def test_var_list(self):
def cond(i, mem):
return layers.less_than(i, ten)
def body(i, mem):
mem = layers.elementwise_add(x=mem, y=one)
i = layers.increment(i)
return [i, mem]
main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
i = layers.zeros(shape=[1], dtype='int64')
ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
mem = fluid.data(name='mem', shape=[10], dtype='float32')
one = layers.fill_constant(shape=[10], dtype='float32', value=1)
out = layers.while_loop(cond, body, [i, mem])
data = np.random.rand(10).astype('float32')
data_one = np.ones(10).astype('float32')
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
res = exe.run(main_program, feed={'mem': data}, fetch_list=out)
for i in range(10):
data = np.add(data, data_one)
self.assertTrue(np.allclose(np.asarray(res[1]), data))
def value_error_body_returns_with_mutable_list():
test_list = [
layers.fill_constant(shape=[2, 2], dtype='int64', value=1)
]
out = layers.while_loop(cond_returns_with_mutable_list,
body_returns_with_mutable_list,
[data, test_list])
def test_var_dict(self):
def cond(i, ten, test_dict, test_list, test_list_dict):
return layers.less_than(i, ten)
def body(i, ten, test_dict, test_list, test_list_dict):
test_dict["test_key"] = i
test_dict["test_key"] += 1
test_list[0] = fluid.layers.reshape(test_list[0], [2, -1]) + 1
test_list_dict[0]["test_key"] += 1
test_list_dict[0]["test_key"] = fluid.layers.relu(test_list_dict[0][
"test_key"])
i = layers.increment(i)
return [i, ten, test_dict, test_list, test_list_dict]
main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
i = layers.zeros(shape=[1], dtype='int64')
ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
test_data = layers.fill_constant(shape=[1], dtype='int64', value=0)
test_dict = {"test_key": test_data}
test_list = [
layers.fill_constant(
shape=[1, 2], dtype='int64', value=0)
]
test_list_dict = [{
"test_key": layers.fill_constant(
shape=[1], dtype='float32', value=0)
}]
i, ten, test_dict, test_list, test_list_dict = layers.while_loop(
cond, body, [i, ten, test_dict, test_list, test_list_dict])
place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
) else fluid.CPUPlace()
exe = fluid.Executor(place)
res = exe.run(main_program,
fetch_list=[
test_dict["test_key"], test_list[0],
test_list_dict[0]["test_key"]
])
self.assertTrue(
np.allclose(
np.asarray(res[0]),
np.full(
shape=(1), fill_value=10, dtype=np.int64)))
self.assertTrue(
np.allclose(
np.asarray(res[1]),
np.full(
shape=(2, 1), fill_value=10, dtype=np.int64)))
self.assertTrue(
np.allclose(
np.asarray(res[2]),
np.full(
shape=(1), fill_value=10, dtype=np.float32)))
def value_error_body_returns_with_mutable_dict():
test_dict = {
"int_constant":
layers.fill_constant(shape=[2, 2], dtype='int64', value=1)
}
out = layers.while_loop(cond_returns_with_mutable_dict,
body_returns_with_mutable_dict,
[data, test_dict])
def test_nested_net(self):
def external_cond(i, j, init, sums):
return layers.less_than(i, loop_len1)
def external_body(i, j, init, sums):
def internal_cond(j, init, sums):
return layers.less_than(j, loop_len2)
def internal_body(j, init, sums):
init = layers.elementwise_add(x=init, y=ones)
sums = layers.elementwise_add(x=init, y=sums)
j = layers.increment(j)
return [j, init, sums]
result = layers.while_loop(internal_cond, internal_body,
[j, init, sums])
j = result[0]
init = result[1]
sums = result[2]
sums = layers.elementwise_add(x=init, y=sums)
i = layers.increment(i)
return [i, j, init, sums]
main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
i = layers.zeros(shape=[1], dtype='int64')
j = layers.zeros(shape=[1], dtype='int64')
init = fluid.data(name='init', shape=[3, 3], dtype='float32')
sums = fluid.data(name='sums', shape=[3, 3], dtype='float32')
loop_len1 = layers.fill_constant(shape=[1], dtype='int64', value=2)
loop_len2 = layers.fill_constant(shape=[1], dtype='int64', value=3)
ones = layers.fill_constant(shape=[3, 3], dtype='float32', value=1)
out = layers.while_loop(external_cond, external_body,
[i, j, init, sums])
data = np.random.rand(3, 3).astype('float32')
data_sums = np.zeros([3, 3]).astype('float32')
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
res = exe.run(main_program,
feed={
'init': data,
'sums': data_sums
},
fetch_list=out)
for i in range(3):
data = np.add(data, 1)
data_sums = np.add(data, data_sums)
for j in range(2):
data_sums = np.add(data, data_sums)
self.assertTrue(np.allclose(np.asarray(res[3]), data_sums))
def test_while_loop_backward(self):
def cond(i, x):
return layers.less_than(i, eleven)
def body(j, x):
