def __init__(self, num_tokentypes=0, parallel_output=True, topology=None): args = get_args() self.parallel_output = parallel_output self.hidden_size = args.hidden_size self.num_tokentypes = num_tokentypes self.init_method = init_method_normal(args.init_method_std) self.output_layer_init_method = scaled_init_method_normal(args.init_method_std, args.num_layers) weight_tying = not args.no_weight_tying if args.pos_emb == 'rpe': rpe_emb = ParallelRelativePositionBias(causal=True, num_buckets=args.rpe_num_buckets, max_distance=args.rpe_max_distance, heads=args.num_attention_heads) self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy # # forward() prototype # self.specs = [] # Embedding layer if weight_tying: self.specs.append(TiedLayerSpec('embed', EmbeddingPipe, self.hidden_size, args.padded_vocab_size, args.max_position_embeddings, args.hidden_dropout, self.init_method, self.num_tokentypes, tied_weight_attr='word_embeddings_weight')) else: self.specs.append(LayerSpec(EmbeddingPipe, self.hidden_size, args.padded_vocab_size, args.max_position_embeddings, args.hidden_dropout, self.init_method, self.num_tokentypes)) # outputs are now (hidden_states, attention_mask) # data format change to avoid explicit tranposes : [b s h] --> [s b h] self.specs.append(lambda x: (x[0].transpose(0, 1).contiguous(), *x[1:])) # Transformer layers for x in range(args.num_layers): if args.sparsity == 'none': sparse = False elif args.sparsity == 'all': sparse = True elif args.sparsity == 'interspersed': sparse = not x % 2 == 0 self.specs.append( LayerSpec(ParallelTransformerLayerPipe, attention_mask_func=gpt2_attention_mask_func, init_method=self.init_method, output_layer_init_method=self.output_layer_init_method, layer_number=x, sparse=sparse, rpe=rpe_emb if args.pos_emb == 'rpe' else None, rotary=args.pos_emb == 'rotary')) # Undo data format change and drop mask self.specs.append(lambda x: x[0].transpose(0, 1).contiguous()) # Final layernorm after transformer layers if args.norm == "rmsnorm": norm = RMSNorm eps = args.rms_norm_epsilon elif args.norm == "layernorm": eps = args.layernorm_epsilon norm = LayerNorm elif args.norm == "scalenorm": eps = args.scalenorm_epsilon norm = ScaleNorm self.specs.append( LayerSpec(norm, args.hidden_size, eps=eps)) # XXX forward_method_parallel_output is assumed to be None, but we're not in a # fwd method to assert def _logits_helper(embedding, lm_output): """Just a wrapper to massage inputs/outputs from pipeline. """ return parallel_lm_logits( lm_output, embedding.word_embeddings_weight, self.parallel_output) if weight_tying: self.specs.append( TiedLayerSpec('embed', EmbeddingPipe, self.hidden_size, args.padded_vocab_size, args.max_position_embeddings, args.hidden_dropout, self.init_method, self.num_tokentypes, forward_fn=_logits_helper, tied_weight_attr='word_embeddings_weight') ) else: # TODO: not sure whether to use RowParallelLinear's default scatter to mp region here, or copy, which is # the default of parallel_lm_logits. Should investigate benefits of both self.specs.append( LayerSpec( mpu.RowParallelLinear, args.hidden_size, args.padded_vocab_size, bias=False, input_is_parallel=False, parallel_output=self.parallel_output, skip_bias_add=False ) ) self.specs.append(lambda x: x[0]) # drop bias loss_fn = partial(cross_entropy, _fp16=self.fp16_lm_cross_entropy) if args.checkpoint_activations: interval = args.checkpoint_num_layers else: interval = 0 super().__init__(layers=self.specs, loss_fn=loss_fn, topology=topology, activation_checkpoint_interval=interval, partition_method=args.pipe_partition_method) # 'type:transformer' / 'parameters'
