megatron_patch/model/glm130b/transformer.py (875 lines of code) (raw):
# Copyright (c) 2023 Alibaba PAI and Nvidia Megatron-LM Team.
#
# 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 math
from contextlib import nullcontext
from typing import Optional
import torch
import torch.nn.functional as F
from torch import Tensor
from megatron import core
from megatron import get_args
from megatron import get_num_microbatches
from megatron.core import mpu, tensor_parallel
from megatron.model import LayerNorm
from megatron.model.enums import AttnMaskType
from megatron.model.enums import AttnType
from megatron.model.enums import LayerType
from megatron.model.fused_bias_gelu import bias_gelu_impl
from megatron.model.fused_softmax import FusedScaleMaskSoftmax
from megatron.model.module import MegatronModule
from megatron.model.rotary_pos_embedding import apply_rotary_pos_emb
from megatron.model.utils import attention_mask_func
from megatron.model.utils import openai_gelu
from megatron.model.utils import erf_gelu
from megatron.core.enums import ModelType
try:
from einops import rearrange
except ImportError:
rearrange = None
try:
from flash_attn.flash_attn_interface import flash_attn_unpadded_func
except ImportError:
flash_attn_unpadded_func = None
""" 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
# hidden_state: [s, b, h]
shape = (1, ) + (
hidden_state.shape[1], ) + (1, ) * (hidden_state.ndim - 2)
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
def _args_to_kwargs():
args = get_args()
common_kwargs = {
'params_dtype': args.params_dtype,
'use_cpu_initialization': args.use_cpu_initialization,
'perform_initialization': args.perform_initialization,
'gradient_accumulation_fusion': args.gradient_accumulation_fusion,
'sequence_parallel_enabled': args.sequence_parallel,
}
return common_kwargs
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()
self.add_bias = args.add_bias_linear
inner_hidden_size = 4 * args.hidden_size * 2 // 3
self.dense_h_to_4h = tensor_parallel.ColumnParallelLinear(
args.hidden_size,
2 * inner_hidden_size,
bias=self.add_bias,
gather_output=False,
init_method=init_method,
skip_bias_add=False,
async_tensor_model_parallel_allreduce=args.
async_tensor_model_parallel_allreduce,
**_args_to_kwargs())
self.bias_gelu_fusion = False
self.activation_func = None
self.swiglu = args.swiglu
if args.openai_gelu:
self.activation_func = openai_gelu
elif args.onnx_safe:
self.activation_func = erf_gelu
elif args.swiglu:
def swiglu(x):
x = torch.chunk(x, 2, dim=-1)
return F.silu(x[0]) * x[1]
self.activation_func = swiglu
elif args.squared_relu:
def squared_relu(x):
return torch.pow(F.relu(x), 2)
self.activation_func = squared_relu
elif args.geglu:
def geglu(x):
x1, x2 = x.chunk(2, dim=(x.ndim - 1))
return x1 * F.gelu(x2)
self.activation_func = geglu
else:
self.bias_gelu_fusion = args.bias_gelu_fusion
self.activation_func = F.gelu
# Project back to h.
self.dense_4h_to_h = tensor_parallel.RowParallelLinear(
inner_hidden_size,
args.hidden_size,
bias=self.add_bias,
input_is_parallel=True,
init_method=output_layer_init_method,
skip_bias_add=False,
**_args_to_kwargs())
def forward(self, hidden_states):
# [s, b, 4hp]
intermediate_parallel, bias_parallel = self.dense_h_to_4h(
hidden_states)
if self.bias_gelu_fusion:
assert self.add_bias is True
assert self.activation_func == F.gelu
intermediate_parallel = bias_gelu_impl(intermediate_parallel,
bias_parallel)
else:
if bias_parallel is not None:
intermediate_parallel = intermediate_parallel + bias_parallel
intermediate_parallel = self.activation_func(intermediate_parallel)
# [s, b, h]
output, output_bias = self.dense_4h_to_h(intermediate_parallel)
return output, output_bias
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 = core.utils.divide(
projection_size, world_size)
self.hidden_size_per_attention_head = core.utils.divide(
projection_size, args.num_attention_heads)
self.num_attention_heads_per_partition = core.utils.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]
# ===================================
query_key_layer_scaling_coeff = float(self.layer_number)
query_layer = query_layer / (
math.sqrt(self.hidden_size_per_attention_head) *
query_key_layer_scaling_coeff)
# [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 = mpu.get_global_memory_buffer().get_tensor(
(output_size[0] * output_size[1], output_size[2], output_size[3]),
query_layer.dtype, 'mpu')
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)
# 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 tensor_parallel.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 FlashSelfAttention(torch.nn.Module):
"""Implement the scaled dot product attention with softmax.
