @jiahy0825
enkilee authored and jiahy0825 committed May 25, 2023
1 parent ccd8267 commit 8d5dd00
Showing 1 changed file with 240 additions and 24 deletions.
264 changes: 240 additions & 24 deletions python/tests/ops/test_floor_divide_op.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@
import unittest
import numpy as np
from op_test import OpTest, OpTestTool
from op_test_helper import TestCaseHelper
import paddle
import paddle.nn.functional as F
import cinn
Expand All @@ -28,17 +29,24 @@
"x86 test will be skipped due to timeout.")
class TestFloorDivideOp(OpTest):
def setUp(self):
print(f"\nRunning {self.__class__.__name__}: {self.case}")
self.init_case()

def init_case(self):
self.inputs = {
"x": np.array([7]).astype('int32'),
"y": np.array([-3]).astype('int32')
}
self.x_np = self.random(
shape=self.case["x_shape"],
dtype=self.case["x_dtype"],
low=-10,
high=10)
self.y_np = self.random(
shape=self.case["y_shape"],
dtype=self.case["y_dtype"],
low=1,
high=10)

def build_paddle_program(self, target):
x = paddle.to_tensor(self.inputs["x"], stop_gradient=False)
y = paddle.to_tensor(self.inputs["y"], stop_gradient=False)
x = paddle.to_tensor(self.x_np, stop_gradient=True)
y = paddle.to_tensor(self.y_np, stop_gradient=True)

out = paddle.floor_divide(x, y)

Expand All @@ -47,38 +55,246 @@ def build_paddle_program(self, target):
def build_cinn_program(self, target):
builder = NetBuilder("pow")
x = builder.create_input(
self.nptype2cinntype(self.inputs["x"].dtype),
self.inputs["x"].shape, "x")
self.nptype2cinntype(self.case["x_dtype"]), self.case["x_shape"],
"x")
y = builder.create_input(
self.nptype2cinntype(self.inputs["y"].dtype),
self.inputs["y"].shape, "y")
self.nptype2cinntype(self.case["y_dtype"]), self.case["y_shape"],
"y")
out = builder.floor_divide(x, y)

prog = builder.build()
res = self.get_cinn_output(prog, target, [x, y],
[self.inputs["x"], self.inputs["y"]], [out])
[self.x_np, self.y_np], [out])

self.cinn_outputs = [res[0]]

def test_check_results(self):
self.check_outputs_and_grads()
max_relative_error = self.case[
"max_relative_error"] if "max_relative_error" in self.case else 1e-5
self.check_outputs_and_grads(max_relative_error=max_relative_error)


class TestFloorDivideCase1(TestFloorDivideOp):
def init_case(self):
self.inputs = {
"x": self.random([32, 64], "int32", 20, 100),
"y": self.random([32, 64], "int32", 1, 20),
}
class TestFloorDivideAll(TestCaseHelper):
def init_attrs(self):
self.class_name = "TestFloorDivideOpCase"
self.cls = TestFloorDivideOp
self.inputs = [
{
"x_shape": [1],
"y_shape": [1],
},
{
"x_shape": [1024],
"y_shape": [1024],
},
{
"x_shape": [512, 256],
"y_shape": [512, 256],
},
{
"x_shape": [128, 64, 32],
"y_shape": [128, 64, 32],
},
{
"x_shape": [16, 8, 4, 2],
"y_shape": [16, 8, 4, 2],
},
{
"x_shape": [16, 8, 4, 2, 1],
"y_shape": [16, 8, 4, 2, 1],
},
]
self.dtypes = [
{
"x_dtype": "int32",
"y_dtype": "int32",
},
{
"x_dtype": "int64",
"y_dtype": "int64",
},
]
self.attrs = []