def init_specs(self): weight_tying = not self.neox_args.no_weight_tying self.specs = [] # Embedding layer # input will be (input_ids, position_ids, attention_mask) if weight_tying: self.specs.append( TiedLayerSpec( "embed", EmbeddingPipe, self.neox_args, self.hidden_size, self.neox_args.padded_vocab_size, self.neox_args.max_position_embeddings, self.neox_args.hidden_dropout, self.init_method, self.num_tokentypes, tied_weight_attr="word_embeddings_weight", )) else: self.specs.append( LayerSpec( EmbeddingPipe, self.neox_args, self.hidden_size, self.neox_args.padded_vocab_size, self.neox_args.max_position_embeddings, self.neox_args.hidden_dropout, self.init_method, self.num_tokentypes, )) # NB: the attention mask always needs to be the *last* item in the args when being passed from # one stage to the next, because deepspeed is hacks on top of hacks. # # outputs are now (hidden_states, attention_mask) self.specs.append(_pre_transformer_block) # T5 RPE positional embedding if self.neox_args.pos_emb == "rpe": hidden_size_per_attention_head = mpu.divide( self.neox_args.hidden_size, self.neox_args.num_attention_heads) rpe_scale = math.sqrt(hidden_size_per_attention_head) rpe_emb = ParallelRelativePositionBias( neox_args=self.neox_args, scale=rpe_scale, causal=True, num_buckets=self.neox_args.rpe_num_buckets, max_distance=self.neox_args.rpe_max_distance, heads=self.neox_args.num_attention_heads, ) # Transformer layers for i in range(self.neox_args.num_layers): layer_type = self.neox_args.attention_config[i] if layer_type in ["gmlp", "amlp"]: self.specs.append( LayerSpec( GMLPBlock, init_method=self.init_method, layer_number=i, output_layer_init_method=self.output_layer_init_method, neox_args=self.neox_args, mask_fn=gpt2_attention_mask_func, )) else: self.specs.append( LayerSpec( ParallelTransformerLayerPipe, neox_args=self.neox_args, attention_mask_func=gpt2_attention_mask_func, init_method=self.init_method, output_layer_init_method=self.output_layer_init_method, layer_number=i, rpe=rpe_emb if self.neox_args.pos_emb == "rpe" else None, rotary=self.neox_args.pos_emb == "rotary", use_cache=self.use_cache, )) # used to drop attention mask + reshape hidden states self.specs.append(_post_transformer_block) # NormPipe is a (deprecated) helper class that used to be used to pass presents along the pipeline - since presents are now cached to the `TransformerLayer` class this is no longer needed norm, eps = get_norm(self.neox_args) self.specs.append( LayerSpec(NormPipe, norm, self.neox_args.hidden_size, eps=eps)) # outputs are now a single tensor: hidden_states def _logits_helper(embedding, lm_output): """Just a wrapper to massage inputs/outputs from pipeline.""" logits = parallel_lm_logits(lm_output, embedding.word_embeddings_weight, self.parallel_output) return logits if weight_tying: self.specs.append( TiedLayerSpec( "embed", EmbeddingPipe, self.neox_args, self.hidden_size, self.neox_args.padded_vocab_size, self.neox_args.max_position_embeddings, self.neox_args.hidden_dropout, self.init_method, self.num_tokentypes, forward_fn=_logits_helper, tied_weight_attr="word_embeddings_weight", )) else: self.specs.append( LayerSpec( ParallelLinearPipe, neox_args=self.neox_args, init_method=self.init_method, parallel_output=self.parallel_output, ))
def __init__(self, attention_mask_func, init_method, output_layer_init_method): super(ParallelTransformer, self).__init__() args = get_args() # Store activation checkpoiting flag. self.checkpoint_activations = args.checkpoint_activations self.checkpoint_num_layers = args.checkpoint_num_layers # Number of layers: self.num_layers = args.num_layers self.num_unique_layers = args.num_unique_layers if self.num_unique_layers is None: self.num_unique_layers = self.num_layers assert self.num_layers % self.num_unique_layers == 0, \ 'number of layers should be divisible by number of unique layers' self.param_sharing_style = args.param_sharing_style if args.pos_emb == 'rpe': rpe_emb = ParallelRelativePositionBias(causal=True, num_buckets=args.rpe_num_buckets, max_distance=args.rpe_max_distance, heads=args.num_attention_heads) # Transformer layers. sparsity = args.sparsity def build_layer(layer_number): if sparsity == 'none': sparse = False elif sparsity == 'all': sparse = True elif sparsity == 'interspersed': sparse = not layer_number % 2 == 0 else: raise ValueError(f'Sparsity type {sparsity} not recognized') return ParallelTransformerLayer( attention_mask_func, init_method, output_layer_init_method, layer_number, sparse=sparse, rpe=rpe_emb if args.pos_emb == 'rpe' else None) self.layers = torch.nn.ModuleList( [build_layer(i + 1) for i in range(self.num_unique_layers)]) # Print