Arguments
---------
softmax_scale: The temperature to use for the softmax attention.
(default: 1/sqrt(d_keys) where d_keys is computed at
runtime)
attention_dropout: The dropout rate to apply to the attention
(default: 0.0)
"""
def __init__(self,
causal=False,
softmax_scale=None,
attention_dropout=0.0,
device=None,
dtype=None):
super().__init__()
assert flash_attn_unpadded_func is not None, (
'Please install FlashAttention first, '
'e.g., with pip install flash-attn')
assert rearrange is not None,\
'Please install einops first, e.g., with pip install einops'
self.causal = causal
self.softmax_scale = softmax_scale
self.dropout_p = attention_dropout
def forward(self, q, k, v):
"""Implements the multihead softmax attention.
Arguments
---------
q, k, v: The tensor containing the query,
key, and value. (B, S, H, D)
"""
assert all(
(i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v)))
assert all((i.is_cuda for i in (q, k, v)))
batch_size, seqlen_q = q.shape[0], q.shape[1]
seqlen_k = k.shape[1]
q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q,
step=seqlen_q,
dtype=torch.int32,
device=q.device)
if self.training:
# during training q,k,v always have same seqlen
assert seqlen_k == seqlen_q
is_causal = self.causal
cu_seqlens_k = cu_seqlens_q
else:
# turn off FA causal mask after first
# inference autoregressive iteration
# only on first autoregressive step
# q,k,v have same seqlen
is_causal = seqlen_q == seqlen_k
cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k,
step=seqlen_k,
dtype=torch.int32,
device=q.device)
self.dropout_p = 0
output = flash_attn_unpadded_func(q,
k,
v,
cu_seqlens_q,
cu_seqlens_k,
seqlen_q,
seqlen_k,
self.dropout_p,
softmax_scale=self.softmax_scale,
causal=is_causal)
output = rearrange(output, '(b s) ... -> b s ...', b=batch_size)
return output
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
self.sequence_parallel = args.sequence_parallel
self.use_flash_attn = args.use_flash_attn
if self.use_flash_attn:
if flash_attn_unpadded_func is None:
raise ImportError(
'FlashAttention is not installed, please install with '
'pip install flash-attn')
assert attention_type == AttnType.self_attn, (
'FlashAttention code path only supports '
'self-attention for now')
assert self.attn_mask_type == AttnMaskType.causal, (
'FlashAttention code path only '
'supports causal mask for now')
if rearrange is None:
raise ImportError('einops is not installed, '
'please install with pip install einops')
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 = core.utils.divide(
projection_size, args.num_attention_heads)
self.num_attention_heads_per_partition = core.utils.divide(
args.num_attention_heads, world_size)
# Strided linear layer.
if attention_type == AttnType.self_attn:
self.query_key_value = tensor_parallel.ColumnParallelLinear(
args.hidden_size,
3 * projection_size,
bias=args.add_bias_linear,
gather_output=False,
init_method=init_method,
async_tensor_model_parallel_allreduce=args.
async_tensor_model_parallel_allreduce,
**_args_to_kwargs())
else:
assert attention_type == AttnType.cross_attn
self.query = tensor_parallel.ColumnParallelLinear(
args.hidden_size,
projection_size,
bias=args.add_bias_linear,
gather_output=False,
init_method=init_method,
async_tensor_model_parallel_allreduce=args.
async_tensor_model_parallel_allreduce,
**_args_to_kwargs())
self.key_value = tensor_parallel.ColumnParallelLinear(
args.hidden_size,
2 * projection_size,
bias=args.add_bias_linear,
gather_output=False,
init_method=init_method,
async_tensor_model_parallel_allreduce=args.
