# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. 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. """Transformer.""" import math from contextlib import nullcontext import torch import torch.nn.functional as F from megatron import get_timers, get_args, get_global_memory_buffer from megatron import mpu from .module import MegatronModule from megatron.model.enums import AttnMaskType, ModelType, LayerType, AttnType from megatron.model import LayerNorm from megatron.model.fused_softmax import FusedScaleMaskSoftmax from megatron.model.fused_bias_gelu import bias_gelu_impl from megatron.model.utils import attention_mask_func, openai_gelu, erf_gelu """ We use the following notation throughout this file: h: hidden size n: number of attention heads p: number of model parallel partitions np: n/p hp: h/p hn: h/n b: batch size s: sequence length l: number of layers Transformer takes input of size [s, b, h] and returns a tensor of the same size. We use the following arguments: hyperparameters: transformer hyperparameters """ class DropPath(MegatronModule): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob=0.): super(DropPath, self).__init__() self.drop_prob = drop_prob def forward(self, hidden_state): if self.drop_prob == 0. or not self.training: return hidden_state keep_prob = 1 - self.drop_prob # work with diff dim tensors, not just 2D ConvNets shape = (hidden_state.shape[0],) + (1,) * (hidden_state.ndim - 1) random_tensor = keep_prob + \ torch.rand(shape, dtype=hidden_state.dtype, device=hidden_state.device) random_tensor.floor_() # binarize output = hidden_state.div(keep_prob) * random_tensor return output class ParallelMLP(MegatronModule): """MLP. MLP will take the input with h hidden state, project it to 4*h hidden dimension, perform nonlinear transformation, and project the state back into h hidden dimension. """ def __init__(self, init_method, output_layer_init_method): super(ParallelMLP, self).__init__() args = get_args() # Project to 4h. self.dense_h_to_4h = mpu.ColumnParallelLinear( args.hidden_size, args.ffn_hidden_size, gather_output=False, init_method=init_method, skip_bias_add=True) self.bias_gelu_fusion = args.bias_gelu_fusion self.activation_func = F.gelu if args.openai_gelu: self.activation_func = openai_gelu elif args.onnx_safe: self.activation_func = erf_gelu # Project back to h. self.dense_4h_to_h = mpu.RowParallelLinear( args.ffn_hidden_size, args.hidden_size, input_is_parallel=True, init_method=output_layer_init_method, skip_bias_add=True) def forward(self, hidden_states): # [s, b, 4hp] intermediate_parallel, bias_parallel = self.dense_h_to_4h(hidden_states) if self.bias_gelu_fusion: intermediate_parallel = \ bias_gelu_impl(intermediate_parallel, bias_parallel) else: intermediate_parallel = \ self.activation_func(intermediate_parallel + bias_parallel) # [s, b, h] output, output_bias = self.dense_4h_to_h(intermediate_parallel) return output, output_bias class SwitchMLP(MegatronModule): """ Routes input to one of N MLP "experts" """ def __init__(self, init_method, output_layer_init_method): super(SwitchMLP, self).__init__() args = get_args() self.router = torch.nn.Linear(args.hidden_size, args.num_experts) self.experts = torch.nn.ModuleList() for i in range(args.num_experts): self.experts.append(ParallelMLP(init_method, output_layer_init_method)) def forward(self, hidden_states): # hidden_states: [s, b, h] s = hidden_states.size(0) b = hidden_states.size(1) h = hidden_states.size(2) route = self.router(hidden_states) route = torch.nn.functional.softmax(route, dim=2) max_prob, max_ind = torch.max(route, dim=2) max_prob = torch.unsqueeze(max_prob, 2) # [s b 1] # TODO (rprenger) TODO this could be made easier to read # Converting [s, b, h] to [s*b, h]. # Each vector could be routed differently hidden_states = hidden_states.view(-1, hidden_states.size(2)) # [s*b h] max_prob = max_prob.view(-1, max_prob.size(2)) # [s*b 1] max_ind = max_ind.view(-1) # [s*b] output_total = torch.empty_like(hidden_states) output_bias_total = torch.empty_like(hidden_states) #TODO (rprenger) This does each expert in serial, but it could be parallelized for expert_num, expert in enumerate(self.experts): local_indices = (max_ind == expert_num).nonzero() hidden = hidden_states[local_indices,:] output, output_bias = expert(hidden) output_bias = output_bias.expand_as(output) output_total[local_indices,:] = output output_bias_total[local_indices,:] = output_bias output_total = output_total*max_prob output_bias_total = output_bias_total*max_prob output_total = output_total.view(s, b, h) output_bias_total = output_bias_total.view(s, b, h) return output_total, output_bias_total class CoreAttention(MegatronModule): def __init__(self, layer_number, attn_mask_type=AttnMaskType.padding): super(CoreAttention, self).