[AutoParallel] add gpt model for unittest (#38202)

* add gpt modeling

* update file name

python/paddle/fluid/tests/unittests/auto_parallel_gpt_model.py

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed 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.
+
+from __future__ import print_function
+
+import collections
+import random
+import numpy as np
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+import paddle.tensor as tensor
+import paddle.distributed.auto_parallel as auto
+from paddle import fluid
+from paddle.fluid import layers
+from paddle.distributed import fleet
+from paddle.nn.layer.transformer import _convert_param_attr_to_list
+from paddle.fluid.initializer import Normal, NumpyArrayInitializer
+
+paddle.enable_static()
+
+
+def init_global():
+    global _global_parallel_strategy
+    global _global_process_mesh
+    global PP_MESH_LIST
+    global DPPP_MESH_LIST
+    global MPPP_MESH_LIST
+    global DPMPPP_MESH_LIST
+
+
+class MultiHeadAttention(nn.Layer):
+    """
+    Attention mapps queries and a set of key-value pairs to outputs, and
+    Multi-Head Attention performs multiple parallel attention to jointly attending
+    to information from different representation subspaces.
+    """
+    Cache = collections.namedtuple("Cache", ["k", "v"])
+    StaticCache = collections.namedtuple("StaticCache", ["k", "v"])
+
+    def __init__(self,
+                 embed_dim,
+                 num_heads,
+                 dropout=0.,
+                 kdim=None,
+                 vdim=None,
+                 need_weights=False,
+                 weight_attr=None,
+                 bias_attr=None,
+                 fuse=False,
+                 mesh_idx=None):
+        super(MultiHeadAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.need_weights = need_weights
+        self.fuse = fuse
+        self.mesh_idx = mesh_idx
+        self.head_dim = embed_dim // num_heads
+        assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
+        if self.fuse:
+            assert self.kdim == embed_dim
+            assert self.vdim == embed_dim
+            self.qkv_proj = nn.Linear(
+                embed_dim, 3 * embed_dim, weight_attr, bias_attr=bias_attr)
+        else:
+            self.q_proj = nn.Linear(
+                embed_dim,
+                embed_dim,
+                weight_attr=weight_attr,
+                bias_attr=bias_attr)
+            self.k_proj = nn.Linear(
+                self.kdim,
+                embed_dim,
+                weight_attr=weight_attr,
+                bias_attr=bias_attr)
+            self.v_proj = nn.Linear(
+                self.vdim,
+                embed_dim,
+                weight_attr=weight_attr,
+                bias_attr=bias_attr)
+        self.out_proj = nn.Linear(
+            embed_dim, embed_dim, weight_attr=weight_attr, bias_attr=bias_attr)
+
+    def _fuse_prepare_qkv(self, query):
+        mix_layer = self.qkv_proj(query)
+        mix_layer = paddle.reshape_(mix_layer,
+                                    [0, 0, self.num_heads, 3 * self.head_dim])
+        mix_layer = paddle.transpose(mix_layer, [0, 2, 1, 3])
+        q, k, v = paddle.split(mix_layer, num_or_sections=3, axis=-1)
+        return q, k, v
+
+    def _prepare_qkv(self, query, key, value, use_cache=False, cache=None):
+        """
+        Prapares linear projected queries, keys and values for usage of subsequnt
+        multiple parallel attention. If `cache` is not None, using cached results
+        to reduce redundant calculations.
+        """
+        q = self.q_proj(query)
+        if _global_parallel_strategy == "mp":
+            auto.shard_tensor(
+                self.q_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 0]
+                })
+        elif _global_parallel_strategy == "dp_mp":
+            auto.shard_tensor(
+                self.q_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 1]
+                })
+        elif _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                self.q_proj.weight,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 0]
+                })
+        elif _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                self.q_proj.weight,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 1]
+                })
+        q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
+        q = tensor.transpose(x=q, perm=[0, 2, 1, 3])
+        if isinstance(cache, self.StaticCache):
+            # for encoder-decoder attention in inference and has cached
+            k, v = cache.k, cache.v
+        else:
+            k, v = self.compute_kv(key, value)
+        if isinstance(cache, self.Cache):
+            # for decoder self-attention in inference
+            k = tensor.concat([cache.k, k], axis=2)
+            v = tensor.concat([cache.v, v], axis=2)
+        if use_cache is True:
+            cache = self.Cache(k, v)
+        return (q, k, v) if use_cache is False else (q, k, v, cache)
+
+    def compute_kv(self, key, value):
+        """
+        Applies linear projection on input keys and values, then splits heads
+        (reshape and transpose) to get keys and values from different representation
+        subspaces. The results are used as key-values pairs for subsequent multiple
+        parallel attention.