async_tensor_model_parallel_allreduce,
**_args_to_kwargs())
self.core_attention = CoreAttention(self.layer_number,
self.attn_mask_type)
self.checkpoint_core_attention =\
args.recompute_granularity == 'selective'
if self.use_flash_attn:
self.core_attention_flash = FlashSelfAttention(
causal=True, attention_dropout=args.attention_dropout)
# Output.
self.dense = tensor_parallel.RowParallelLinear(
projection_size,
args.hidden_size,
bias=args.add_bias_linear,
input_is_parallel=True,
init_method=output_layer_init_method,
skip_bias_add=False,
**_args_to_kwargs())
def _checkpointed_attention_forward(self,
query_layer,
key_layer,
value_layer,
attention_mask,
rotary_pos_emb=None):
"""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_
q_pos_emb, k_pos_emb = (None, None) if rotary_pos_emb is None \
else rotary_pos_emb
hidden_states = tensor_parallel.checkpoint(custom_forward, False,
query_layer, key_layer,
value_layer, attention_mask,
q_pos_emb, k_pos_emb)
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,
rotary_pos_emb=None):
# hidden_states: [sq, b, h]
# =================================================
# Pre-allocate memory for key-values for inference.
# =================================================
is_first_step = False
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)
is_first_step = True
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) = tensor_parallel.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) = tensor_parallel.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
# ==================================
# duplicate the pos_emb for self attention
if rotary_pos_emb is not None:
if isinstance(rotary_pos_emb, tuple):
rotary_pos_emb = rotary_pos_emb
else:
rotary_pos_emb = ((rotary_pos_emb, ) * 2)
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, ...]
# adjust the key rotary positional embedding
if rotary_pos_emb is not None:
q_pos_emb, k_pos_emb = rotary_pos_emb
# need to cross check this condition during inference
# if not set_inference_key_value_memory:
if not is_first_step:
# In inference, we compute one token at a time.
# Select the correct positional embedding
# (only the last token in the sequence)
q_pos_emb = q_pos_emb[sequence_end - 1:sequence_end]
else:
# In the first forward pass of inference,
# we use the entire provided prefix.
# q_pos_emb here has the rope embeddings of the entire
# prefix + to-be-generated output so
# we slice to just the prefix.
q_pos_emb = q_pos_emb[:sequence_end, :, :, :]
k_pos_emb = k_pos_emb[:sequence_end, :, :, :]
rotary_pos_emb = (q_pos_emb, k_pos_emb)
# ==================================
# core attention computation
# ==================================
# apply relative positional encoding (rotary embedding)
if rotary_pos_emb is not None:
q_pos_emb, k_pos_emb = rotary_pos_emb
query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb)
key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb)
# TODO, can apply positional embedding to value_layer so it has
# absolute positional embedding.
# otherwise, only relative positional embedding takes effect
# value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb)
if not self.use_flash_attn:
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)
else:
q, k, v = [
rearrange(x, 's b ... -> b s ...').contiguous()
for x in (query_layer, key_layer, value_layer)
]
if not self.sequence_parallel:
with tensor_parallel.get_cuda_rng_tracker().fork():
context_layer = self.core_attention_flash(q, k, v)
else:
context_layer = self.core_attention_flash(q, k, v)
context_layer = rearrange(context_layer,
'b s h d -> s b (h d)').contiguous()
# =================
# Output. [sq, b, h]
# =================
output, bias = self.dense(context_layer)
return output, bias
def bias_dropout_add(x, bias, residual, prob, training):
# type: (Tensor, Optional[Tensor], Tensor, float, bool) -> Tensor
if bias is not None:
x = x + bias
out = torch.nn.functional.dropout(x, 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: Optional[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: Optional[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.num_layers = args.num_layers
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,
apply_layernorm_1p=args.apply_layernorm_1p)
# 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,
apply_layernorm_1p=args.apply_layernorm_1p)
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,
apply_layernorm_1p=args.apply_layernorm_1p)
# MLP
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,
rotary_pos_emb=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,
rotary_pos_emb=rotary_pos_emb)
alpha = (2 * self.num_layers)**0.5
# Residual connection.
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output * alpha
else:
residual = hidden_states
layernorm_input = attention_output + residual
# Layer norm post the self attention.
layernorm_output = self.post_attention_layernorm(layernorm_input)
mlp_output, mlp_bias = self.mlp(layernorm_output)
# Second residual connection.
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output * alpha
else:
residual = layernorm_input
output = mlp_output + residual
# 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 = core.utils.make_viewless_tensor(
inp=output, requires_grad=output.requires_grad, keep_graph=True)
return output
def _get_num_layers(args, is_encoder_and_decoder_model, is_decoder=False):
"""Compute the number of transformer
layers resident on the current rank."""
