提交 50639faf 编写于 作者: M minqiyang

Polish code

test=develop
上级 fe406b98
develop 2.0.1-rocm-post Ligoml-patch-1 OliverLPH-patch-1 OliverLPH-patch-2 PaddlePM-patch-1 PaddlePM-patch-2 ZHUI-patch-1 add_default_att add_model_benchmark_ci add_some_yaml_config addfile all_new_design_exec ascendrc ascendrelease cherry_undefined_var compile_windows delete_2.0.1-rocm-post delete_add_default_att delete_all_new_design_exec delete_ascendrc delete_compile_windows delete_delete_addfile delete_disable_iterable_dataset_unittest delete_fix_dataloader_memory_leak delete_fix_imperative_dygraph_error delete_fix_retry_ci delete_fix_undefined_var delete_improve_sccache delete_incubate/lite delete_paddle_tiny_install delete_paralleltest delete_prv-disable-more-cache delete_revert-31068-fix_conv3d_windows delete_revert-31562-mean delete_revert-33630-bug-fix delete_revert-34159-add_npu_bce_logical_dev delete_revert-34910-spinlocks_for_allocator delete_revert-35069-revert-34910-spinlocks_for_allocator delete_revert-36057-dev/read_flags_in_ut dingjiaweiww-patch-1 disable_iterable_dataset_unittest dy2static enable_eager_model_test final_state_gen_python_c final_state_intermediate fix-numpy-issue fix_concat_slice fix_dataloader_memory_leak fix_imperative_dygraph_error fix_npu_ci fix_op_flops fix_retry_ci fix_rnn_docs fix_tensor_type fix_undefined_var fixiscan fixiscan1 fixiscan2 fixiscan3 github/fork/123malin/netifaces github/fork/123malin/tdm_abacus github/fork/AshburnLee/dev_unique github/fork/ForFishes/fix_memory_matmul github/fork/ForFishes/rm_fluid github/fork/LielinJiang/move-2.0-api github/fork/LielinJiang/visual-dl-cb github/fork/LiuChiachi/add-transformer-generate-square-subsequent-mask-api github/fork/LiuChiachi/fix-example-code-for-hapi-Model github/fork/LiuChiachi/remove-input-requirment-in-dygraph-Model github/fork/MrChengmo/fix_ps_profiler github/fork/MrChengmo/update_ps_heter github/fork/PWhiddy/patch-1 github/fork/Shixiaowei02/dev/save_load_upgrade github/fork/TCChenlong/fix_hapi github/fork/TCChenlong/fix_inden github/fork/Thunderbrook/xpu_slice github/fork/XieYunshen/disable_ut_test_parallel_executor_fetch_isolated_var github/fork/XieYunshen/disable_ut_test_parallel_executor_fetch_isolated_var_2 github/fork/XieYunshen/disable_ut_test_parallel_executor_fetch_isolated_var_3 github/fork/XieYunshen/timeout_20S_ut github/fork/ZeyuChen/remove-nltk github/fork/arlesniak/arlesniak/selective__mkldnn_flags github/fork/baiyfbupt/code_doc_mig github/fork/chalsliu/set_timeout github/fork/chen-zhiyu/develop github/fork/chenwhql/ci/try_to_find_test_buffer_shared_memory_reuse_pass_error github/fork/chenwhql/dygraph/remove_scale_loss_and_apply_collective_grads github/fork/chenwhql/saveload/add_get_inference_program github/fork/chenwhql/saveload/remove_save_load_config github/fork/cryoco/pass-compatibility-trt github/fork/danleifeng/isempty_api2.0 github/fork/frankwhzhang/api_transfer github/fork/hbwx24/error_msg/cuda_kernel_error_msg github/fork/heavengate/cherry_yolo_box github/fork/heavengate/update_yolo_box github/fork/iclementine/rnn_fix github/fork/iducn/testestse github/fork/jczaja/prv-25537-fix github/fork/jeff41404/release/1.8 github/fork/jiweibo/api_2.0 github/fork/jiweibo/fix_lite_resnet50_test github/fork/juncaipeng/fix_doc_1 github/fork/lfchener/sample_code github/fork/littletomatodonkey/fix_reg_doc github/fork/liym27/dy2stat_update_assign_to_rc20 github/fork/luotao1/profiler_ut github/fork/mapingshuo/add_wait github/fork/mapingshuo/doc_2.0 github/fork/mapingshuo/zero-0.5 github/fork/miraiwk/dev github/fork/pangyoki/add-Categorical-class-branch github/fork/pangyoki/add-multinomial-op-branch