__init__() args = get_args() self.fp16 = args.fp16 self.bf16 = args.bf16 self.apply_query_key_layer_scaling = args.apply_query_key_layer_scaling self.attention_softmax_in_fp32 = args.attention_softmax_in_fp32 if self.apply_query_key_layer_scaling: self.attention_softmax_in_fp32 = True self.layer_number = max(1, layer_number) self.attn_mask_type = attn_mask_type self.sequence_parallel = args.sequence_parallel projection_size = args.kv_channels * args.num_attention_heads # Per attention head and per partition values. world_size = mpu.get_tensor_model_parallel_world_size() self.hidden_size_per_partition = mpu.divide(projection_size, world_size) self.hidden_size_per_attention_head = mpu.divide( projection_size, args.num_attention_heads) self.num_attention_heads_per_partition = mpu.divide( args.num_attention_heads, world_size) coeff = None self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) if self.apply_query_key_layer_scaling: coeff = self.layer_number self.norm_factor *= coeff self.scale_mask_softmax = FusedScaleMaskSoftmax( self.fp16, self.bf16, self.attn_mask_type, args.masked_softmax_fusion, attention_mask_func, self.attention_softmax_in_fp32, coeff) # Dropout. Note that for a single iteration, this layer will generate # different outputs on different number of parallel partitions but # on average it should not be partition dependent. self.attention_dropout = torch.nn.Dropout(args.attention_dropout) def forward(self, query_layer, key_layer, value_layer, attention_mask): # =================================== # Raw attention scores. [b, np, s, s] # =================================== # [b, np, sq, sk] output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) # [sq, b, np, hn] -> [sq, b * np, hn] query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) # [sk, b, np, hn] -> [sk, b * np, hn] key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1) # preallocting input tensor: [b * np, sq, sk] matmul_input_buffer = get_global_memory_buffer().get_tensor( (output_size[0]*output_size[1], output_size[2], output_size[3]), query_layer.dtype, "mpu") # Raw attention scores. [b * np, sq, sk] matmul_result = torch.baddbmm( matmul_input_buffer, query_layer.transpose(0, 1), # [b * np, sq, hn] key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk] beta=0.0, alpha=(1.0/self.norm_factor)) # change view to [b, np, sq, sk] attention_scores = matmul_result.view(*output_size) # =========================== # Attention probs and dropout # =========================== # attention scores and attention mask [b, np, sq, sk] attention_probs = self.scale_mask_softmax(attention_scores, attention_mask) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. if not self.sequence_parallel: with mpu.get_cuda_rng_tracker().fork(): attention_probs = self.attention_dropout(attention_probs) else: attention_probs = self.attention_dropout(attention_probs) # ========================= # Context layer. [sq, b, hp] # ========================= # value_layer -> context layer. # [sk, b, np, hn] --> [b, np, sq, hn] # context layer shape: [b, np, sq, hn] output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3)) # change view [sk, b * np, hn] value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1) # change view [b * np, sq, sk] attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) # matmul: [b * np, sq, hn] context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1)) # change view [b, np, sq, hn] context_layer = context_layer.view(*output_size) # [b, np, sq, hn] --> [sq, b, np, hn] context_layer = context_layer.permute(2, 0, 1, 3).contiguous() # [sq, b, np, hn] --> [sq, b, hp] new_context_layer_shape = context_layer.size()[:-2] + \ (self.hidden_size_per_partition,) context_layer = context_layer.view(*new_context_layer_shape) return context_layer class ParallelAttention(MegatronModule): """Parallel self-attention layer abstract class. Self-attention layer takes input with size [s, b, h] and returns output of the same size. """ def __init__(self, init_method, output_layer_init_method, layer_number, attention_type=AttnType.self_attn, attn_mask_type=AttnMaskType.padding): super(ParallelAttention, self).