# 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. import collections import paddle import paddle.nn as nn import paddle.nn.functional as F import paddle.tensor as tensor from paddle.distributed.fleet import auto from paddle.fluid import layers from paddle.nn.layer.transformer import _convert_param_attr_to_list paddle.enable_static() def init_global(): global _global_parallel_strategy _global_parallel_strategy = None 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, _global_process_mesh, [None, "x"]) elif _global_parallel_strategy == "dp_mp": auto.shard_tensor(self.q_proj.weight, _global_process_mesh, [None, "y"]) elif _global_parallel_strategy == "mp_pp": auto.shard_tensor(self.q_proj.weight, MPPP_MESH_LIST[self.mesh_idx], [None, "x"]) elif _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(self.q_proj.weight, DPMPPP_MESH_LIST[self.mesh_idx], [None, "y"]) 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, _global_process_mesh, [None, "x"]) elif _global_parallel_strategy == "dp_mp": auto.shard_tensor(self.k_proj.weight, _global_process_mesh, [None, "y"]) elif _global_parallel_strategy == "mp_pp": auto.shard_tensor(self.k_proj.weight, MPPP_MESH_LIST[self.mesh_idx], [None, "x"]) elif _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(self.k_proj.weight, DPMPPP_MESH_LIST[self.mesh_idx], [None, "y"]) v = self.v_proj(value) if _global_parallel_strategy == "mp": auto.shard_tensor(self.v_proj.weight, _global_process_mesh, [None, "x"]) elif _global_parallel_strategy == "dp_mp": auto.shard_tensor(self.v_proj.weight, _global_process_mesh, [None, "y"]) elif _global_parallel_strategy == "mp_pp": auto.shard_tensor(self.v_proj.weight, MPPP_MESH_LIST[self.mesh_idx], [None, "x"]) elif _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(self.v_proj.weight, DPMPPP_MESH_LIST[self.mesh_idx], [None, "y"]) 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, _global_process_mesh, ["x", None]) elif _global_parallel_strategy == "dp_mp": auto.shard_tensor(self.out_proj.weight, _global_process_mesh, ["y", None]) elif _global_parallel_strategy == "mp_pp": auto.shard_tensor(self.out_proj.weight, MPPP_MESH_LIST[self.mesh_idx], ["x", None]) elif _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(self.out_proj.weight, DPMPPP_MESH_LIST[self.mesh_idx], ["y", None]) 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 == "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, PP_MESH_LIST[0], [None for i in range(len(output.shape))]) if _global_parallel_strategy == "dp_pp": auto.shard_tensor(output, DPPP_MESH_LIST[0], ["x"].extends( [None for i in range(len(output.shape) - 1)])) if _global_parallel_strategy == "mp_pp": auto.shard_tensor(output, MPPP_MESH_LIST[0], [None for i in range(len(output.shape))]) if _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(output, DPMPPP_MESH_LIST[0], ["x"].extends( [None 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, PP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, PP_MESH_LIST[mod.mesh_idx], [None for i in range(len(output.shape))]) elif _global_parallel_strategy == "dp_pp": output, new_cache = auto.shard_op( mod, DPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, DPPP_MESH_LIST[mod.mesh_idx], ["x"].extends( [None for i in range(len(output.shape) - 1)])) elif _global_parallel_strategy == "mp_pp": output, new_cache = auto.shard_op( mod, MPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, MPPP_MESH_LIST[mod.mesh_idx], [None for i in range(len(output.shape))]) elif _global_parallel_strategy == "dp_mp_pp": output, new_cache = auto.shard_op( mod, DPMPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, DPMPPP_MESH_LIST[mod.mesh_idx], [None for i in range(len(output.shape))]) 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, PP_MESH_LIST[mod.mesh_idx])( output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, PP_MESH_LIST[mod.mesh_idx], [None for i in range(len(output.shape))]) elif _global_parallel_strategy == "dp_pp": output = auto.shard_op( mod, DPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, DPPP_MESH_LIST[mod.mesh_idx], ["x"].extends( [None for i in range(len(output.shape) - 1)])) elif _global_parallel_strategy == "mp_pp": output = auto.shard_op( mod, MPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, MPPP_MESH_LIST[mod.mesh_idx], [None for i in range(len(output.shape))]) elif _global_parallel_strategy == "dp_mp_pp": output = auto.shard_op(mod, DPMPPP_MESH_LIST[mod.mesh_idx])( output, memory, tgt_mask, use_cache, cache) auto.shard_tensor( output, DPMPPP_MESH_LIST[mod.mesh_idx], ["x"].extends( [None 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, PP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor(output, PP_MESH_LIST[mod.mesh_idx], [None for i in range(len(output.shape))]) elif _global_parallel_strategy == "dp_pp": output, new_cache = auto.shard_op( mod, DPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor(output, DPPP_MESH_LIST[mod.mesh_idx], [ "x" ].extends([None for i in range(len(output.shape) - 1)])) elif _global_parallel_strategy == "mp_pp": output, new_cache = auto.shard_op( mod, MPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor(output, MPPP_MESH_LIST[mod.mesh_idx], [None for i in range(len(output.shape))]) elif _global_parallel_strategy == "dp_mp_pp": output, new_cache = auto.shard_op( mod, DPMPPP_MESH_LIST[mod.mesh_idx])(output, memory, tgt_mask, use_cache, cache) auto.shard_tensor(output, DPMPPP_MESH_LIST[mod.mesh_idx], [ "x" ].extends([None 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, _global_process_mesh, [None, "x"]) elif _global_parallel_strategy == "dp_mp": auto.shard_tensor(self.linear1.weight, _global_process_mesh, [None, "y"]) elif _global_parallel_strategy == "mp_pp": auto.shard_tensor(self.linear1.weight, MPPP_MESH_LIST[self.mesh_idx], [None, "x"]) if _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(self.linear1.weight, DPMPPP_MESH_LIST[self.mesh_idx], [None, "y"]) if _global_parallel_strategy == "mp": auto.shard_tensor(self.linear2.weight, _global_process_mesh, ["x", None]) elif _global_parallel_strategy == "dp_mp": auto.shard_tensor(self.linear2.weight, _global_process_mesh, ["y", None]) elif _global_parallel_strategy == "mp_pp": auto.shard_tensor(self.linear2.weight, MPPP_MESH_LIST[self.mesh_idx], ["x", None]) elif _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(self.linear2.weight, DPMPPP_MESH_LIST[self.mesh_idx], ["y", None]) 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, _global_process_mesh, ["x", None]) elif _global_parallel_strategy == "dp_mp": auto.shard_tensor(self.word_embeddings.weight, _global_process_mesh, ["y", None]) elif _global_parallel_strategy == "mp_pp": auto.shard_tensor(self.word_embeddings.weight, MPPP_MESH_LIST[0], ["x", None]) elif _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(self.word_embeddings.weight, DPMPPP_MESH_LIST[0], ["y", None]) 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, PP_MESH_LIST[0], [None for i in range(len(input_ids.shape))]) if _global_parallel_strategy == "dp_pp": auto.shard_tensor(input_ids, DPPP_MESH_LIST[0], ["x"].extends( [None for i in range(len(input_ids.shape) - 1)])) if _global_parallel_strategy == "dp_mp_pp": auto.shard_tensor(input_ids, DPMPPP_MESH_LIST[0], ["x"].extends( [None for i in range(len(input_ids.shape) - 1)])) 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.gpt = gpt def forward(self, input_ids, position_ids=None, attention_mask=None, masked_positions=None, use_cache=False, cache=None): input_ids.stop_gradient = True position_ids.stop_gradient = True attention_mask.stop_gradient = True 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 x = encoder_outputs w = self.gpt.embeddings.word_embeddings.weight mesh = None if _global_parallel_strategy == "pp": mesh = PP_MESH_LIST[-1] x_dims_mapping = [None for i in range(len(x.shape))] w_dims_mapping = [None for i in range(len(w.shape))] elif _global_parallel_strategy == "dp": mesh = _global_process_mesh x_dims_mapping = ["x"] + [None for i in range(len(x.shape) - 1)] w_dims_mapping = [None for i in range(len(w.shape))] elif _global_parallel_strategy == "mp": mesh = _global_process_mesh x_dims_mapping = [None for i in range(len(x.shape))] w_dims_mapping = ["x"] + [None for i in range(len(w.shape) - 1)] elif _global_parallel_strategy == "dp_mp": mesh = _global_process_mesh x_dims_mapping = ["x"] + [None for i in range(len(x.shape) - 1)] w_dims_mapping = ["y"] + [None for i in range(len(w.shape) - 1)] elif _global_parallel_strategy == "dp_pp": mesh = DPPP_MESH_LIST[-1] x_dims_mapping = ["x"] + [None for i in range(len(x.shape) - 1)] w_dims_mapping = [None for i in range(len(w.shape))] elif _global_parallel_strategy == "mp_pp": mesh = MPPP_MESH_LIST[-1] x_dims_mapping = [None for i in range(len(x.shape))] w_dims_mapping = ["x"] + [-1 for i in range(len(w.shape) - 1)] elif _global_parallel_strategy == "dp_mp_pp": mesh = DPMPPP_MESH_LIST[-1] x_dims_mapping = ["x"] + [None for i in range(len(x.shape) - 1)] w_dims_mapping = ["y"] + [None for i in range(len(w.shape) - 1)] if mesh: matmul = auto.shard_op(paddle.matmul, mesh, [x_dims_mapping, w_dims_mapping, None]) logits = matmul(x, w, transpose_y=True) else: logits = paddle.matmul(x, w, 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_labels.stop_gradient = True loss_mask.stop_gradient = True mesh = None if _global_parallel_strategy == "dp": mesh = _global_process_mesh dims_mapping = ["x" ] + [None for i in range(len(loss_mask.shape) - 1)] elif _global_parallel_strategy == "dp_mp": mesh = _global_process_mesh dims_mapping = ["x" ] + [None for i in range(len(loss_mask.shape) - 1)] elif _global_parallel_strategy == "dp_pp": mesh = DPPP_MESH_LIST[-1] dims_mapping = ["x" ] + [None for i in range(len(loss_mask.shape) - 1)] elif _global_parallel_strategy == "dp_mp_pp": mesh = DPMPPP_MESH_LIST[-1] dims_mapping = ["x" ] + [None for i in range(len(loss_mask.shape) - 1)] if mesh: auto.shard_tensor(loss_mask, mesh, dims_mapping) 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() return total_loss