+        It is part of calculations in multi-head attention, and is provided as
+        a method to pre-compute and prefetch these results, thus we can use them
+        to construct cache for inference.
+        """
+        k = self.k_proj(key)
+        if _global_parallel_strategy == "mp":
+            auto.shard_tensor(
+                self.k_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 0]
+                })
+        elif _global_parallel_strategy == "dp_mp":
+            auto.shard_tensor(
+                self.k_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 1]
+                })
+        elif _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                self.k_proj.weight,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 0]
+                })
+        elif _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                self.k_proj.weight,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 1]
+                })
+        v = self.v_proj(value)
+        if _global_parallel_strategy == "mp":
+            auto.shard_tensor(
+                self.v_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 0]
+                })
+        elif _global_parallel_strategy == "dp_mp":
+            auto.shard_tensor(
+                self.v_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 1]
+                })
+        elif _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                self.v_proj.weight,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 0]
+                })
+        elif _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                self.v_proj.weight,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 1]
+                })
+        k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
+        k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
+        v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
+        v = tensor.transpose(x=v, perm=[0, 2, 1, 3])
+        return k, v
+
+    def gen_cache(self, key, value=None, type=Cache):
+        """
+        Generates cache for `forward` usage in inference accroding to arguments.
+        The generated cache is an instance of `MultiHeadAttention.Cache` or an
+        instance of `MultiHeadAttention.StaticCache`.
+        """
+        if type == MultiHeadAttention.StaticCache:  # static_kv
+            k, v = self.compute_kv(key, value)
+            return self.StaticCache(k, v)
+        elif value is None:  # incremental_state
+            k = layers.fill_constant_batch_size_like(
+                input=key,
+                shape=[-1, self.num_heads, 0, self.head_dim],
+                dtype=key.dtype,
+                value=0)
+            v = layers.fill_constant_batch_size_like(
+                input=key,
+                shape=[-1, self.num_heads, 0, self.head_dim],
+                dtype=key.dtype,
+                value=0)
+            return self.Cache(k, v)
+        else:
+            # incremental_state with initial value, mainly for usage like UniLM
+            return self.Cache(key, value)
+
+    def forward(self,
+                query,
+                key,
+                value,
+                attn_mask=None,
+                use_cache=False,
+                cache=None):
+        """
+        Applies multi-head attention to map queries and a set of key-value pairs
+        to outputs.
+        """
+        key = query if key is None else key
+        value = query if value is None else value
+        # compute q ,k ,v
+        if use_cache is False:
+            if self.fuse:
+                q, k, v = self._fuse_prepare_qkv(query)
+            else:
+                q, k, v = self._prepare_qkv(query, key, value, use_cache, cache)
+        else:
+            q, k, v, cache = self._prepare_qkv(query, key, value, use_cache,
+                                               cache)
+        product = layers.matmul(
+            x=q, y=k, transpose_y=True, alpha=self.head_dim**-0.5)
+        if attn_mask is not None:
+            product = product + attn_mask
+        weights = F.softmax(product)
+        if self.dropout:
+            weights = F.dropout(
+                weights,
+                self.dropout,
+                training=self.training,
+                mode="upscale_in_train")
+        out = tensor.matmul(weights, v)
+        # combine heads
+        out = tensor.transpose(out, perm=[0, 2, 1, 3])
+        out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
+        # project to output
+        out = self.out_proj(out)
+        if _global_parallel_strategy == "mp":
+            auto.shard_tensor(
+                self.out_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [0, -1]
+                })
+        elif _global_parallel_strategy == "dp_mp":
+            auto.shard_tensor(
+                self.out_proj.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [1, -1]
+                })
+        elif _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                self.out_proj.weight,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [0, -1]
+                })
+        elif _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                self.out_proj.weight,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [1, -1]
+                })
+        outs = [out]
+        if self.need_weights:
+            outs.append(weights)
+        if use_cache:
+            outs.append(cache)
+        return out if len(outs) == 1 else tuple(outs)
+
+
+class TransformerDecoder(nn.Layer):
+    """
+    TransformerDecoder is a stack of N decoder layers.
