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add test #70280

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154 changes: 81 additions & 73 deletions test/ir/pir/cinn/test_cinn_sub_graph.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,6 @@

import unittest

import numpy as np
import utils

import paddle
Expand Down Expand Up @@ -58,8 +57,13 @@ def __init__(self):
self.fn = exp_sub

def forward(self, x):
out = self.fn(x)
return out
# out = self.fn(x)
# return out
t1 = x.shape[1]

out = t1 == 1

return t1, out


class CINNSoftmaxSubGraphNet(paddle.nn.Layer):
Expand Down Expand Up @@ -152,7 +156,7 @@ def setUp(self):
self.prepare_data()

def prepare_data(self):
self.shape = [128, 128, 768]
self.shape = [2, 2]
self.axis = -1
self.x = paddle.uniform(self.shape, dtype="float64", min=-0.5, max=0.5)
self.x.stop_gradient = False
Expand All @@ -166,101 +170,105 @@ class TestCinnExpSubNet(TestCinnSubGraphBase):
def eval(self, use_cinn):
paddle.seed(2022)
net = CINNSubGraphNet()
net = utils.apply_to_static(net, use_cinn)

input_spec = [
paddle.static.InputSpec(shape=[2, -1], dtype='float32', name='in_x')
]
net = utils.apply_to_static(net, use_cinn, input_spec=input_spec)
net.eval()
out = net(self.x)
if use_cinn:
self.check_jit_kernel_info(net.forward)
# if use_cinn:
# self.check_jit_kernel_info(net.forward)
return out

def test_eval(self):
cinn_out = self.eval(use_cinn=True)
dy_out = self.eval(use_cinn=False)
np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)

# dy_out = self.eval(use_cinn=False)
# np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)

class TestCinnSoftmax(TestCinnSubGraphBase):
def train(self, use_cinn):
paddle.seed(2022)
net = CINNSoftmaxSubGraphNet()
net = utils.apply_to_static(net, use_cinn)
out = net(self.x, self.axis)

loss = out.sum()
loss.backward()
return out, self.x.gradient()
# class TestCinnSoftmax(TestCinnSubGraphBase):
# def train(self, use_cinn):
# paddle.seed(2022)
# net = CINNSoftmaxSubGraphNet()
# net = utils.apply_to_static(net, use_cinn)
# out = net(self.x, self.axis)

def test_forward(self):
cinn_out, cinn_grad = self.train(use_cinn=True)
dy_out, dy_grad = self.train(use_cinn=False)
np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)
np.testing.assert_allclose(cinn_grad, dy_grad, atol=1e-8)
# loss = out.sum()
# loss.backward()
# return out, self.x.gradient()

# def test_forward(self):
# cinn_out, cinn_grad = self.train(use_cinn=True)
# dy_out, dy_grad = self.train(use_cinn=False)
# np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)
# np.testing.assert_allclose(cinn_grad, dy_grad, atol=1e-8)

class TestCinnSlice(TestCinnSubGraphBase):
def train(self, use_cinn):
paddle.seed(2022)
net = CINNSliceSubGraphNet()

input_spec = [
paddle.static.InputSpec(
shape=[-1, -1], dtype='float32', name='in_x'
),
paddle.static.InputSpec(shape=[1], dtype='int64', name='d1'),
paddle.static.InputSpec(shape=[1], dtype='int64', name='d2'),
paddle.static.InputSpec(shape=[1], dtype='int64', name='d3'),
paddle.static.InputSpec(shape=[1], dtype='int64', name='42'),
]
# class TestCinnSlice(TestCinnSubGraphBase):
# def train(self, use_cinn):
# paddle.seed(2022)
# net = CINNSliceSubGraphNet()

# input_spec = [
# paddle.static.InputSpec(
# shape=[-1, -1], dtype='float32', name='in_x'
# ),
# paddle.static.InputSpec(shape=[1], dtype='int64', name='d1'),
# paddle.static.InputSpec(shape=[1], dtype='int64', name='d2'),
# paddle.static.InputSpec(shape=[1], dtype='int64', name='d3'),
# paddle.static.InputSpec(shape=[1], dtype='int64', name='42'),
# ]

self.x = paddle.uniform([16, 256], dtype="float64", min=-0.5, max=0.5)
self.d1 = paddle.full([1], fill_value=4, dtype="int64")
self.d2 = paddle.full([1], fill_value=16, dtype="int64")
self.d3 = paddle.full([1], fill_value=4, dtype="int64")
self.d4 = paddle.full([1], fill_value=4, dtype="int64")
# self.x = paddle.uniform([16, 256], dtype="float64", min=-0.5, max=0.5)
# self.d1 = paddle.full([1], fill_value=4, dtype="int64")
# self.d2 = paddle.full([1], fill_value=16, dtype="int64")
# self.d3 = paddle.full([1], fill_value=4, dtype="int64")
# self.d4 = paddle.full([1], fill_value=4, dtype="int64")

net = utils.apply_to_static(net, use_cinn, input_spec=input_spec)
out = net(self.x, self.d1, self.d2, self.d3, self.d4)
# net = utils.apply_to_static(net, use_cinn, input_spec=input_spec)
# out = net(self.x, self.d1, self.d2, self.d3, self.d4)

return out
# return out

def test_forward(self):
cinn_out = self.train(use_cinn=True)
dy_out = self.train(use_cinn=False)
np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)
# def test_forward(self):
# cinn_out = self.train(use_cinn=True)
# dy_out = self.train(use_cinn=False)
# np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)


class TestCinnSmallSoftmax(TestCinnSoftmax):
def prepare_data(self):
self.shape = [1, 1, 17, 17]
self.axis = -1
self.x = paddle.uniform(self.shape, dtype="float64", min=-0.5, max=0.5)
self.x.stop_gradient = False
# class TestCinnSmallSoftmax(TestCinnSoftmax):
# def prepare_data(self):
# self.shape = [1, 1, 17, 17]
# self.axis = -1
# self.x = paddle.uniform(self.shape, dtype="float64", min=-0.5, max=0.5)
# self.x.stop_gradient = False


class TestReduceAs(TestCinnSubGraphBase):
def train(self, use_cinn):
paddle.seed(2022)
net = CINNAddSubGraphNet()
# class TestReduceAs(TestCinnSubGraphBase):
# def train(self, use_cinn):
# paddle.seed(2022)
# net = CINNAddSubGraphNet()

input_spec = [
paddle.static.InputSpec(shape=[-1, -1], dtype='float32', name='x')
]
# input_spec = [
# paddle.static.InputSpec(shape=[-1, -1], dtype='float32', name='x')
# ]

self.x = paddle.uniform([16, 256], dtype="float64", min=-0.5, max=0.5)
self.x.stop_gradient = False
# self.x = paddle.uniform([16, 256], dtype="float64", min=-0.5, max=0.5)
# self.x.stop_gradient = False

net = utils.apply_to_static(net, use_cinn, input_spec=input_spec)
out = net(self.x)
# net = utils.apply_to_static(net, use_cinn, input_spec=input_spec)
# out = net(self.x)

loss = out.sum()
loss.backward()
# loss = out.sum()
# loss.backward()

return out
# return out

def test_forward(self):
cinn_out = self.train(use_cinn=True)
dy_out = self.train(use_cinn=False)
np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)
# def test_forward(self):
# cinn_out = self.train(use_cinn=True)
# dy_out = self.train(use_cinn=False)
# np.testing.assert_allclose(cinn_out.numpy(), dy_out.numpy(), atol=1e-8)


# class TestCinnLayerNorm(TestCinnSubGraphBase):
Expand Down