__init__() args = get_args() self.layer_number = max(1, layer_number) self.attention_type = attention_type self.attn_mask_type = attn_mask_type self.params_dtype = args.params_dtype projection_size = args.kv_channels * args.num_attention_heads # Per attention head and per partition values. world_size = mpu.get_tensor_model_parallel_world_size() self.hidden_size_per_attention_head = mpu.divide( projection_size, args.num_attention_heads) self.num_attention_heads_per_partition = mpu.divide( args.num_attention_heads, world_size) # Strided linear layer. if attention_type == AttnType.self_attn: self.query_key_value = mpu.ColumnParallelLinear( args.hidden_size, 3 * projection_size, gather_output=False, init_method=init_method) else: assert attention_type == AttnType.cross_attn self.query = mpu.ColumnParallelLinear( args.hidden_size, projection_size, gather_output=False, init_method=init_method) self.key_value = mpu.ColumnParallelLinear( args.hidden_size, 2 * projection_size, gather_output=False, init_method=init_method) self.core_attention = CoreAttention(self.layer_number, self.attn_mask_type) self.checkpoint_core_attention = args.recompute_granularity == 'selective' # Output. self.dense = mpu.RowParallelLinear( projection_size, args.hidden_size, input_is_parallel=True, init_method=output_layer_init_method, skip_bias_add=True) def _checkpointed_attention_forward(self, query_layer, key_layer, value_layer, attention_mask): """Forward method with activation checkpointing.""" def custom_forward(*inputs): query_layer = inputs[0] key_layer = inputs[1] value_layer = inputs[2] attention_mask = inputs[3] output_ = self.core_attention(query_layer, key_layer, value_layer, attention_mask) return output_ hidden_states = mpu.checkpoint( custom_forward, False, query_layer, key_layer, value_layer, attention_mask) return hidden_states def _allocate_memory(self, inference_max_sequence_len, batch_size): return torch.empty( inference_max_sequence_len, batch_size, self.num_attention_heads_per_partition, self.hidden_size_per_attention_head, dtype=self.params_dtype, device=torch.cuda.current_device()) def forward(self, hidden_states, attention_mask, encoder_output=None, inference_params=None): # hidden_states: [sq, b, h] # ================================================= # Pre-allocate memory for key-values for inference. # ================================================= if inference_params: if self.layer_number not in inference_params.key_value_memory_dict: inf_max_seq_len = inference_params.max_sequence_len inf_max_batch_size = inference_params.max_batch_size inference_key_memory = self._allocate_memory( inf_max_seq_len, inf_max_batch_size) inference_value_memory = self._allocate_memory( inf_max_seq_len, inf_max_batch_size) inference_params.key_value_memory_dict[self.layer_number] = ( inference_key_memory, inference_value_memory) else: inference_key_memory, inference_value_memory = \ inference_params.key_value_memory_dict[self.layer_number] # ===================== # Query, Key, and Value # ===================== if self.attention_type == AttnType.self_attn: # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)] mixed_x_layer, _ = self.query_key_value(hidden_states) # [sq, b, (np * 3 * hn)] --> [sq, b, np, 3 * hn] new_tensor_shape = mixed_x_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, 3 * self.hidden_size_per_attention_head) mixed_x_layer = mixed_x_layer.view(*new_tensor_shape) # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn] (query_layer, key_layer, value_layer) = mpu.split_tensor_along_last_dim(mixed_x_layer, 3) else: # Attention heads [sk, b, h] --> [sk, b, (np * 2 * hn)] mixed_kv_layer, _ = self.key_value(encoder_output) # [sk, b, (np * 2 * hn)] --> [sk, b, np, 2 * hn] new_tensor_shape = mixed_kv_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, 2 * self.hidden_size_per_attention_head) mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape) # [sk, b, np, 2 * hn] --> 2 [sk, b, np, hn] (key_layer, value_layer) = mpu.split_tensor_along_last_dim(mixed_kv_layer, 2) # Attention head [sq, b, h] --> [sq, b, hp] query_layer, _ = self.query(hidden_states) # [sq, b, hp] --> [sq, b, np, hn] new_tensor_shape = query_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) query_layer = query_layer.view(*new_tensor_shape) # ================================== # Adjust key and value for inference # ================================== if inference_params: batch_start = inference_params.batch_size_offset batch_end = batch_start + key_layer.size(1) assert batch_end <= inference_key_memory.size(1) sequence_start = inference_params.sequence_len_offset sequence_end = sequence_start + key_layer.size(0) assert sequence_end <= inference_key_memory.size(0) # Copy key and values. inference_key_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = key_layer inference_value_memory[sequence_start:sequence_end, batch_start:batch_end, ...] = value_layer key_layer = inference_key_memory[ :sequence_end, batch_start:batch_end, ...] value_layer = inference_value_memory[ :sequence_end, batch_start:batch_end, ...] # ================================== # core attention computation # ================================== if self.checkpoint_core_attention: context_layer = self._checkpointed_attention_forward( query_layer, key_layer, value_layer, attention_mask) else: context_layer = self.core_attention( query_layer, key_layer, value_layer, attention_mask) # ================= # Output. [sq, b, h] # ================= output, bias = self.dense(context_layer) return output, bias def bias_dropout_add(x, bias, residual, prob, training): # type: (Tensor, Tensor, Tensor, float, bool) -> Tensor out = torch.nn.functional.dropout(x + bias, p=prob, training=training) out = residual + out return out def get_bias_dropout_add(training): def _bias_dropout_add(x, bias, residual, prob): return bias_dropout_add(x, bias, residual, prob, training) return _bias_dropout_add @torch.jit.script def bias_dropout_add_fused_train(x: torch.Tensor, bias: torch.Tensor, residual: torch.Tensor, prob: float) -> torch.Tensor: return bias_dropout_add(x, bias, residual, prob, True) @torch.jit.script def bias_dropout_add_fused_inference(x: torch.Tensor, bias: torch.Tensor, residual: torch.Tensor, prob: float) -> torch.Tensor: return bias_dropout_add(x, bias, residual, prob, False) class ParallelTransformerLayer(MegatronModule): """A single transformer layer. Transformer layer takes input with size [s, b, h] and returns an output of the same size. """ def __init__(self, init_method, output_layer_init_method, layer_number, layer_type=LayerType.encoder, self_attn_mask_type=AttnMaskType.padding, drop_path_rate=0.): args = get_args() super(ParallelTransformerLayer, self).__init__() self.layer_number = layer_number self.layer_type = layer_type self.apply_residual_connection_post_layernorm \ = args.apply_residual_connection_post_layernorm self.bf16 = args.bf16 self.fp32_residual_connection = args.fp32_residual_connection # Layernorm on the input data. self.input_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon, no_persist_layer_norm=args.no_persist_layer_norm, sequence_parallel=args.sequence_parallel) # Self attention. self.self_attention = ParallelAttention( init_method, output_layer_init_method, layer_number, attention_type=AttnType.self_attn, attn_mask_type=self_attn_mask_type) self.hidden_dropout = args.hidden_dropout self.bias_dropout_fusion = args.bias_dropout_fusion self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0.0 else None # Layernorm on the attention output self.post_attention_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon, no_persist_layer_norm=args.no_persist_layer_norm, sequence_parallel=args.sequence_parallel) if self.layer_type == LayerType.decoder: self.inter_attention = ParallelAttention( init_method, output_layer_init_method, layer_number, attention_type=AttnType.cross_attn) # Layernorm on the attention output. self.post_inter_attention_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon, no_persist_layer_norm=args.no_persist_layer_norm, sequence_parallel=args.sequence_parallel) # MLP if args.num_experts is not None: self.mlp = SwitchMLP(init_method, output_layer_init_method) else: self.mlp = ParallelMLP(init_method, output_layer_init_method) # Set bias+dropout+add fusion grad_enable execution handler. TORCH_MAJOR = int(torch.