# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import numpy as np
import topi
import topi.testing
import tvm
from tvm import relay
from tvm.relay.testing import check_grad, ctx_list, run_infer_type
from tvm.relay.transform import gradient
def verify_max_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode):
x = relay.var("x", relay.TensorType(x_shape, "float32"))
y = tvm.relay.nn.max_pool2d(x, pool_size=pool_size, strides=strides, padding=padding,
ceil_mode=ceil_mode)
fwd_func = relay.Function([x], y)
fwd_func = run_infer_type(fwd_func)
bwd_func = run_infer_type(gradient(fwd_func))
data = np.random.rand(*x_shape).astype("float32")
ph, pw = padding
y_shape = topi.util.get_const_tuple(fwd_func.ret_type.shape)
out_grad = np.ones(shape=y_shape)
ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=pool_size, strides=strides,
padding=[ph, pw, ph, pw],
pool_type='max', ceil_mode=ceil_mode)
for target, ctx in ctx_list():
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
def test_max_pool2d_grad():
verify_max_pool2d_grad((1, 4, 16, 16), pool_size=(2, 2), strides=(2, 2), padding=(0, 0), ceil_mode=False)
verify_max_pool2d_grad((1, 4, 16, 16), pool_size=(1, 1), strides=(1, 1), padding=(1, 1), ceil_mode=False)
def verify_avg_pool2d_grad(x_shape, pool_size, strides, padding, ceil_mode, count_include_pad):
x = relay.var("x", relay.TensorType(x_shape, "float32"))
y = tvm.relay.nn.avg_pool2d(x, pool_size=pool_size, strides=strides, padding=padding,
ceil_mode=ceil_mode, count_include_pad=count_include_pad)
fwd_func = relay.Function([x], y)
fwd_func = run_infer_type(fwd_func)
bwd_func = run_infer_type(gradient(fwd_func))
data = np.random.rand(*x_shape).astype("float32")
ph, pw = padding
y_shape = topi.util.get_const_tuple(fwd_func.ret_type.shape)
out_grad = np.ones(shape=y_shape)
ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=pool_size, strides=strides,
padding=[ph, pw, ph, pw],
pool_type='avg', ceil_mode=ceil_mode)
for target, ctx in ctx_list():
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
def test_avg_pool2d_grad():
verify_avg_pool2d_grad((1, 4, 16, 16), pool_size=(2, 2), strides=(2, 2), padding=(0, 0),
ceil_mode=False, count_include_pad=True)
verify_avg_pool2d_grad((1, 4, 16, 16), pool_size=(1, 1), strides=(1, 1), padding=(1, 1),
ceil_mode=False, count_include_pad=False)
def verify_global_avg_pool2d_grad(x_shape):
x = relay.var("x", relay.TensorType(x_shape, "float32"))
y = tvm.relay.nn.global_avg_pool2d(x)
fwd_func = relay.Function([x], y)
fwd_func = run_infer_type(fwd_func)
bwd_func = run_infer_type(gradient(fwd_func))
data = np.random.rand(*x_shape).astype("float32")
y_shape = topi.util.get_const_tuple(fwd_func.ret_type.shape)
out_grad = np.ones(shape=y_shape)
ref_grad = topi.testing.pool_grad_nchw(data, out_grad, pool_size=(x_shape[2], x_shape[3]),
strides=(1, 1), padding=[0, 0, 0, 0], pool_type='avg',
ceil_mode=False)
for target, ctx in ctx_list():
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (op_grad, ) = intrp.evaluate(bwd_func)(data)
np.testing.assert_allclose(op_grad.asnumpy(), ref_grad, rtol=0.01)
def test_global_avg_pool2d_grad():
verify_global_avg_pool2d_grad((1, 4, 16, 16))
verify_global_avg_pool2d_grad((1, 8, 8, 24))
def verify_conv2d_grad(dshape, wshape, strides, padding, dilation, groups=1, mode='higher_order'):
try:
import torch
import torch.nn.functional as F
except ImportError:
print('Skip because pytorch is not installed')
return
dtype = 'float32'
data = relay.var('data', shape=dshape, dtype=dtype)
weight = relay.var('weight', shape=wshape, dtype=dtype)
conv = relay.nn.conv2d(data, weight, strides=strides, padding=padding, dilation=dilation,
groups=groups)
fwd_func = relay.Function([data, weight], conv)
fwd_func = run_infer_type(fwd_func)
bwd_func = run_infer_type(gradient(fwd_func, mode=mode))
data_pt = torch.randn(*dshape, dtype=torch.float32, requires_grad=True)
weight_pt = torch.randn(*wshape, dtype=torch.float32, requires_grad=True)
out_pt = F.conv2d(data_pt, weight_pt, stride=strides, padding=padding, dilation=dilation,
groups=groups)
grad_output_pt = torch.ones(out_pt.shape)
grad_input_pt = F.grad.conv2d_input(dshape, weight_pt, grad_output_pt, stride=strides,
padding=padding, dilation=dilation, groups=groups) \
.detach().numpy()
grad_weight_pt = F.grad.conv2d_weight(data_pt, wshape, grad_output_pt, stride=strides,
padding=padding, dilation=dilation, groups=groups) \
.detach().numpy()
for target, ctx in ctx_list():
data = tvm.nd.array(data_pt.detach().numpy(), ctx)
weight = tvm.nd.array(weight_pt.detach().numpy(), ctx)
intrp = relay.create_executor(ctx=ctx, target=target)
op_res, (grad_input, grad_weight) = intrp.evaluate(bwd_func)(data, weight)
np.testing.assert_allclose(grad_input.asnumpy(), grad_input_pt, rtol=1e-4, atol=1e-4)
np.testing.assert_allclose(grad_weight.asnumpy(), grad_weight_pt, rtol=1e-4, atol=1e-4)
def test_conv2d_grad():
verify_conv2d_grad((1, 4, 16, 16), (16, 4, 3, 3), [1, 1], [1, 1], [1, 1])
verify_conv2d_grad((1, 4, 16, 16), (16, 4, 1, 1), [1, 1], [0, 0], [1, 1])
verify_conv2d_grad((1, 4, 16, 16), (16, 4, 1, 1), [2, 2], [0, 0], [1, 1])
verify_conv2d_grad((1, 4, 16, 16), (16, 4, 3, 3), [1, 1], [1, 1], [1, 1], mode='first_order')
def verify_dense_grad(d_shape, w_shape):
data = relay.var("data", relay.TensorType(d_shape, "float32"))
weight = relay.var("weight", relay.TensorType(w_shape, "float32"))
fwd_func = relay.Function([data, weight], relay.nn.dense(data, weight))
check_grad(fwd_func)
def test_dense_grad():
verify_dense_grad((1, 8), (16, 8))
verify_dense_grad((1, 4), (3, 4))
def verify_batch_flatten_grad(d_shape):
data = relay.var("data", relay.TensorType(d_shape, "float32"))
fwd_func = relay.Function([data], relay.nn.batch_flatten(data))
check_grad(fwd_func)
def test_batch_flatten_grad():
verify_batch_flatten_grad((1, 2, 3, 4))
verify_batch_flatten_grad((1, 8))
if __name__ == "__main__":
test_max_pool2d_grad()
test_avg_pool2d_grad()
test_global_avg_pool2d_grad()
test_conv2d_grad()
test_dense_grad()
test_batch_flatten_grad()