+    """
+
+    def __init__(self, decoder_layers, num_layers, norm=None, hidden_size=None):
+        super(TransformerDecoder, self).__init__()
+
+        self.num_layers = num_layers
+        self.layers = decoder_layers
+        self.norm = norm
+        if norm is "LayerNorm":
+            self.norm = nn.LayerNorm(hidden_size)
+        elif norm is not None:
+            raise ValueError("Only support LayerNorm")
+        self.checkpoints = []
+
+    def forward(self,
+                tgt,
+                memory,
+                tgt_mask=None,
+                memory_mask=None,
+                use_cache=False,
+                cache=None):
+        """
+        Applies a stack of N Transformer decoder layers on inputs. If `norm` is
+        provided, also applies layer normalization on the output of last decoder
+        layer.
+        """
+        output = tgt
+        new_caches = []
+        self.checkpoints = []
+        if _global_parallel_strategy == "pp":
+            auto.shard_tensor(
+                output,
+                dist_attr={
+                    "process_mesh": PP_MESH_LIST[0],
+                    "dims_mapping": [-1 for i in range(len(output.shape))]
+                })
+        if _global_parallel_strategy == "dp_pp":
+            auto.shard_tensor(
+                output,
+                dist_attr={
+                    "process_mesh": DPPP_MESH_LIST[0],
+                    "dims_mapping":
+                    [0] + [-1 for i in range(len(output.shape) - 1)]
+                })
+        if _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                output,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[0],
+                    "dims_mapping":
+                    [-1] + [-1 for i in range(len(output.shape) - 1)]
+                })
+        if _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                output,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[0],
+                    "dims_mapping":
+                    [0] + [-1 for i in range(len(output.shape) - 1)]
+                })
+        for i, mod in enumerate(self.layers):
+            if cache is None:
+                if use_cache:
+                    if _global_parallel_strategy == "pp":
+                        output, new_cache = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": PP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": PP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping":
+                                [-1 for i in range(len(output.shape))]
+                            })
+                    elif _global_parallel_strategy == "dp_pp":
+                        output, new_cache = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": DPPP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": DPPP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping": [0] +
+                                [-1 for i in range(len(output.shape) - 1)]
+                            })
+                    elif _global_parallel_strategy == "mp_pp":
+                        output, new_cache = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": MPPP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": MPPP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping": [-1] +
+                                [-1 for i in range(len(output.shape) - 1)]
+                            })
+                    elif _global_parallel_strategy == "dp_mp_pp":
+                        output, new_cache = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": DPMPPP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": DPMPPP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping": [0] +
+                                [-1 for i in range(len(output.shape) - 1)]
+                            })
+                    else:
+                        output, new_cache = mod(output,
+                                                memory,
+                                                tgt_mask=tgt_mask,
+                                                use_cache=use_cache,
+                                                cache=cache)
+                    new_caches.append(new_cache)
+                else:
+                    if _global_parallel_strategy == "pp":
+                        output = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": PP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": PP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping":
+                                [-1 for i in range(len(output.shape))]
+                            })
+                    elif _global_parallel_strategy == "dp_pp":
+                        output = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": DPPP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": DPPP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping": [0] +
+                                [-1 for i in range(len(output.shape) - 1)]
+                            })
+                    elif _global_parallel_strategy == "mp_pp":
+                        output = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": MPPP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": MPPP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping": [-1] +