# TODO: In while block, if the var created in parent block
# participates in the calculation of gradient, the result of gradient
# is incorrect because each step scope always returns the same value
# generated by last step.
# Here we call `assign` op in while block to avoid this bug, and working on fixing it in next PR.
i = layers.assign(j)
x = layers.elementwise_mul(x=i, y=i)
j = layers.increment(j)
return [j, x]
main_program = Program()
startup_program = Program()
with fluid.program_guard(main_program, startup_program):
i = fluid.data(name='i', shape=[1], dtype='float32')
i.stop_gradient = False
eleven = layers.fill_constant(shape=[1], dtype='float32', value=11)
one = layers.fill_constant(shape=[1], dtype='float32', value=1)
x = fluid.data(name='x', shape=[1], dtype='float32')
x.stop_gradient = False
out = layers.while_loop(cond, body, [i, x])
mean = layers.mean(out[1])
append_backward(mean)
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
feed_i = np.ones(1).astype('float32')
feed_x = np.ones(1).astype('float32')
data = np.asarray([100]).astype('float32')
i_grad = np.asarray([110]).astype('float32')
res = exe.run(main_program,
feed={
'i': feed_i,
'x': feed_x
},
fetch_list=[mean.name, i.grad_name])
self.assertTrue(np.allclose(np.asarray(res[0]), data))
self.assertTrue(np.allclose(np.asarray(res[1]), i_grad),
msg=" \nres = \n{} \n\n ans = \n{}".format(
res[1], i_grad))
def external_body(i, j, init, sums):
def internal_cond(j, init, sums):
return layers.less_than(j, loop_len2)
def internal_body(j, init, sums):
init = layers.elementwise_add(x=init, y=ones)
sums = layers.elementwise_add(x=init, y=sums)
j = layers.increment(j)
return [j, init, sums]
result = layers.while_loop(internal_cond, internal_body,
[j, init, sums])
j = result[0]
init = result[1]
sums = result[2]
sums = layers.elementwise_add(x=init, y=sums)
i = layers.increment(i)
return [i, j, init, sums]
def test_while_loop_backward2(self):
def cond(i, x):
return i < 3
def body(i, x):
x = x * i
i = i + 1
return [i, x]
main_program = Program()
startup_program = Program()
with fluid.program_guard(main_program, startup_program):
i = fluid.data(name='i', shape=[1], dtype='float32')
i.stop_gradient = False
x = fluid.data(name='x', shape=[1], dtype='float32')
x.stop_gradient = False
out = layers.while_loop(cond, body, [i, x])
mean = layers.mean(out[1])
append_backward(mean)
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
feed_i = np.ones(1).astype('float32')
feed_x = np.ones(1).astype('float32')
data = np.asarray([2]).astype('float32')
i_grad = np.asarray([3]).astype('float32')
x_grad = np.asarray([2]).astype('float32')
res = exe.run(main_program,
feed={
'i': feed_i,
'x': feed_x
},
fetch_list=[mean.name, i.grad_name, x.grad_name])
self.assertTrue(np.allclose(np.asarray(res[0]), data))
self.assertTrue(np.allclose(np.asarray(res[1]), i_grad),
msg=" \nres = \n{} \n\n ans = \n{}".format(
res[1], i_grad))
self.assertTrue(np.allclose(np.asarray(res[2]), x_grad),
msg=" \nres = \n{} \n\n ans = \n{}".format(
res[2], x_grad))
def test_while_loop_backward(self):
def cond(i, x):
return layers.less_than(i, eleven)
def body(i, x):
x = layers.elementwise_mul(x=i, y=i)
i = layers.increment(i)
return [i, x]
main_program = Program()
startup_program = Program()
with fluid.program_guard(main_program, startup_program):
i = fluid.data(name='i', shape=[1], dtype='float32')
i.stop_gradient = False
eleven = layers.fill_constant(shape=[1], dtype='float32', value=11)
one = layers.fill_constant(shape=[1], dtype='float32', value=1)
x = fluid.data(name='x', shape=[1], dtype='float32')
x.stop_gradient = False
out = layers.while_loop(cond, body, [i, x])