if mpu.get_pipeline_model_parallel_world_size() > 1:
if is_encoder_and_decoder_model:
assert args.pipeline_model_parallel_split_rank is not None
# When a standalone embedding stage is used, a rank is taken from
# the encoder's ranks, to be used for the encoder's embedding
# layer. This way, the rank referenced by the 'split rank' remains
# the same whether or not a standalone embedding stage is used.
num_ranks_in_encoder = (args.pipeline_model_parallel_split_rank -
1 if args.standalone_embedding_stage else
args.pipeline_model_parallel_split_rank)
num_ranks_in_decoder =\
args.transformer_pipeline_model_parallel_size -\
num_ranks_in_encoder
assert args.encoder_num_layers % num_ranks_in_encoder == 0
assert args.decoder_num_layers % num_ranks_in_decoder == 0
if mpu.is_pipeline_stage_before_split():
num_layers = (0 if args.standalone_embedding_stage
and mpu.get_pipeline_model_parallel_rank() == 0
else args.encoder_num_layers //
num_ranks_in_encoder)
else:
num_layers = args.decoder_num_layers // num_ranks_in_decoder
else:
assert args.num_layers == args.encoder_num_layers
assert args.num_layers %\
args.transformer_pipeline_model_parallel_size == 0
num_layers = (0 if args.standalone_embedding_stage
and mpu.get_pipeline_model_parallel_rank() == 0 else
args.num_layers //
args.transformer_pipeline_model_parallel_size)
else:
if not is_decoder:
num_layers = args.encoder_num_layers
else:
num_layers = args.decoder_num_layers
return num_layers
class ParallelTransformer(MegatronModule):
"""Transformer class."""
def __init__(self,
init_method,
output_layer_init_method,
layer_type=LayerType.encoder,
self_attn_mask_type=AttnMaskType.padding,
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
self.transformer_impl = args.transformer_impl
# 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
# Transformer Engine Init.
if self.transformer_impl == 'transformer_engine':
global transformer_engine
import transformer_engine
self.use_fp8 = args.fp8_e4m3 or args.fp8_hybrid
self.fp8_recipe = None
self.fp8_group = None
if self.use_fp8:
self.fp8_group = mpu.get_data_parallel_group()
if args.fp8_e4m3:
fp8_format = transformer_engine.common.recipe.Format.E4M3
elif args.fp8_hybrid:
fp8_format = transformer_engine.common.recipe.Format.HYBRID
self.fp8_recipe = transformer_engine.common.recipe.DelayedScaling(
margin=args.fp8_margin,
interval=args.fp8_interval,
fp8_format=fp8_format,
amax_history_len=args.fp8_amax_history_len,
amax_compute_algo=args.fp8_amax_compute_algo,
override_linear_precision=(False, False, not args.fp8_wgrad),
)
self.num_microbatches_in_previous_step = -1
self.microbatch_count = 0
self.checkpoint_core_attention =\
args.recompute_granularity == 'selective'
# Number of layers.
self.num_layers = _get_num_layers(
args, args.model_type == ModelType.encoder_and_decoder,
layer_type == LayerType.decoder)
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):
if args.transformer_impl == 'local':
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])
else:
return transformer_engine.pytorch.TransformerLayer(
args.hidden_size,
args.ffn_hidden_size,
args.num_attention_heads,
layernorm_epsilon=args.layernorm_epsilon,
hidden_dropout=args.hidden_dropout,
attention_dropout=args.attention_dropout,
init_method=init_method,
output_layer_init_method=output_layer_init_method,
layer_number=layer_number,
kv_channels=args.kv_channels,
self_attn_mask_type=self_attn_mask_type.name,
tp_group=mpu.get_tensor_model_parallel_group(),
get_rng_state_tracker=tensor_parallel.get_cuda_rng_tracker,
fuse_wgrad_accumulation=args.gradient_accumulation_fusion,
apply_query_key_layer_scaling=args.
apply_query_key_layer_scaling,
attention_softmax_in_fp32=args.attention_softmax_in_fp32,
seq_length=args.seq_length,
micro_batch_size=args.micro_batch_size,
sequence_parallel=args.sequence_parallel,
params_dtype=args.params_dtype,
apply_residual_connection_post_layernorm=args.