github/fork/pangyoki/fix-test_distritbution-CI github/fork/qjing666/doublegrad github/fork/qjing666/fix_hdfs_download github/fork/sandyhouse/add_gather_etc github/fork/sandyhouse/add_send_recv_alltoall_etc github/fork/sandyhouse/pipeline_exe_run github/fork/seiriosPlus/feature/large_scale_kv_save_delta github/fork/seiriosPlus/fix/paddle_errors_fix github/fork/seiriosPlus/fix/paddle_op_errors github/fork/shangzhizhou/fix_test_activation_op_random_bug github/fork/smallv0221/yxp0924 github/fork/smallv0221/yxp0925 github/fork/swtkiwi/del-matplotlib github/fork/tianshuo78520a/kunlun_test github/fork/tianshuo78520a/update_dockerfile github/fork/wanghaoshuang/bert_fuse github/fork/wanghaoshuang/label_smooth github/fork/wanghuancoder/develop_CUDASynchronize github/fork/wanghuancoder/develop_Layer_doc github/fork/wanghuancoder/develop_ParameterList_doc github/fork/wanghuancoder/develop_Sequential_doc github/fork/wanghuancoder/develop_bilinear_tensor_product github/fork/wanghuancoder/develop_coverage_build_sh github/fork/wanghuancoder/develop_in_dynamic_mode_doc github/fork/wanghuancoder/develop_unique_name_doc github/fork/wangxicoding/fleet_meta_combine github/fork/wawltor/error_message_fix_5 github/fork/willthefrog/remove_l2_norm github/fork/windstamp/momentum_op github/fork/windstamp/mv_op_5 github/fork/windstamp/normal_api github/fork/wojtuss/wojtuss/fusion_gru_quantization github/fork/wojtuss/wojtuss/quantization-with-shift github/fork/wzzju/fix_err_info github/fork/wzzju/pure_fp16 github/fork/xiemoyuan/op_error_message github/fork/xiemoyuan/optimize_error_message github/fork/yaoxuefeng6/fix_doc github/fork/yaoxuefeng6/mod_dataset_v2 github/fork/yongqiangma/lod github/fork/ysh329/fix-clip-by-norm-error github/fork/ysh329/fix-error-clip-by-value github/fork/yukavio/error_info github/fork/zhangting2020/conv_filter_grad github/fork/zhangting2020/is_compile_with_cuda github/fork/zhangting2020/place_doc github/fork/zhangting2020/program github/fork/zhhsplendid/fix_any github/fork/zhhsplendid/refine_api2 github/fork/zhhsplendid/refine_api2_test github/fork/zhhsplendid/refine_api_test_ptb_lm github/fork/zhhsplendid/refine_api_test_resnet github/fork/zhhsplendid/refine_api_test_simnet github/fork/zhiqiu/dev/refine_initializer github/fork/zhiqiu/dev/remove_inplace_argument github/fork/zlsh80826/nvinfer_plugin_var_len_cuda11 improve_sccache incubate/infrt incubate/lite inplace_addto make_flag_adding_easier move_embedding_to_phi move_histogram_to_pten move_sgd_to_phi move_slice_to_pten move_temporal_shift_to_phi move_yolo_box_to_phi npu_fix_alloc numel paddle_tiny_install paralleltest preln_ernie prv-disable-more-cache prv-md-even-more prv-onednn-2.5 pten_tensor_refactor release/1.4 release/1.5 release/1.6 release/1.7 release/1.8 release/2.0 release/2.0-alpha release/2.0-beta release/2.0-rc release/2.0-rc1 release/2.1 release/2.2 release/2.3 release/2.3-fc-ernie-fix release/2.4 release/lite-0.1 revert-24981-add_device_attr_for_regulization revert-26856-strategy_example2 revert-27520-disable_pr revert-31068-fix_conv3d_windows revert-31562-mean revert-32290-develop-hardlabel revert-33037-forci revert-33475-fix_cifar_label_dimension revert-33630-bug-fix revert-34159-add_npu_bce_logical_dev revert-34406-add_copy_from_tensor revert-34910-spinlocks_for_allocator revert-35069-revert-34910-spinlocks_for_allocator revert-36057-dev/read_flags_in_ut revert-36201-refine_fast_threaded_ssa_graph_executor revert-36985-add_license revert-37318-refactor_dygraph_to_eager revert-37926-eager_coreops_500 revert-37956-revert-37727-pylayer_support_tuple revert-38100-mingdong