mean = layers.mean(out[1])
append_backward(mean)
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
feed_i = np.ones(1).astype('float32')
feed_x = np.ones(1).astype('float32')
data = np.asarray([100]).astype('float32')
i_grad = np.asarray([110]).astype('float32')
res = exe.run(main_program,
feed={
'i': feed_i,
'x': feed_x
},
fetch_list=[mean.name, i.grad_name])
self.assertTrue(np.allclose(np.asarray(res[0]), data))
self.assertTrue(np.allclose(np.asarray(res[1]), i_grad),
msg=" \nres = \n{} \n\n ans = \n{}".format(
res[1], i_grad))
def test_var_tuple(self):
def cond(i):
return layers.less_than(i, ten)
def body(i):
return layers.elementwise_add(x=i, y=one)
main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
i = layers.fill_constant(shape=[1], dtype='int64', value=0)
one = layers.fill_constant(shape=[1], dtype='int64', value=1)
ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
out = layers.while_loop(cond, body, (i, ))
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
res = exe.run(main_program, fetch_list=out)
self.assertTrue(
np.allclose(np.asarray(res[0]), np.full((1), 10, np.int64)))
def test_with_switch_case(self):
def cond(i):
return layers.less_than(i, ten)
def body(i):
def fn_add_three():
data_add_three = layers.elementwise_add(x=i, y=three)
return data_add_three
def fn_square():
data_mul_data = layers.elementwise_mul(x=i, y=i)
return data_mul_data
def fn_add_one():
data_add_one = layers.elementwise_add(x=i, y=one)
return data_add_one
return layers.switch_case(branch_index=i,
branch_fns={
2: fn_add_three,
5: fn_square
},
default=fn_add_one)
main_program = Program()
startup_program = Program()
with fluid.program_guard(main_program, startup_program):
i = layers.fill_constant(shape=[1], dtype='int64', value=1)
ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
three = layers.fill_constant(shape=[1], dtype='int64', value=3)
one = layers.fill_constant(shape=[1], dtype='int64', value=1)
out = layers.while_loop(cond, body, [i])
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
res = exe.run(main_program, fetch_list=out)
data = np.asarray([25]).astype('int64')
self.assertTrue(np.allclose(np.asarray(res[0]), data))
def external_body(i, j, x, mem_array):
def internal_cond(j, x, mem_array):
return layers.less_than(j, array_len2)
def internal_body(j, x, mem_array):
inner_data = layers.array_read(array=data_array, i=j)
inner_prev = layers.array_read(array=mem_array, i=j)
inner_sum_0 = layers.elementwise_add(x=inner_data, y=inner_prev)
inner_sum_1 = layers.elementwise_add(x=x, y=inner_sum_0)
j = layers.increment(x=j, in_place=True)
layers.array_write(inner_sum_1, i=j, array=mem_array)
return [j, x, mem_array]
outer_data = layers.array_read(array=data_array, i=i)
outer_prev = layers.array_read(array=mem_array, i=i)
outer_sum_0 = layers.elementwise_add(x=outer_data, y=outer_prev)
outer_sum_1 = layers.elementwise_add(x=x, y=outer_sum_0)
i = layers.increment(x=i, in_place=True)
layers.array_write(outer_sum_1, i=i, array=mem_array)
j, x, mem_array = layers.while_loop(internal_cond, internal_body,
[j, x, mem_array])
return [i, j, x, mem_array]
def test_ignore_var(self):
def cond(i, ten, temp, y):
return i < ten
def body_func(i, ten, batch_info, origin_seq):
print(batch_info)
batch_info = fluid.contrib.layers.shuffle_batch(batch_info)
print(batch_info)
i = i + 1
return [i, ten, batch_info, origin_seq]
x = fluid.layers.data(name='x', shape=[-1, 1, 4])
y = fluid.layers.data(name='y', shape=[-1, 1, 1])
temp = layers.concat(input=[x, y], axis=-1)
i = layers.fill_constant(shape=[1], value=0, dtype='int32')
num = layers.fill_constant(shape=[1], value=5, dtype='int32')