__version__.split('.')[0]) TORCH_MINOR = int(torch.__version__.split('.')[1]) use_nvfuser = TORCH_MAJOR > 1 or (TORCH_MAJOR == 1 and TORCH_MINOR >= 10) self.bias_dropout_add_exec_handler = \ nullcontext if use_nvfuser else torch.enable_grad def forward(self, hidden_states, attention_mask, encoder_output=None, enc_dec_attn_mask=None, inference_params=None): # hidden_states: [s, b, h] # Layer norm at the beginning of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Self attention. attention_output, attention_bias = \ self.self_attention( layernorm_output, attention_mask, inference_params=inference_params) # Residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states if self.drop_path is None: # jit scripting for a nn.module (with dropout) is not # trigerring the fusion kernel. For now, we use two # different nn.functional routines to account for varying # dropout semantics during training and inference phases. if self.bias_dropout_fusion: if self.training: bias_dropout_add_func = bias_dropout_add_fused_train else: bias_dropout_add_func = bias_dropout_add_fused_inference else: bias_dropout_add_func = get_bias_dropout_add(self.training) with self.bias_dropout_add_exec_handler(): layernorm_input = bias_dropout_add_func( attention_output, attention_bias.expand_as(residual), residual, self.hidden_dropout) else: out = torch.nn.functional.dropout(attention_output + attention_bias, p=self.hidden_dropout, training=self.training) layernorm_input = residual + self.drop_path(out) # Layer norm post the self attention. layernorm_output = self.post_attention_layernorm(layernorm_input) if self.layer_type == LayerType.decoder: attention_output, attention_bias = \ self.inter_attention(layernorm_output, enc_dec_attn_mask, encoder_output=encoder_output) # residual connection if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = layernorm_input with self.bias_dropout_add_exec_handler(): layernorm_input = bias_dropout_add_func( attention_output, attention_bias.expand_as(residual), residual, self.hidden_dropout) # Layer norm post the decoder attention layernorm_output = self.post_inter_attention_layernorm(layernorm_input) # MLP. mlp_output, mlp_bias = self.mlp(layernorm_output) # Second residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = layernorm_input if self.drop_path is None: with self.bias_dropout_add_exec_handler(): output = bias_dropout_add_func( mlp_output, mlp_bias.expand_as(residual), residual, self.hidden_dropout) # Jit compiled function creates 'view' tensor. This tensor # potentially gets saved in the MPU checkpoint function context, # which rejects view tensors. While making a viewless tensor here # won't result in memory savings (like the data loader, or # p2p_communication), it serves to document the origin of this # 'view' tensor. output = mpu.make_viewless_tensor(inp = output, requires_grad = output.requires_grad, keep_graph = True) else: out = torch.nn.functional.dropout(mlp_output + mlp_bias, p=self.hidden_dropout, training=self.training) output = residual + self.drop_path(out) return output class NoopTransformerLayer(MegatronModule): """A single 'no-op' transformer layer. The sole purpose of this layer is for when a standalone embedding layer is used (i.e., args.standalone_embedding_stage == True). In this case, zero transformer layers are assigned when pipeline rank == 0. Additionally, when virtual pipeline rank >= 1, zero total model parameters are created (virtual rank 0 contains the input embedding). This results in the model's input and output tensors being the same, which causes an error when performing certain memory optimiations on the output tensor (e.g., deallocating it). Thus, this layer disconnects the input from the output via a clone. Since ranks containing a no-op layer are generally under- utilized (both compute and memory), there's no worry of any performance degredation. """ def __init__(self, layer_number): super().