+                                [-1 for i in range(len(output.shape) - 1)]
+                            })
+                    elif _global_parallel_strategy == "dp_mp_pp":
+                        output = auto.shard_op(
+                            mod,
+                            dist_attr={
+                                "process_mesh": DPMPPP_MESH_LIST[mod.mesh_idx]
+                            })(output, memory, tgt_mask, use_cache, cache)[0]
+                        auto.shard_tensor(
+                            output,
+                            dist_attr={
+                                "process_mesh": DPMPPP_MESH_LIST[mod.mesh_idx],
+                                "dims_mapping": [0] +
+                                [-1 for i in range(len(output.shape) - 1)]
+                            })
+                    else:
+                        output = mod(output,
+                                     memory,
+                                     tgt_mask=tgt_mask,
+                                     use_cache=use_cache,
+                                     cache=cache)
+            else:
+                if _global_parallel_strategy == "pp":
+                    output, new_cache = auto.shard_op(
+                        mod,
+                        dist_attr={"process_mesh": PP_MESH_LIST[mod.mesh_idx]})(
+                            output, memory, tgt_mask, use_cache, cache)
+                    auto.shard_tensor(
+                        output,
+                        dist_attr={
+                            "process_mesh": PP_MESH_LIST[mod.mesh_idx],
+                            "dims_mapping":
+                            [-1 for i in range(len(output.shape))]
+                        })
+                elif _global_parallel_strategy == "dp_pp":
+                    output, new_cache = auto.shard_op(
+                        mod,
+                        dist_attr={
+                            "process_mesh": DPPP_MESH_LIST[mod.mesh_idx]
+                        })(output, memory, tgt_mask, use_cache, cache)
+                    auto.shard_tensor(
+                        output,
+                        dist_attr={
+                            "process_mesh": DPPP_MESH_LIST[mod.mesh_idx],
+                            "dims_mapping":
+                            [0] + [-1 for i in range(len(output.shape) - 1)]
+                        })
+                elif _global_parallel_strategy == "mp_pp":
+                    output, new_cache = auto.shard_op(
+                        mod,
+                        dist_attr={
+                            "process_mesh": MPPP_MESH_LIST[mod.mesh_idx]
+                        })(output, memory, tgt_mask, use_cache, cache)
+                    auto.shard_tensor(
+                        output,
+                        dist_attr={
+                            "process_mesh": MPPP_MESH_LIST[mod.mesh_idx],
+                            "dims_mapping":
+                            [-1] + [-1 for i in range(len(output.shape) - 1)]
+                        })
+                elif _global_parallel_strategy == "dp_mp_pp":
+                    output, new_cache = auto.shard_op(
+                        mod,
+                        dist_attr={
+                            "process_mesh": DPMPPP_MESH_LIST[mod.mesh_idx]
+                        })(output, memory, tgt_mask, use_cache, cache)
+                    auto.shard_tensor(
+                        output,
+                        dist_attr={
+                            "process_mesh": DPMPPP_MESH_LIST[mod.mesh_idx],
+                            "dims_mapping":
+                            [0] + [-1 for i in range(len(output.shape) - 1)]
+                        })
+                else:
+                    output, new_cache = mod(output,
+                                            memory,
+                                            tgt_mask=tgt_mask,
+                                            use_cache=use_cache,
+                                            cache=cache[i])
+                new_caches.append(new_cache)
+            self.checkpoints.append(output.name)
+        if self.norm is not None:
+            output = self.norm(output)
+        return output if use_cache is False else (output, new_caches)
+
+    def gen_cache(self, memory, do_zip=False):
+        """
+        Generates cache for `forward` usage. The generated cache is a list, and
+        each element in it is a tuple( :code:`(incremental_cache, static_cache)` )
+        produced by `TransformerDecoderLayer.gen_cache`. See `TransformerDecoderLayer.gen_cache`
+        for more details. If `do_zip` is True, apply `zip` on these tuples to get
+        a list with two elements.
+       """
+        cache = [layer.gen_cache(memory) for layer in self.layers]
+        if do_zip:
+            cache = list(zip(*cache))
+        return cache
+
+
+class TransformerDecoderLayer(nn.Layer):
+    """
+    The transformer decoder layer.
+    It contains multiheadattention and some linear layers.