revert-38301-allocation_rearrange_pr revert-38703-numpy_bf16_package_reupload revert-38732-remove_useless_header_in_elementwise_mul_grad revert-38959-Reduce_Grad revert-39143-adjust_empty revert-39227-move_trace_op_to_pten revert-39268-dev/remove_concat_fluid_kernel revert-40170-support_partial_grad revert-41056-revert-40727-move_some_activaion_to_phi revert-41065-revert-40993-mv_ele_floordiv_pow revert-41068-revert-40790-phi_new revert-41944-smaller_inference_api_test revert-42149-do-not-reset-default-stream-for-stream-safe-cuda-allocator revert-43155-fix_ut_tempfile revert-43882-revert-41944-smaller_inference_api_test revert-45808-phi/simplify_size_op revert-46827-deform_comment rocm_dev_0217 support_weight_transpose test_benchmark_ci test_feature_precision_test_c test_model_benchmark test_model_benchmark_ci zhiqiu-patch-1 v2.4.0-rc0 v2.3.2 v2.3.1 v2.3.0 v2.3.0-rc0 v2.2.2 v2.2.1 v2.2.0 v2.2.0-rc0 v2.2.0-bak0 v2.1.3 v2.1.2 v2.1.1 v2.1.0 v2.1.0-rc0 v2.0.2 v2.0.1 v2.0.0 v2.0.0-rc1 v2.0.0-rc0 v2.0.0-beta0 v2.0.0-alpha0 v1.8.5 v1.8.4 v1.8.3 v1.8.2 v1.8.1 v1.8.0 v1.7.2 v1.7.1 v1.7.0 v1.6.3 v1.6.2 v1.6.1 v1.6.0 v1.6.0-rc0 v1.5.2 v1.5.1 v1.5.0 v1.4.1 v1.4.0 lite-v0.1
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......@@ -156,6 +156,7 @@ void BlockDesc::RemoveOp(size_t s, size_t e) {
}
void BlockDesc::RemoveOpInternal(const OpDesc *op_desc) {
// TODO(minqiyang): make this faster
for (auto it = ops_.begin(); it != ops_.end(); ++it) {
if (it->get() == op_desc) {
ops_.erase(it);
......
......@@ -226,6 +226,8 @@ class PYBIND11_HIDDEN OpBase {
backward_hooks_() {}
virtual ~OpBase() {
// TODO(minqiyang): remove op_desc from block_desc in tracer
//
// reset all output vars' pre op
for (auto iter : output_vars_) {
for (VarBase* var : iter.second) {
......@@ -233,13 +235,6 @@ class PYBIND11_HIDDEN OpBase {
}
}
// remove op desc from block desc
if (op_desc_) {
if (block_) {
block_->RemoveOpInternal(op_desc_);
}
}
// release resource
for (framework::OpDesc* desc : grad_op_descs_) {
delete desc;
......
......@@ -19,7 +19,7 @@ import numpy as np
from .wrapped_decorator import signature_safe_contextmanager
from .core import VarDesc
from . import unique_name
from .imperative import base
from .imperative import base as imperative_base
__all__ = [
'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear',
......@@ -166,7 +166,7 @@ class ConstantInitializer(Initializer):
'force_cpu': self._force_cpu or force_init_on_cpu()
},
stop_gradient=True)
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......@@ -246,7 +246,7 @@ class UniformInitializer(Initializer):
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......@@ -325,7 +325,7 @@ class NormalInitializer(Initializer):
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......@@ -404,7 +404,7 @@ class TruncatedNormalInitializer(Initializer):
outputs={"Out": var},
attrs={"in_dtype": out_var.dtype,
"out_dtype": var.dtype})
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......@@ -510,7 +510,7 @@ class XavierInitializer(Initializer):
"seed": self._seed
},
stop_gradient=True)
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......@@ -611,7 +611,7 @@ class MSRAInitializer(Initializer):
"seed": self._seed
},
stop_gradient=True)
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......@@ -710,7 +710,7 @@ class BilinearInitializer(Initializer):
'shape': list(shape),
value_name: values
})
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......@@ -769,7 +769,7 @@ class NumpyArrayInitializer(Initializer):
value_name: values
},
stop_gradient=True)
if not base.enabled():
if not imperative_base.enabled():
var.op = op
return op
......