class TestFloorDivideAllWithBroadcast(TestCaseHelper):
def init_attrs(self):
self.class_name = "TestFloorDivideOpCase"
self.cls = TestFloorDivideOp
self.inputs = [
{
"x_shape": [1],
"y_shape": [1],
},
{
"x_shape": [1024],
"y_shape": [1],
},
{
"x_shape": [512, 256],
"y_shape": [1, 1],
},
{
"x_shape": [128, 64, 32],
"y_shape": [1, 1, 1],
},
{
"x_shape": [16, 8, 4, 2],
"y_shape": [1, 1, 1, 1],
},
{
"x_shape": [16, 8, 4, 2, 1],
"y_shape": [1, 1, 1, 1, 1],
},
]
self.dtypes = [
{
"x_dtype": "int32",
"y_dtype": "int32",
},
{
"x_dtype": "int64",
"y_dtype": "int64",
},
]
self.attrs = []


class TestFloorDivideNegOp(OpTest):
def setUp(self):
print(f"\nRunning {self.__class__.__name__}: {self.case}")
self.init_case()

class TestFloorDivideCase2(TestFloorDivideOp):
def init_case(self):
self.inputs = {
"x": self.random([32, 64], "int32", 20, 100),
"y": self.random([32, 64], "int32", -20, -1),
}
self.x_np = self.random(
shape=self.case["x_shape"],
dtype=self.case["x_dtype"],
low=-10,
high=10)
self.y_np = self.random(
shape=self.case["y_shape"],
dtype=self.case["y_dtype"],
low=-10,
high=-1)

def build_paddle_program(self, target):
x = paddle.to_tensor(self.x_np, stop_gradient=True)
y = paddle.to_tensor(self.y_np, stop_gradient=True)

out = paddle.floor_divide(x, y)

self.paddle_outputs = [out]

def build_cinn_program(self, target):
builder = NetBuilder("pow")
x = builder.create_input(
self.nptype2cinntype(self.case["x_dtype"]), self.case["x_shape"],
"x")
y = builder.create_input(
self.nptype2cinntype(self.case["y_dtype"]), self.case["y_shape"],
"y")
out = builder.floor_divide(x, y)

prog = builder.build()
res = self.get_cinn_output(prog, target, [x, y],
[self.x_np, self.y_np], [out])

self.cinn_outputs = [res[0]]

def test_check_results(self):
max_relative_error = self.case[
"max_relative_error"] if "max_relative_error" in self.case else 1e-5
self.check_outputs_and_grads(max_relative_error=max_relative_error)


class TestFloorDivideNegAll(TestCaseHelper):
def init_attrs(self):
self.class_name = "TestFloorDivideNegOpCase"
self.cls = TestFloorDivideNegOp
self.inputs = [
{
"x_shape": [1],
"y_shape": [1],
},
{
"x_shape": [1024],
"y_shape": [1024],
},
{
"x_shape": [512, 256],
"y_shape": [512, 256],
},
{
"x_shape": [128, 64, 32],
"y_shape": [128, 64, 32],
},
{
"x_shape": [16, 8, 4, 2],
"y_shape": [16, 8, 4, 2],
},
{
"x_shape": [16, 8, 4, 2, 1],
"y_shape": [16, 8, 4, 2, 1],
},
]
self.dtypes = [
{
"x_dtype": "int32",
"y_dtype": "int32",
},
{
"x_dtype": "int64",
"y_dtype": "int64",
},
]
self.attrs = []


class TestFloorDivideNegAllWithBroadcast(TestCaseHelper):
def init_attrs(self):
self.class_name = "TestFloorDivideNegOpCase"
self.cls = TestFloorDivideNegOp
self.inputs = [
{
"x_shape": [1],
"y_shape": [1],
},
{
"x_shape": [1024],
"y_shape": [1],
},
{
"x_shape": [512, 256],
"y_shape": [1, 1],
},
{
"x_shape": [128, 64, 32],
"y_shape": [1, 1, 1],
},
{
"x_shape": [16, 8, 4, 2],
"y_shape": [1, 1, 1, 1],
},
{
"x_shape": [16, 8, 4, 2, 1],
"y_shape": [1, 1, 1, 1, 1],
},
]
self.dtypes = [
{
"x_dtype": "int32",
"y_dtype": "int32",
},
{
"x_dtype": "int64",
"y_dtype": "int64",
},
]
self.attrs = []


if __name__ == "__main__":
unittest.main()
TestFloorDivideAll().run()
TestFloorDivideNegAll().run()
TestFloorDivideAllWithBroadcast().run()
TestFloorDivideNegAllWithBroadcast().run()

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