+    """
+
+    def __init__(self,
+                 d_model,
+                 nhead,
+                 dim_feedforward,
+                 dropout=0.1,
+                 activation="gelu",
+                 attn_dropout=None,
+                 act_dropout=None,
+                 normalize_before=True,
+                 weight_attr=None,
+                 bias_attr=None,
+                 mesh_idx=None):
+        self._config = locals()
+        self._config.pop("self")
+        self._config.pop("__class__", None)  # py3
+        self.mesh_idx = mesh_idx
+        super(TransformerDecoderLayer, self).__init__()
+        attn_dropout = dropout if attn_dropout is None else attn_dropout
+        act_dropout = dropout if act_dropout is None else act_dropout
+        self.normalize_before = normalize_before
+        weight_attrs = _convert_param_attr_to_list(weight_attr, 3)
+        bias_attrs = _convert_param_attr_to_list(bias_attr, 3)
+        self.self_attn = MultiHeadAttention(
+            d_model,
+            nhead,
+            dropout=attn_dropout,
+            weight_attr=weight_attrs[0],
+            bias_attr=bias_attrs[0],
+            mesh_idx=self.mesh_idx)
+        self.linear1 = nn.Linear(
+            d_model, dim_feedforward, weight_attrs[2], bias_attr=bias_attrs[2])
+        self.linear2 = nn.Linear(
+            dim_feedforward, d_model, weight_attrs[2], bias_attr=bias_attrs[2])
+        self.norm1 = nn.LayerNorm(d_model, epsilon=1e-5)
+        self.norm2 = nn.LayerNorm(d_model, epsilon=1e-5)
+        self.dropout1 = nn.Dropout(dropout, mode="upscale_in_train")
+        self.dropout2 = nn.Dropout(act_dropout, mode="upscale_in_train")
+        self.activation = getattr(F, activation)
+
+    def forward(self, tgt, memory, tgt_mask=None, use_cache=False, cache=None):
+        residual = tgt
+        if self.normalize_before:
+            tgt = self.norm1(tgt)
+        if use_cache is False:
+            tgt = self.self_attn(tgt, tgt, tgt, tgt_mask, use_cache, cache)
+        else:
+            tgt, incremental_cache = self.self_attn(tgt, tgt, tgt, tgt_mask,
+                                                    use_cache, cache)
+        tgt = residual + self.dropout1(tgt)
+        if not self.normalize_before:
+            tgt = self.norm1(tgt)
+        residual = tgt
+        if self.normalize_before:
+            tgt = self.norm2(tgt)
+        if _global_parallel_strategy == "mp":
+            auto.shard_tensor(
+                self.linear1.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 0]
+                })
+        elif _global_parallel_strategy == "dp_mp":
+            auto.shard_tensor(
+                self.linear1.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [-1, 1]
+                })
+        elif _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                self.linear1.weight,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 0]
+                })
+        if _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                self.linear1.weight,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [-1, 1]
+                })
+        if _global_parallel_strategy == "mp":
+            auto.shard_tensor(
+                self.linear2.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [0, -1]
+                })
+        elif _global_parallel_strategy == "dp_mp":
+            auto.shard_tensor(
+                self.linear2.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [1, -1]
+                })
+        elif _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                self.linear2.weight,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [0, -1]
+                })
+        elif _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                self.linear2.weight,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[self.mesh_idx],
+                    "dims_mapping": [1, -1]
+                })
+        tgt = self.dropout2(
+            self.linear2(F.gelu(
+                self.linear1(tgt), approximate=True)))
+        tgt = residual + tgt
+        if not self.normalize_before:
+            tgt = self.norm2(tgt)
+        return tgt if use_cache is False else (tgt, incremental_cache)
+
+    def gen_cache(self, memory):
+        incremental_cache = self.self_attn.gen_cache(
+            memory, type=self.self_attn.Cache)
+        return incremental_cache
+
+
+class GPTEmbeddings(nn.Layer):
+    """
+    Include embeddings from word, position and token_type embeddings
+    """
+
+    def __init__(self,
+                 vocab_size,
+                 hidden_size=768,
+                 hidden_dropout_prob=0.1,
+                 max_position_embeddings=512,
+                 type_vocab_size=16,
+                 initializer_range=0.02):
+        super(GPTEmbeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(
