From 38f9b71bdb2ac16bbc23c6ba6493b7fef4698b5c Mon Sep 17 00:00:00 2001 From: Chengmo Date: Fri, 3 Jul 2020 11:24:40 +0800 Subject: [PATCH] [cherry-pick] fix fluid.embedding (#25328) * test=release/1.8, cherry fix fluid.embedding --- .../distributed/parameter_prefetch.cc | 12 +++-- .../distributed_lookup_table_op.cc | 49 ++++++++++++++++--- .../fluid/tests/unittests/dist_simnet_bow.py | 27 +++++----- .../tests/unittests/test_dist_simnet_bow.py | 8 +-- .../fluid/transpiler/distribute_transpiler.py | 37 +++++++------- 5 files changed, 88 insertions(+), 45 deletions(-) diff --git a/paddle/fluid/operators/distributed/parameter_prefetch.cc b/paddle/fluid/operators/distributed/parameter_prefetch.cc index c9bbc3c1935..428ee6ee184 100644 --- a/paddle/fluid/operators/distributed/parameter_prefetch.cc +++ b/paddle/fluid/operators/distributed/parameter_prefetch.cc @@ -209,16 +209,20 @@ void prefetchs(const std::vector& id_var_names, TableAndEndpoints tables; for (auto& id_name : id_var_names) { - auto& id_tensor = scope.FindVar(id_name)->Get(); - auto* id_data = id_tensor.data(); + auto* id_tensor = + scope.FindVar(id_name)->GetMutable(); + auto id_dims = id_tensor->dims(); + id_tensor->Resize(framework::make_ddim( + {static_cast(id_dims[0] * id_dims[1]), 1})); + auto* id_data = id_tensor->data(); std::vector ids; - for (int64_t i = 0; i < id_tensor.numel(); ++i) { + for (int64_t i = 0; i < id_tensor->numel(); ++i) { ids.push_back(id_data[i]); ids_union.push_back(id_data[i]); } ids_group.push_back(ids); - ids_lods.push_back(id_tensor.lod()); + ids_lods.push_back(id_tensor->lod()); } std::unordered_set s(ids_union.begin(), ids_union.end()); diff --git a/paddle/fluid/operators/distributed_ops/distributed_lookup_table_op.cc b/paddle/fluid/operators/distributed_ops/distributed_lookup_table_op.cc index f37d2b1eee1..77150c4e48e 100644 --- a/paddle/fluid/operators/distributed_ops/distributed_lookup_table_op.cc +++ b/paddle/fluid/operators/distributed_ops/distributed_lookup_table_op.cc @@ -26,7 +26,7 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; - void InferShape(framework::InferShapeContext *ctx) const override { + void InferShape(framework::InferShapeContext* ctx) const override { PADDLE_ENFORCE(ctx->HasInputs("Ids"), "Input(Ids) of LookupTableOp should not be null."); PADDLE_ENFORCE(ctx->HasInput("W"), @@ -40,11 +40,9 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel { PADDLE_ENFORCE_EQ(table_dims.size(), 2, "Only 2 dimensions of the 'Embedding' is supported."); - for (auto &ids_dim : ids_dims) { + for (auto& ids_dim : ids_dims) { PADDLE_ENFORCE_EQ(ids_dim.size(), 2, "The dimension of the 'Ids' tensor must be 2."); - PADDLE_ENFORCE_EQ(ids_dim[1], 1, - "The last dimension of the 'Ids' tensor must be 1."); } auto lookup_tables = @@ -52,6 +50,8 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel { auto height_sections = ctx->Attrs().Get>("height_sections"); auto endpoints = ctx->Attrs().Get>("endpoints"); + auto lookup_table_version = + ctx->Attrs().Get("lookup_table_version"); PADDLE_ENFORCE(lookup_tables.size() == height_sections.size() && lookup_tables.size() == endpoints.size() && @@ -61,8 +61,15 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel { auto outputs_dims = std::vector(); - for (auto &ids_dim : ids_dims) { - outputs_dims.push_back(framework::make_ddim({ids_dim[0], table_dims[1]})); + for (auto& ids_dim : ids_dims) { + if (lookup_table_version == "lookup_table") { + outputs_dims.push_back( + framework::make_ddim({ids_dim[0], table_dims[1]})); + } else if (lookup_table_version == "lookup_table_v2") { + outputs_dims.push_back(framework::make_ddim( + {static_cast(ids_dim[0]), static_cast(ids_dim[1]), + static_cast(table_dims[1])})); + } } ctx->SetOutputsDim("Outputs", outputs_dims); @@ -71,7 +78,7 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel { protected: framework::OpKernelType GetExpectedKernelType( - const framework::ExecutionContext &ctx) const override { + const framework::ExecutionContext& ctx) const override { return framework::OpKernelType( framework::proto::VarType::Type(ctx.Attr("dtype")), ctx.GetPlace()); @@ -81,7 +88,7 @@ class DistributedLookupTableOp : public framework::OperatorWithKernel { template class DistributedLookupTableKernel : public framework::OpKernel { public: - void Compute(const framework::ExecutionContext &context) const override { + void Compute(const framework::ExecutionContext& context) const override { auto ids_vars = context.MultiInputVar("Ids"); auto emb_vars = context.MultiOutput("Embeddings"); @@ -93,10 +100,30 @@ class DistributedLookupTableKernel : public framework::OpKernel { auto height_sections = context.Attr>("height_sections"); auto endpoints = context.Attr>("endpoints"); + auto lookup_table_version = + context.Attr("lookup_table_version"); operators::distributed::prefetchs( id_names, out_names, embedding_name, false, lookup_tables, endpoints, height_sections, context, context.scope()); + + if (lookup_table_version == "lookup_table_v2") { + auto& scope = context.scope(); + auto emb_dim = + scope.FindVar(embedding_name)->Get().dims()[1]; + + for (size_t i = 0; i < id_names.size(); ++i) { + auto* id_var = scope.FindVar(id_names[i]); + auto* out_var = scope.FindVar(out_names[i]); + auto* id_tensor = id_var->GetMutable(); + auto* out_tensor = out_var->GetMutable(); + + auto id_dims = id_tensor->dims(); + out_tensor->Resize(framework::make_ddim( + {static_cast(id_dims[0]), static_cast(id_dims[1]), + static_cast(emb_dim)})); + } + } } }; @@ -134,6 +161,12 @@ class DistributedLookupTableOpMaker : public framework::OpProtoAndCheckerMaker { AddAttr("trainer_id", "trainer id from 0 ~ worker_num.").SetDefault(0); + AddAttr( + "lookup_table_version", + "(string, default lookup_table) " + "To distinguish between different versions of embedding OP") + .SetDefault(std::string("lookup_table")); + AddAttr("padding_idx", "(int64, default -1) " "If the value is -1, it makes no effect to lookup. " diff --git a/python/paddle/fluid/tests/unittests/dist_simnet_bow.py b/python/paddle/fluid/tests/unittests/dist_simnet_bow.py index 09afae6114e..9fcba2aede1 100644 --- a/python/paddle/fluid/tests/unittests/dist_simnet_bow.py +++ b/python/paddle/fluid/tests/unittests/dist_simnet_bow.py @@ -92,8 +92,8 @@ def train_network(batch_size, # query q = fluid.layers.data( name="query_ids", shape=[1], dtype="int64", lod_level=1) - ## embedding - q_emb = fluid.layers.embedding( + # embedding + q_emb = fluid.embedding( input=q, is_distributed=is_distributed, size=[dict_dim, emb_dim], @@ -104,10 +104,11 @@ def train_network(batch_size, initializer=fluid.initializer.Constant(value=0.01), name="__emb__"), is_sparse=is_sparse) - ## vsum + q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim]) + # vsum q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum') q_ss = fluid.layers.softsign(q_sum) - ## fc layer after conv + # fc layer after conv q_fc = fluid.layers.fc( input=q_ss, size=hid_dim, @@ -120,8 +121,8 @@ def train_network(batch_size, # pt pt = fluid.layers.data( name="pos_title_ids", shape=[1], dtype="int64", lod_level=1) - ## embedding - pt_emb = fluid.layers.embedding( + # embedding + pt_emb = fluid.embedding( input=pt, is_distributed=is_distributed, size=[dict_dim, emb_dim], @@ -132,10 +133,11 @@ def train_network(batch_size, initializer=fluid.initializer.Constant(value=0.01), name="__emb__"), is_sparse=is_sparse) - ## vsum + pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim]) + # vsum pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum') pt_ss = fluid.layers.softsign(pt_sum) - ## fc layer + # fc layer pt_fc = fluid.layers.fc( input=pt_ss, size=hid_dim, @@ -147,8 +149,8 @@ def train_network(batch_size, # nt nt = fluid.layers.data( name="neg_title_ids", shape=[1], dtype="int64", lod_level=1) - ## embedding - nt_emb = fluid.layers.embedding( + # embedding + nt_emb = fluid.embedding( input=nt, is_distributed=is_distributed, size=[dict_dim, emb_dim], @@ -159,10 +161,11 @@ def train_network(batch_size, initializer=fluid.initializer.Constant(value=0.01), name="__emb__"), is_sparse=is_sparse) - ## vsum + nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim]) + # vsum nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum') nt_ss = fluid.layers.softsign(nt_sum) - ## fc layer + # fc layer nt_fc = fluid.layers.fc( input=nt_ss, size=hid_dim, diff --git a/python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py b/python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py index a872b5ce4db..3189f092413 100644 --- a/python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py +++ b/python/paddle/fluid/tests/unittests/test_dist_simnet_bow.py @@ -46,7 +46,7 @@ class TestDistSimnetBow2x2DenseAsync(TestDistBase): self._sync_mode = False self._enforce_place = "CPU" - #FIXME(typhoonzero): fix async tests later + # FIXME(typhoonzero): fix async tests later def notest_simnet_bow(self): need_envs = { "IS_DISTRIBUTED": '0', @@ -107,7 +107,7 @@ class TestDistSimnetBow2x2LookupTableSync(TestDistBase): def test_simnet_bow(self): need_envs = { - "IS_DISTRIBUTED": '1', + "IS_DISTRIBUTED": '0', "IS_SPARSE": '1', 'IS_SELF_CONTAINED_LR': '1' } @@ -126,7 +126,7 @@ class TestDistSimnetBow2x2LookupTableAsync(TestDistBase): def test_simnet_bow(self): need_envs = { - "IS_DISTRIBUTED": '1', + "IS_DISTRIBUTED": '0', "IS_SPARSE": '1', 'IS_SELF_CONTAINED_LR': '1' } @@ -145,7 +145,7 @@ class TestDistSimnetBow2x2LookupTableNotContainLRSync(TestDistBase): def test_simnet_bow(self): need_envs = { - "IS_DISTRIBUTED": '1', + "IS_DISTRIBUTED": '0', "IS_SPARSE": '1', 'IS_SELF_CONTAINED_LR': '0' } diff --git a/python/paddle/fluid/transpiler/distribute_transpiler.py b/python/paddle/fluid/transpiler/distribute_transpiler.py index 11729940e16..b6ee381136a 100644 --- a/python/paddle/fluid/transpiler/distribute_transpiler.py +++ b/python/paddle/fluid/transpiler/distribute_transpiler.py @@ -50,8 +50,8 @@ from .details import delete_ops, find_op_by_output_arg from ..distribute_lookup_table import find_distributed_lookup_table from . import collective -LOOKUP_TABLE_TYPE = "lookup_table" -LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad" +LOOKUP_TABLE_TYPE = ["lookup_table", "lookup_table_v2"] +LOOKUP_TABLE_GRAD_TYPE = ["lookup_table_grad", "lookup_table_v2_grad"] OP_NAME_SCOPE = "op_namescope" CLIP_OP_NAME_SCOPE = "@CLIP" OP_ROLE_VAR_ATTR_NAME = core.op_proto_and_checker_maker.kOpRoleVarAttrName() @@ -199,10 +199,10 @@ class DistributeTranspilerConfig(object): geo_sgd_need_push_nums = 100 nccl_comm_num = 1 - #The picture here illustrates the principle: - #https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396 + # The picture here illustrates the principle: + # https://github.com/PaddlePaddle/Paddle/pull/17263#discussion_r285411396 use_hierarchical_allreduce = False - #Nccl ranks in a node when use hierarchical allreduce, it's set to gpu cards' number in most cases. + # Nccl ranks in a node when use hierarchical allreduce, it's set to gpu cards' number in most cases. hierarchical_allreduce_inter_nranks = 0 # if mode is collective @@ -445,7 +445,7 @@ class DistributeTranspiler(object): def _get_all_remote_sparse_update_op(self, main_program): sparse_update_ops = [] - sparse_update_op_types = ["lookup_table", "nce"] + sparse_update_op_types = ["lookup_table", "nce", "lookup_table_v2"] for op in main_program.global_block().ops: if op.type in sparse_update_op_types and op.attr( 'remote_prefetch') is True: @@ -475,7 +475,7 @@ class DistributeTranspiler(object): ops.append(op) used_ops.append(idx) - if op_type == "lookup_table": + if op_type in LOOKUP_TABLE_TYPE: all_ops = program.global_block().ops op_idxs = [all_ops.index(op) for op in ops] inputs = [ @@ -521,7 +521,8 @@ class DistributeTranspiler(object): "height_sections": height_sections, "endpoints": endpoints, "padding_idx": padding_idx, - "trainer_id": self.trainer_id + "trainer_id": self.trainer_id, + "lookup_table_version": op_type }) else: raise ValueError( @@ -609,10 +610,12 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler ) assert trainers_num > self.config.hierarchical_allreduce_inter_nranks, \ - "trainers_num:{} < hierarchical_allreduce_inter_nranks:{}".format(trainers_num, self.config.hierarchical_allreduce_inter_nranks) + "trainers_num:{} < hierarchical_allreduce_inter_nranks:{}".format( + trainers_num, self.config.hierarchical_allreduce_inter_nranks) assert trainers_num % self.config.hierarchical_allreduce_inter_nranks == 0, \ - "trainers_num:{} mod hierarchical_allreduce_inter_nranks:{} != 0".format(trainers_num, self.config.hierarchical_allreduce_inter_nranks) + "trainers_num:{} mod hierarchical_allreduce_inter_nranks:{} != 0".format( + trainers_num, self.config.hierarchical_allreduce_inter_nranks) self.origin_program._hierarchical_allreduce_inter_nranks = \ int(self.config.hierarchical_allreduce_inter_nranks) @@ -778,7 +781,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler decay_dummy_output = program.global_block().create_var( name=framework.generate_control_dev_var_name()) if self.config.runtime_split_send_recv: - ## async mode, using communicator to merge and send + # async mode, using communicator to merge and send send_varnames = [self.counter_var.name] else: send_varnames = [] @@ -1015,7 +1018,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler - Delete optimizer related op, because parameter updated on Pserver - After the op which computed gradient of each parameter, add ``Send_op`` and ``Recv_op`` - + Args: wait_port(bool): Whether to wait for the parameter server to be ready before returning to program, default is True @@ -1072,7 +1075,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler sparse_table_names = self._get_sparse_table_names() # self._fake_init_sparsetable(sparse_table_names) - #self._delete_trainer_optimizer(is_startup=True) + # self._delete_trainer_optimizer(is_startup=True) for varname, splited_var in six.iteritems(self.param_var_mapping): if varname in sparse_table_names: @@ -1466,8 +1469,8 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler Program: parameter server side startup program. Examples: - .. code-block:: python - + .. code-block:: python + pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174" trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174" current_endpoint = "192.168.0.1:6174" @@ -2661,7 +2664,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler for op in block.ops: if self._is_opt_role_op(op): # Todo(chengmo): Whether clip related op belongs to Optimize guard should be discussed - # delete clip op from opt_ops when run in Parameter Server mode + # delete clip op from opt_ops when run in Parameter Server mode if OP_NAME_SCOPE in op.all_attrs( ) and CLIP_OP_NAME_SCOPE in op.attr( OP_NAME_SCOPE @@ -2692,7 +2695,7 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler return opt_ops, params_grads def _get_distribute_update_vars(self): - #TODO(chengmo): find more powerful and simple way to deal with these special situation + # TODO(chengmo): find more powerful and simple way to deal with these special situation """ This Function is used for a special model, like PyramidDnn which has pyramid hash op. Some Parameters don't use optimizing op to update its value, but updated in its BP process. -- GitLab