i, ten, shuffle_temp, y = layers.while_loop(cond, body_func,
[i, num, temp, y])
output = shuffle_temp
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
input_x = numpy.array([[1, 2, 3, 4], [4, 5, 6, 7], [7, 8, 9, 10]])
input_x = input_x.reshape(3, 1, 4)
input_y = numpy.array([[10], [12], [33]])
input_y = input_y.reshape(3, 1, 1)
res, = exe.run(fluid.default_main_program(),
feed={
'x': input_x,
'y': input_y
},
fetch_list=[output])
self.assertListEqual(list(res.shape), [3, 1, 5])
def type_error_cond():
out = layers.while_loop(data, body, [data_1d])
def value_error_body_returns_error_length():
out = layers.while_loop(cond_returns_bool_tensor,
body_returns_error_length, [data])
def value_error_body_returns_error_type():
out = layers.while_loop(cond_receives_two_args,
body_returns_error_type, [data, ten])
def type_error_cond_returns_not_boolean():
out = layers.while_loop(cond_returns_not_bool_tensor, body,
[data_1d])
def type_error_shape_cond_returns_2d():
out = layers.while_loop(cond_returns_2d_tensor, body,
[data_2d])
def type_error_cond_returns_not_variable():
out = layers.while_loop(cond_returns_constant, body, [data_1d])
def value_error_loop_vars():
out = layers.while_loop(cond_returns_bool_tensor, body, [])
def type_error_loop_vars():
out = layers.while_loop(cond_returns_bool_tensor, body,
data_1d)
def beam_search():
max_len = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=max_out_len,
force_cpu=True)
step_idx = layers.fill_constant(shape=[1],
dtype=start_tokens.dtype,
value=0,
force_cpu=True)
# array states will be stored for each step.
ids = layers.array_write(layers.reshape(start_tokens, (-1, 1)),
step_idx)
scores = layers.array_write(init_scores, step_idx)
# cell states will be overwrited at each step.
# caches contains states of history steps in decoder self-attention
# and static encoder output projections in encoder-decoder attention
# to reduce redundant computation.
caches = [
{
"k": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_key],
dtype=enc_output.dtype,
value=0),
"v": # for self attention
layers.fill_constant_batch_size_like(
input=start_tokens,
shape=[-1, n_head, 0, d_value],
dtype=enc_output.dtype,
value=0),
"static_k": # for encoder-decoder attention
fluid.data(
shape=[None, n_head, 0, d_key],
dtype=enc_output.dtype,
name=("static_k_%d" % i)),
"static_v": # for encoder-decoder attention
fluid.data(
shape=[None, n_head, 0, d_value],
dtype=enc_output.dtype,
name=("static_v_%d" % i)),
} for i in range(n_layer)
]
def cond_func(step_idx, selected_ids, selected_scores, gather_idx,
caches, trg_src_attn_bias):
length_cond = layers.less_than(x=step_idx, y=max_len)
finish_cond = layers.logical_not(layers.is_empty(x=selected_ids))
return layers.logical_and(x=length_cond, y=finish_cond)
def body_func(step_idx, pre_ids, pre_scores, gather_idx, caches,
trg_src_attn_bias):
# gather cell states corresponding to selected parent
pre_caches = map_structure(
lambda x: layers.gather(x, index=gather_idx), caches)
pre_src_attn_bias = layers.gather(trg_src_attn_bias,
index=gather_idx)
pre_pos = layers.elementwise_mul(
x=layers.fill_constant_batch_size_like(
input=pre_src_attn_bias, # cann't use lod tensor here
value=1,
shape=[-1, 1],
dtype=pre_ids.dtype),
y=step_idx,
axis=0)
logits = wrap_decoder((pre_ids, pre_pos, None, pre_src_attn_bias),
trg_vocab_size,
max_in_len,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
preprocess_cmd,
postprocess_cmd,
weight_sharing,
enc_output=enc_output,
caches=pre_caches,
bos_idx=bos_idx)
# intra-beam topK
topk_scores, topk_indices = layers.topk(
input=layers.softmax(logits), k=beam_size)
accu_scores = layers.elementwise_add(x=layers.log(topk_scores),
y=pre_scores,
axis=0)