+            vocab_size,
+            hidden_size,
+            weight_attr=paddle.ParamAttr(
+                name="word_embeddings",
+                initializer=nn.initializer.Normal(
+                    mean=0.0, std=initializer_range)))
+        self.position_embeddings = nn.Embedding(
+            max_position_embeddings,
+            hidden_size,
+            weight_attr=paddle.ParamAttr(
+                name="pos_embeddings",
+                initializer=nn.initializer.Normal(
+                    mean=0.0, std=initializer_range)))
+        self.dropout = nn.Dropout(hidden_dropout_prob)
+
+    def forward(self, input_ids, position_ids=None):
+        if position_ids is None:
+            ones = paddle.ones_like(input_ids, dtype="int64")
+            seq_length = paddle.cumsum(ones, axis=-1)
+            position_ids = seq_length - ones
+        input_embedings = self.word_embeddings(input_ids)
+        if _global_parallel_strategy == "mp":
+            auto.shard_tensor(
+                self.word_embeddings.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [0, -1]
+                })
+        elif _global_parallel_strategy == "dp_mp":
+            auto.shard_tensor(
+                self.word_embeddings.weight,
+                dist_attr={
+                    "process_mesh": _global_process_mesh,
+                    "dims_mapping": [1, -1]
+                })
+        elif _global_parallel_strategy == "mp_pp":
+            auto.shard_tensor(
+                self.word_embeddings.weight,
+                dist_attr={
+                    "process_mesh": MPPP_MESH_LIST[0],
+                    "dims_mapping": [0, -1]
+                })
+        elif _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                self.word_embeddings.weight,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[0],
+                    "dims_mapping": [1, -1]
+                })
+        position_embeddings = self.position_embeddings(position_ids)
+        embeddings = input_embedings + position_embeddings
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class GPTModel(nn.Layer):
+    """
+    The base model of gpt.
+    """
+
+    def __init__(self,
+                 vocab_size=50304,
+                 hidden_size=1024,
+                 num_hidden_layers=24,
+                 num_attention_heads=16,
+                 intermediate_size=4096,
+                 hidden_act="gelu",
+                 hidden_dropout_prob=0.,
+                 attention_probs_dropout_prob=0.,
+                 max_position_embeddings=512,
+                 type_vocab_size=16,
+                 initializer_range=0.02,
+                 pad_token_id=0,
+                 eos_token_id=7,
+                 bos_token_id=0,
+                 eol_token_id=3,
+                 pp_degree=None):
+        super(GPTModel, self).__init__()
+        self.pad_token_id = pad_token_id
+        self.initializer_range = initializer_range
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        self.layer_per_stage = None
+        self.pipline_mode = (pp_degree is not None and pp_degree > 1)
+        if self.pipline_mode:
+            self.layer_per_stage = num_hidden_layers // pp_degree
+        self.embeddings = GPTEmbeddings(
+            vocab_size, hidden_size, hidden_dropout_prob,
+            max_position_embeddings, type_vocab_size, self.initializer_range)
+        decoder_layers = nn.LayerList()
+        for i in range(num_hidden_layers):
+            mesh_index = None
+            DecoderLayer = TransformerDecoderLayer
+            if self.layer_per_stage is not None:
+                mesh_index = i // self.layer_per_stage
+            decoder_layers.append(
+                DecoderLayer(
+                    d_model=hidden_size,
+                    nhead=num_attention_heads,
+                    dim_feedforward=intermediate_size,
+                    dropout=hidden_dropout_prob,
+                    activation=hidden_act,
+                    attn_dropout=attention_probs_dropout_prob,
+                    act_dropout=hidden_dropout_prob,
+                    weight_attr=paddle.ParamAttr(
+                        initializer=nn.initializer.Normal(
+                            mean=0.0, std=self.initializer_range)),
+                    bias_attr=None,
+                    mesh_idx=mesh_index))
+        Decoder = TransformerDecoder
+        self.decoder = Decoder(
+            decoder_layers,
+            num_hidden_layers,
+            norm="LayerNorm",
+            hidden_size=hidden_size)
+        self.checkpoints = []
+
+    def forward(self,
+                input_ids,
+                position_ids=None,
+                attention_mask=None,
+                use_cache=False,
+                cache=None):
+        self.checkpoints = []
+        if position_ids is None:
+            past_length = 0