......@@ -191,126 +191,28 @@ class SimpleRNN(fluid.imperative.Layer):
return outs, pre_hiddens
# class TestImperative(unittest.TestCase):
# def test_sum_op(self):
# x = np.ones([2, 2], np.float32)
# with fluid.imperative.guard():
# inputs = []
# for _ in range(10):
# inputs.append(fluid.imperative.base.to_variable(x))
# ret = fluid.layers.sums(inputs)
# loss = fluid.layers.reduce_sum(ret)
# loss._backward()
# self.assertTrue(np.allclose(ret._numpy(), x * 10))
# self.assertTrue(np.allclose(inputs[0]._gradient(), x))
# def test_layer(self):
# with fluid.imperative.guard():
# cl = core.Layer()
# cl.forward([])
# l = fluid.imperative.Layer("l")
# self.assertRaises(NotImplementedError, l.forward, [])
# def test_layer_in_out(self):
# np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
# with fluid.imperative.guard():
# var_inp = fluid.imperative.base.to_variable(np_inp)
# l = MyLayer("my_layer")
# x = l(var_inp)[0]
# self.assertIsNotNone(x)
# dy_out = x._numpy()
# x._backward()
# dy_grad = l._x_for_debug._gradient()
# with new_program_scope():
# inp = fluid.layers.data(name="inp", shape=[3], append_batch_size=False)
# l = MyLayer("my_layer")
# x = l(inp)[0]
# param_grads = fluid.backward.append_backward(x, parameter_list=[l._x_for_debug.name])[0]
# exe = fluid.Executor(fluid.CPUPlace(
# ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
# static_out, static_grad = exe.run(feed={inp.name: np_inp},
# fetch_list=[x.name, param_grads[1].name])
# self.assertTrue(np.allclose(dy_out, static_out))
# self.assertTrue(np.allclose(dy_grad, static_grad))
# with fluid.imperative.guard():
# var_inp = fluid.imperative.base.to_variable(np_inp)
# mlp = MLP("mlp")
# out = mlp(var_inp)
# dy_out = out._numpy()
# out._backward()
# dy_grad = mlp._fc1._w._gradient()
# with new_program_scope():
# inp = fluid.layers.data(
# name="inp", shape=[2, 2], append_batch_size=False)
# mlp = MLP("mlp")
# out = mlp(inp)
# param_grads = fluid.backward.append_backward(out, parameter_list=[mlp._fc1._w.name])[0]
# exe = fluid.Executor(fluid.CPUPlace(
# ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
# exe.run(fluid.default_startup_program())
# static_out, static_grad = exe.run(
# feed={inp.name: np_inp},
# fetch_list=[out.name, param_grads[1].name])
# self.assertTrue(np.allclose(dy_out, static_out))
# self.assertTrue(np.allclose(dy_grad, static_grad))
# params = mlp.parameters(True)
# self.assertEqual("mlp/MLP_0/FC_0_0.w_0", params[0].name)
# self.assertEqual("mlp/MLP_0/FC_0_0.b_0", params[1].name)
# self.assertEqual("mlp/MLP_0/FC_1_0.w_0", params[2].name)
# self.assertEqual("mlp/MLP_0/FC_1_0.b_0", params[3].name)
# self.assertEqual(len(params), 4)
# sublayers = mlp.sublayers(True)
# self.assertEqual(mlp._fc1, sublayers[0])
# self.assertEqual(mlp._fc2, sublayers[1])
# self.assertEqual(len(sublayers), 2)
# def test_rnn(self):
# np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
# [10.0, 11.0, 12.0]])
# np_inp = np_inp.reshape((1, 4, 3))
# np_inp = np_inp.astype(np.float32)
# with fluid.imperative.guard():
# var_inp = fluid.imperative.base.to_variable(np_inp)
# var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
# simple_rnn = SimpleRNN("simple_rnn")
# outs, pre_hiddens = simple_rnn.forward(var_inp)
# dy_out = outs[3]._numpy()
# outs[3]._backward()
# dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
# dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
# dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
# with new_program_scope():
# inp = fluid.layers.data(
# name="inp", shape=[1, 4, 3], append_batch_size=False)
# simple_rnn = SimpleRNN("simple_rnn")
# outs, pre_hiddens = simple_rnn(inp)
# param_grads = fluid.backward.append_backward(outs[3])
# exe = fluid.Executor(fluid.CPUPlace())
# exe.run(fluid.default_startup_program())
# static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run(
# feed={inp.name: np_inp},
# fetch_list=[
# outs[3].name, param_grads[0][1].name,
# param_grads[1][1].name, param_grads[2][1].name
# ])
# self.assertTrue(np.allclose(dy_out, static_out))
# self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o))
# self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h))
# self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h))
class TestImperativePyLayer(unittest.TestCase):