__init__() self.layer_number = layer_number def forward(self, hidden_states, attention_mask, encoder_output=None, enc_dec_attn_mask=None, inference_params=None): return hidden_states.clone() class ParallelTransformer(MegatronModule): """Transformer class.""" def __init__(self, init_method, output_layer_init_method, layer_type=LayerType.encoder, self_attn_mask_type=AttnMaskType.padding, trans_layer_type="encoder", post_layer_norm=True, pre_process=True, post_process=True, drop_path_rate=0.0): super(ParallelTransformer, self).__init__() args = get_args() self.layer_type = layer_type self.model_type = args.model_type self.bf16 = args.bf16 self.fp32_residual_connection = args.fp32_residual_connection self.post_layer_norm = post_layer_norm self.pre_process = pre_process self.post_process = post_process self.input_tensor = None self.drop_path_rate = drop_path_rate # Store activation checkpoiting flag. self.recompute_granularity = args.recompute_granularity self.recompute_method = args.recompute_method self.recompute_num_layers = args.recompute_num_layers self.distribute_saved_activations = \ args.distribute_saved_activations and not args.sequence_parallel self.sequence_parallel = args.sequence_parallel # Number of layers. if trans_layer_type == "encoder": self.num_layers = mpu.get_num_layers( args, args.model_type == ModelType.encoder_and_decoder) elif trans_layer_type == "decoder": self.num_layers = mpu.get_num_layers_decoder( args, args.model_type == ModelType.encoder_and_decoder) else: print("No support layer type") import sys;sys.exit(0) self.drop_path_rates = [rate.item() for rate in torch.linspace(0, self.drop_path_rate, args.num_layers)] # Transformer layers. def build_layer(layer_number): return ParallelTransformerLayer( init_method, output_layer_init_method, layer_number, layer_type=layer_type, self_attn_mask_type=self_attn_mask_type, drop_path_rate=self.drop_path_rates[layer_number - 1]) if args.virtual_pipeline_model_parallel_size is not None: assert args.num_layers % args.virtual_pipeline_model_parallel_size == 0, \ 'num_layers_per_stage must be divisible by ' \ 'virtual_pipeline_model_parallel_size' assert args.model_type != ModelType.encoder_and_decoder # Number of layers in each model chunk is the number of layers in the stage, # divided by the number of model chunks in a stage. self.num_layers = self.num_layers // args.virtual_pipeline_model_parallel_size # With 8 layers, 2 stages, and 4 model chunks, we want an assignment of # layers to stages like (each list is a model chunk): # Stage 0: [0] [2] [4] [6] # Stage 1: [1] [3] [5] [7] # With 8 layers, 2 stages, and 2 virtual stages, we want an assignment of # layers to stages like (each list is a model chunk): # Stage 0: [0, 1] [4, 5] # Stage 1: [2, 3] [6, 7] offset = mpu.get_virtual_pipeline_model_parallel_rank() * ( args.num_layers // args.virtual_pipeline_model_parallel_size) + \ (mpu.get_pipeline_model_parallel_rank() * self.num_layers) else: # Each stage gets a contiguous set of layers. if args.model_type == ModelType.encoder_and_decoder and \ mpu.get_pipeline_model_parallel_world_size() > 1: pipeline_rank = mpu.get_pipeline_model_parallel_rank() if layer_type == LayerType.encoder: offset = pipeline_rank * self.num_layers else: num_ranks_in_enc = args.pipeline_model_parallel_split_rank offset = (pipeline_rank - num_ranks_in_enc) * self.num_layers else: offset = mpu.get_pipeline_model_parallel_rank() * self.num_layers if self.num_layers == 0: # When a standalone embedding stage is used (e.g., # args.standalone_embedding_stage == True), virtual pipeline ranks # on pipeline rank 0 will have zero transformer layers assigned to # them. This results in the model's input and output tensors to be # the same, which will cause failure for certain output tensor # optimizations (e.g., pipeline output deallocation). To remedy # this, we assign a 'no-op' layer on these ranks, which will # disconnect the input tensor from the output tensor. self.num_layers = 1 self.layers = torch.nn.ModuleList([ NoopTransformerLayer(1) ]) else: self.layers = torch.nn.ModuleList( [build_layer(i + 1 + offset) for i in range(self.num_layers)]) if self.post_process and self.post_layer_norm: # Final layer norm before output. self.final_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon, no_persist_layer_norm=args.no_persist_layer_norm, sequence_parallel=args.sequence_parallel) def _get_layer(self, layer_number): return self.layers[layer_number] def _checkpointed_forward(self, hidden_states, attention_mask, encoder_output, enc_dec_attn_mask): """Forward method with activation checkpointing.""" def custom(start, end): def custom_forward(*inputs): x_ = inputs[0] attention_mask = inputs[1] encoder_output = inputs[2] enc_dec_attn_mask = inputs[3] for index in range(start, end): layer = self._get_layer(index) x_ = layer(x_, attention_mask, encoder_output, enc_dec_attn_mask) return x_ return custom_forward if self.recompute_method == 'uniform': # Uniformly divide the total number of Transformer layers and checkpoint # the input activation of each divided chunk. # A method to further reduce memory usage reducing checkpoints. l = 0 while l < self.num_layers: hidden_states = mpu.checkpoint( custom(l, l + self.recompute_num_layers), self.distribute_saved_activations, hidden_states, attention_mask, encoder_output, enc_dec_attn_mask) l += self.recompute_num_layers elif self.recompute_method == 'block': # Checkpoint the input activation of only a set number of individual # Transformer layers and skip the rest. # A method fully use the device memory removing redundant re-computation. for l in range(self.num_layers): if l < self.recompute_num_layers: hidden_states = mpu.checkpoint( custom(l, l + 1), self.distribute_saved_activations, hidden_states, attention_mask, encoder_output, enc_dec_attn_mask) else: hidden_states = custom(l, l + 1)( hidden_states, attention_mask, encoder_output, enc_dec_attn_mask) else: raise ValueError("Invalid activation recompute method.") return hidden_states def set_input_tensor(self, input_tensor): """Set input tensor to be used instead of forward()'s input. When doing pipeline parallelism the input from the previous stage comes from communication, not from the input, so the model's forward_step_func won't have it. This function is thus used by internal code to bypass the input provided by the forward_step_func""" self.input_tensor = input_tensor def forward(self, hidden_states, attention_mask, encoder_output=None, enc_dec_attn_mask=None, inference_params=None): # hidden_states: [s, b, h] # Checks. if inference_params: assert self.recompute_granularity is None, \ 'inference does not work with activation checkpointing' if not self.pre_process: # See set_input_tensor() hidden_states = self.input_tensor # Viewless tensor. # - We only need to create a viewless tensor in the case of micro batch # size (mbs) == 1, since in this case, 'hidden_states.transpose()' # above creates a view tensor, and '.contiguous()' is a pass-through. # For mbs >= 2, '.contiguous()' creates a new tensor, eliminating # the need to make it viewless. # # However, we don't explicitly check mbs == 1 here because # make_viewless_tensor() has negligible overhead when its input # is already viewless. # # - For the 'else' case above, calling make_viewless_tensor() here is # likely redundant, since p2p_communication.py (likely originator) # already creates viewless tensors. That said, make_viewless_tensor() # is called here to be future-proof and corner-case-proof. hidden_states = mpu.make_viewless_tensor( hidden_states, requires_grad=True, keep_graph=True, ) if self.sequence_parallel: rng_context = mpu.get_cuda_rng_tracker().fork() else: rng_context = nullcontext() with rng_context: # Forward pass. if self.recompute_granularity == 'full': hidden_states = self._checkpointed_forward(hidden_states, attention_mask, encoder_output, enc_dec_attn_mask) else: for index in range(self.num_layers): layer = self._get_layer(index) hidden_states = layer( hidden_states, attention_mask, encoder_output=encoder_output, enc_dec_attn_mask=enc_dec_attn_mask, inference_params=inference_params) # Final layer norm. if self.post_process and self.post_layer_norm: hidden_states = self.final_layernorm(hidden_states) return hidden_states