+            if cache is not None:
+                past_length = paddle.shape(cache[0].k)[-2]
+            position_ids = paddle.arange(
+                past_length,
+                paddle.shape(input_ids)[-1] + past_length,
+                dtype='int64')
+            position_ids = position_ids.unsqueeze(0)
+            position_ids = paddle.fluid.layers.expand_as(position_ids,
+                                                         input_ids)
+        embedding_output = self.embeddings(
+            input_ids=input_ids, position_ids=position_ids)
+        if _global_parallel_strategy == "pp":
+            auto.shard_tensor(
+                input_ids,
+                dist_attr={
+                    "process_mesh": PP_MESH_LIST[0],
+                    "dims_mapping": [-1 for i in range(len(input_ids.shape))]
+                })
+        if _global_parallel_strategy == "dp_pp":
+            auto.shard_tensor(
+                input_ids,
+                dist_attr={
+                    "process_mesh": DPPP_MESH_LIST[0],
+                    "dims_mapping":
+                    [0] + [-1 for i in range(len(input_ids.shape) - 1)]
+                })
+        if _global_parallel_strategy == "dp_mp_pp":
+            auto.shard_tensor(
+                input_ids,
+                dist_attr={
+                    "process_mesh": DPMPPP_MESH_LIST[0],
+                    "dims_mapping":
+                    [0] + [-1 for i in range(len(input_ids.shape) - 1)]
+                })
+        attention_mask.stop_gradient = True
+        encoder_outputs = self.decoder(
+            embedding_output,
+            memory=None,
+            tgt_mask=attention_mask,
+            use_cache=use_cache,
+            cache=cache)
+        self.checkpoints.extend(self.decoder.checkpoints)
+        return encoder_outputs
+
+
+class GPTForPretraining(nn.Layer):
+    """
+    The pretraining model of GPT.
+    It returns some logits and cached_kvs.
+    """
+
+    def __init__(
+            self,
+            gpt,
+            vocab_size=50304,
+            hidden_size=768,
+            initializer_range=0.02, ):
+        super(GPTForPretraining, self).__init__()
+        self.output_embeddings = nn.Embedding(
+            vocab_size,
+            hidden_size,
+            weight_attr=paddle.ParamAttr(
+                name="output_embeddings",
+                initializer=nn.initializer.Normal(
+                    mean=0.0, std=initializer_range)))
+        self.gpt = gpt
+
+    def forward(self,
+                input_ids,
+                position_ids=None,
+                attention_mask=None,
+                masked_positions=None,
+                use_cache=False,
+                cache=None):
+        outputs = self.gpt(input_ids,
+                           position_ids=position_ids,
+                           attention_mask=attention_mask,
+                           use_cache=use_cache,
+                           cache=cache)
+        if use_cache:
+            encoder_outputs, cached_kvs = outputs[:2]
+        else:
+            encoder_outputs = outputs
+        logits = paddle.matmul(
+            encoder_outputs, self.output_embeddings.weight, transpose_y=True)
+        if use_cache:
+            return logits, cached_kvs
+        else:
+            return logits
+
+
+class GPTPretrainingCriterion(nn.Layer):
+    """
+    Criterion for GPT.
+    It calculates the final loss.
+    """
+
+    def __init__(self):
+        super(GPTPretrainingCriterion, self).__init__()
+        self.loss_func = paddle.nn.CrossEntropyLoss(reduction="none")
+
+    def forward(self, prediction_scores, masked_lm_labels, loss_mask):
+        masked_lm_loss = self.loss_func(prediction_scores,
+                                        masked_lm_labels.unsqueeze(2))
+        loss_mask = loss_mask.reshape([-1])
+        masked_lm_loss = paddle.sum(masked_lm_loss.reshape([-1]) * loss_mask)
+        total_loss = masked_lm_loss / loss_mask.sum()
+        pp_total_loss = None
+        loss = total_loss
+        if "pp" in _global_parallel_strategy:
+            total_loss = total_loss
+            masked_lm_loss.persistable = True
+            total_loss.persistable = True
+            total_loss.persistable = True
+            pp_total_loss = paddle.fluid.layers.fill_constant([1, ], "float32",
+                                                              0.0)
+            pp_total_loss.persistable = True
+            block = paddle.static.default_main_program().global_block()
+            acc_steps = 1
+            tmp = total_loss / acc_steps
+            block.append_op(
+                type="elementwise_add",
+                inputs={"X": [pp_total_loss],
+                        "Y": [tmp]},
+                outputs={"Out": [pp_total_loss]})
+            loss = pp_total_loss
+        return loss