layer ordering. if self.num_layers != self.num_unique_layers: if torch.distributed.get_rank() == 0: print('> will be using the following layer ordering:') for i in range(self.num_layers): print(' layer id: {:3d} --> unique layer id: ' '{:3d}'.format(i, self._get_layer_index(i)), flush=True) # Final layer norm before output. if args.norm == "rmsnorm": norm = RMSNorm eps = args.rms_norm_epsilon elif args.norm == "layernorm": eps = args.layernorm_epsilon norm = LayerNorm elif args.norm == "scalenorm": eps = args.scalenorm_epsilon norm = ScaleNorm self.final_layernorm = norm( args.hidden_size, eps=eps) if deepspeed.checkpointing.is_configured(): global get_cuda_rng_tracker, checkpoint get_cuda_rng_tracker = deepspeed.checkpointing.get_cuda_rng_tracker checkpoint = deepspeed.checkpointing.checkpoint
def init_specs(self): weight_tying = not self.neox_args.no_weight_tying if self.embedding_type == 'rpe': rpe_emb = ParallelRelativePositionBias( neox_args=self.neox_args, causal=True, num_buckets=self.neox_args.rpe_num_buckets, max_distance=self.neox_args.rpe_max_distance, heads=self.neox_args.num_attention_heads) self.specs = [] # Embedding layer # input will be (input_ids, position_ids, attention_mask) in Training # and (input_ids, position_ids, attention_mask, layer_past) in Inference if weight_tying: self.specs.append( TiedLayerSpec('embed', EmbeddingPipe, self.neox_args, self.hidden_size, self.neox_args.padded_vocab_size, self.neox_args.max_position_embeddings, self.neox_args.hidden_dropout, self.init_method, self.num_tokentypes, tied_weight_attr='word_embeddings_weight')) else: self.specs.append( LayerSpec(EmbeddingPipe, self.neox_args, self.hidden_size, self.neox_args.padded_vocab_size, self.neox_args.max_position_embeddings, self.neox_args.hidden_dropout, self.init_method, self.num_tokentypes)) # NB: in inference, the attention mask always needs to be the *last* item in the args when being passed from # one stage to the next, because deepspeed is hacks on top of hacks. # # outputs are now # Train: (hidden_states, attention_mask) # Inference: (hidden_states, layer_past, attention_mask) self.specs.append(_pre_transformer_block) # Transformer layers for i in range(self.neox_args.num_layers): layer_type = self.neox_args.attention_config[i] if layer_type in ["gmlp", "amlp"]: self.specs.append( LayerSpec( GMLPBlock, init_method=self.init_method, layer_number=i, output_layer_init_method=self.output_layer_init_method, neox_args=self.neox_args, mask_fn=gpt2_attention_mask_func)) else: self.specs.append( LayerSpec( ParallelTransformerLayerPipe, neox_args=self.neox_args, attention_mask_func=gpt2_attention_mask_func, init_method=self.init_method, output_layer_init_method=self.output_layer_init_method, layer_number=i, rpe=rpe_emb if self.neox_args.pos_emb == 'rpe' else None, rotary=self.neox_args.pos_emb == 'rotary', get_key_value=self.get_key_value)) self.specs.append(_post_transformer_block) # NormPipe is a helper class to pass presents through to the output when doing inference norm, eps = get_norm(self.neox_args) self.specs.append( LayerSpec(NormPipe, norm, self.neox_args.hidden_size, eps=eps)) # outputs are now # Train: hidden_states # Inference: (hidden_states, presents) def _logits_helper(embedding, lm_output): """Just a wrapper to massage inputs/outputs from pipeline. """ if self._inference and len(lm_output) == 2: hidden_states, presents = lm_output logits = parallel_lm_logits(hidden_states, embedding.word_embeddings_weight, self.parallel_output) return logits, presents else: logits = parallel_lm_logits(lm_output, embedding.word_embeddings_weight, self.parallel_output) return logits if weight_tying: self.specs.append( TiedLayerSpec('embed', EmbeddingPipe, self.neox_args, self.hidden_size, self.neox_args.padded_vocab_size, self.neox_args.max_position_embeddings, self.neox_args.hidden_dropout, self.init_method, self.num_tokentypes, forward_fn=_logits_helper, tied_weight_attr='word_embeddings_weight')) else: self.specs.append( LayerSpec(ParallelLinearPipe, neox_args=self.neox_args, init_method=self.init_method, parallel_output=self.parallel_output))