# beam_search op uses lod to differentiate branches.
accu_scores = layers.lod_reset(accu_scores, pre_ids)
# topK reduction across beams, also contain special handle of
# end beams and end sentences(batch reduction)
selected_ids, selected_scores, gather_idx = layers.beam_search(
pre_ids=pre_ids,
pre_scores=pre_scores,
ids=topk_indices,
scores=accu_scores,
beam_size=beam_size,
end_id=eos_idx,
return_parent_idx=True)
step_idx = layers.increment(x=step_idx, value=1.0, in_place=False)
layers.array_write(selected_ids, i=step_idx, array=ids)
layers.array_write(selected_scores, i=step_idx, array=scores)
return (step_idx, selected_ids, selected_scores, gather_idx,
pre_caches, pre_src_attn_bias)
_ = layers.while_loop(cond=cond_func,
body=body_func,
loop_vars=[
step_idx, start_tokens, init_scores,
parent_idx, caches, trg_src_attn_bias
],
is_test=True)
finished_ids, finished_scores = layers.beam_search_decode(
ids, scores, beam_size=beam_size, end_id=eos_idx)
return finished_ids, finished_scores
def test_nested_net_with_backward_and_lodtensor(self):
def external_cond(i, j, x, mem_array):
return layers.less_than(i, array_len)
def external_body(i, j, x, mem_array):
def internal_cond(j, x, mem_array):
return layers.less_than(j, array_len2)
def internal_body(j, x, mem_array):
inner_data = layers.array_read(array=data_array, i=j)
inner_prev = layers.array_read(array=mem_array, i=j)
inner_sum_0 = layers.elementwise_add(x=inner_data,
y=inner_prev)
inner_sum_1 = layers.elementwise_add(x=x, y=inner_sum_0)
j = layers.increment(x=j, in_place=True)
layers.array_write(inner_sum_1, i=j, array=mem_array)
return [j, x, mem_array]
outer_data = layers.array_read(array=data_array, i=i)
outer_prev = layers.array_read(array=mem_array, i=i)
outer_sum_0 = layers.elementwise_add(x=outer_data, y=outer_prev)
outer_sum_1 = layers.elementwise_add(x=x, y=outer_sum_0)
i = layers.increment(x=i, in_place=True)
layers.array_write(outer_sum_1, i=i, array=mem_array)
j, x, mem_array = layers.while_loop(internal_cond, internal_body,
[j, x, mem_array])
return [i, j, x, mem_array]
main_program = Program()
startup_program = Program()
with fluid.program_guard(main_program, startup_program):
d0 = fluid.data(name='d0', shape=[10], dtype='float32')
d1 = fluid.data(name='d1', shape=[10], dtype='float32')
d2 = fluid.data(name='d2', shape=[10], dtype='float32')
x = fluid.data(name='x', shape=[10], dtype='float32')
x.stop_gradient = False
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
init = layers.zeros(shape=[10], dtype='float32')
mem_array = layers.array_write(x=init, i=i)
data_array = layers.array_write(x=d0, i=i)
i = layers.increment(i)
layers.array_write(d1, i, array=data_array)
i = layers.increment(i)
layers.array_write(d2, i, array=data_array)
i = layers.zeros(shape=[1], dtype='int64')
i.stop_gradient = True
array_len = layers.fill_constant(shape=[1], dtype='int64', value=1)
j = layers.fill_constant(shape=[1], dtype='int64', value=1)
j.stop_gradient = True
array_len2 = layers.fill_constant(shape=[1],
dtype='int64',
value=3)
out = layers.while_loop(external_cond, external_body,
[i, j, x, mem_array])
sum_result = layers.array_read(array=mem_array, i=j)
mean = layers.mean(sum_result)
append_backward(mean)
place = fluid.CUDAPlace(
0) if core.is_compiled_with_cuda() else fluid.CPUPlace()
exe = fluid.Executor(place)
d = []
for i in range(3):
d.append(np.random.random(size=[10]).astype('float32'))
feed_x = np.ones(10).astype('float32')
data_sum = d[0] + d[1] + d[2] + 3 * feed_x
x_grad = [0.3] * 10
res = exe.run(main_program,
feed={
'd0': d[0],
'd1': d[1],
'd2': d[2],
'x': feed_x
},
fetch_list=[sum_result.name, x.grad_name])
self.assertTrue(np.allclose(res[0], data_sum))
self.assertTrue(np.allclose(res[1], x_grad))
def type_error_body():
out = layers.while_loop(cond_returns_bool_tensor, data,
[data_1d])
import paddle.fluid as fluid
import paddle.fluid.layers as layers
def cond(i, ten):
return i < ten
def body(i, dummy):
i = i + 1
return i, dummy
i = layers.fill_constant(shape=[1], dtype='int64', value=0)
ten = layers.fill_constant(shape=[1], dtype='int64', value=10)
out, ten = layers.while_loop(cond=cond, body=body, loop_vars=[i, ten])
exe = fluid.Executor(fluid.CPUPlace())
res = exe.run(fluid.default_main_program(), feed={}, fetch_list=out)
print(res)