class TestImperative(unittest.TestCase):
def test_sum_op(self):
x = np.ones([2, 2], np.float32)
with fluid.imperative.guard():
inputs = []
for _ in range(10):
inputs.append(fluid.imperative.base.to_variable(x))
ret = fluid.layers.sums(inputs)
loss = fluid.layers.reduce_sum(ret)
loss._backward()
self.assertTrue(np.allclose(ret._numpy(), x * 10))
self.assertTrue(np.allclose(inputs[0]._gradient(), x))
def test_layer(self):
with fluid.imperative.guard():
cl = core.Layer()
cl.forward([])
l = fluid.imperative.Layer("l")
self.assertRaises(NotImplementedError, l.forward, [])
def test_pylayer_func_id(self):
with fluid.imperative.guard():
class PyLayer1(fluid.imperative.PyLayer):
......@@ -378,6 +280,109 @@ class TestImperativePyLayer(unittest.TestCase):
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
def test_layer_in_out(self):
np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
l = MyLayer("my_layer")
x = l(var_inp)[0]
self.assertIsNotNone(x)
dy_out = x._numpy()
x._backward()
dy_grad = l._x_for_debug._gradient()
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[3], append_batch_size=False)
l = MyLayer("my_layer")
x = l(inp)[0]
param_grads = fluid.backward.append_backward(
x, parameter_list=[l._x_for_debug.name])[0]
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
static_out, static_grad = exe.run(
feed={inp.name: np_inp},
fetch_list=[x.name, param_grads[1].name])
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
def test_mlp(self):
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
mlp = MLP("mlp")
out = mlp(var_inp)
dy_out = out._numpy()
out._backward()
dy_grad = mlp._fc1._w._gradient()
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
mlp = MLP("mlp")
out = mlp(inp)
param_grads = fluid.backward.append_backward(
out, parameter_list=[mlp._fc1._w.name])[0]
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
exe.run(fluid.default_startup_program())
static_out, static_grad = exe.run(
feed={inp.name: np_inp},
fetch_list=[out.name, param_grads[1].name])
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad, static_grad))
params = mlp.parameters(True)
self.assertEqual("mlp/MLP_0/FC_0_0.w_0", params[0].name)
self.assertEqual("mlp/MLP_0/FC_0_0.b_0", params[1].name)
self.assertEqual("mlp/MLP_0/FC_1_0.w_0", params[2].name)
self.assertEqual("mlp/MLP_0/FC_1_0.b_0", params[3].name)
self.assertEqual(len(params), 4)
sublayers = mlp.sublayers(True)
self.assertEqual(mlp._fc1, sublayers[0])
self.assertEqual(mlp._fc2, sublayers[1])
self.assertEqual(len(sublayers), 2)
def test_rnn(self):
np_inp = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0],
[10.0, 11.0, 12.0]])
np_inp = np_inp.reshape((1, 4, 3))
np_inp = np_inp.astype(np.float32)
with fluid.imperative.guard():
var_inp = fluid.imperative.base.to_variable(np_inp)
var_inp = fluid.layers.reshape(var_inp, shape=[1, 4, 3])
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn.forward(var_inp)
dy_out = outs[3]._numpy()
outs[3]._backward()
dy_grad_h2o = simple_rnn._cell._h2o_w._gradient()
dy_grad_h2h = simple_rnn._cell._h2h_w._gradient()
dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
with new_program_scope():
inp = fluid.layers.data(
name="inp", shape=[1, 4, 3], append_batch_size=False)
simple_rnn = SimpleRNN("simple_rnn")
outs, pre_hiddens = simple_rnn(inp)
param_grads = fluid.backward.append_backward(outs[3])
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
static_out, static_grad_h2o, static_grad_h2h, static_grad_i2h = exe.run(
feed={inp.name: np_inp},
fetch_list=[
outs[3].name, param_grads[0][1].name,
param_grads[1][1].name, param_grads[2][1].name
])
self.assertTrue(np.allclose(dy_out, static_out))
self.assertTrue(np.allclose(dy_grad_h2o, static_grad_h2o))
self.assertTrue(np.allclose(dy_grad_h2h, static_grad_h2h))
self.assertTrue(np.allclose(dy_grad_i2h, static_grad_i2h))
if __name__ == '__main__':
unittest.main()
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