apply_residual_connection_post_layernorm,
output_layernorm=False,
layer_type='encoder',
drop_path_rate=self.drop_path_rates[layer_number - 1],
set_parallel_mode=True,
fuse_qkv_params=True)
if args.virtual_pipeline_model_parallel_size is not None:
assert args.num_layers % \
args.virtual_pipeline_model_parallel_size == 0
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
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,
apply_layernorm_1p=args.apply_layernorm_1p)
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,
rotary_pos_emb, is_first_microbatch):
"""Forward method with activation checkpointing."""
def custom(start, end, is_transformer_engine=False):
def custom_forward(*inputs):
x_ = inputs[0]
attention_mask = inputs[1]
encoder_output = inputs[2]
enc_dec_attn_mask = inputs[3]
rotary_pos_emb = inputs[4]
for index in range(start, end):
layer = self._get_layer(index)
x_ = layer(x_,
attention_mask,
encoder_output,
enc_dec_attn_mask,
rotary_pos_emb=rotary_pos_emb)
return x_
def custom_forward_transformer_engine(*args, **kwargs):
return custom_forward(*args,
is_first_microbatch=is_first_microbatch,
**kwargs)
if not is_transformer_engine:
return custom_forward
else:
return custom_forward_transformer_engine
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.
layer = 0
while layer < self.num_layers:
if self.transformer_impl == 'transformer_engine':
tec = transformer_engine.pytorch.distributed.checkpoint
hidden_states = tec(
custom(layer,
layer + self.recompute_num_layers,
is_transformer_engine=True),
self.distribute_saved_activations,
tensor_parallel.get_cuda_rng_tracker,
mpu.get_tensor_model_parallel_group(), hidden_states,
attention_mask, encoder_output, enc_dec_attn_mask,
rotary_pos_emb)
else:
hidden_states = tensor_parallel.checkpoint(
custom(layer, layer + self.recompute_num_layers),
self.distribute_saved_activations, hidden_states,
attention_mask, encoder_output, enc_dec_attn_mask,
rotary_pos_emb)
layer += 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 layer in range(self.num_layers):
if layer < self.recompute_num_layers:
if self.transformer_impl == 'transformer_engine':
tec = transformer_engine.pytorch.distributed.checkpoint
hidden_states = tec(
custom(layer,
layer + 1,
is_transformer_engine=True),
self.distribute_saved_activations,
tensor_parallel.get_cuda_rng_tracker,
mpu.get_tensor_model_parallel_group(),
hidden_states,
attention_mask,
encoder_output,
enc_dec_attn_mask,
rotary_pos_emb=rotary_pos_emb)
else:
hidden_states = tensor_parallel.checkpoint(
custom(layer, layer + 1),
self.distribute_saved_activations,
hidden_states,
attention_mask,
encoder_output,
enc_dec_attn_mask,
rotary_pos_emb=rotary_pos_emb)
else:
if self.transformer_impl == 'transformer_engine':
hidden_states = custom(
layer, layer + 1, is_transformer_engine=True)(
hidden_states,
attention_mask,
encoder_output,
enc_dec_attn_mask,
rotary_pos_emb=rotary_pos_emb)
else:
hidden_states = custom(layer, layer + 1)(
hidden_states,
attention_mask,
encoder_output,
enc_dec_attn_mask,
rotary_pos_emb=rotary_pos_emb)
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,
rotary_pos_emb=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 = core.utils.make_viewless_tensor(
hidden_states,
requires_grad=True,
keep_graph=True,
)
if self.sequence_parallel:
rng_context = tensor_parallel.get_cuda_rng_tracker().fork()
else:
rng_context = nullcontext()
with rng_context:
# The fp8_autocast context manager is a no-op when enabled=True
# The if...else serves to short
# circuit name resolution for fp8_autocast
with transformer_engine.pytorch.fp8_autocast(
enabled=self.use_fp8,
fp8_recipe=self.fp8_recipe,
fp8_group=self.fp8_group) if self.use_fp8 else nullcontext(
):
# Determine if the current iteration is first microbatch
if self.num_microbatches_in_previous_step !=\
get_num_microbatches():
self.microbatch_count = 0
self.num_microbatches_in_previous_step = get_num_microbatches()
is_first_microbatch =\
self.microbatch_count % get_num_microbatches() == 0
# Forward pass.
if self.recompute_granularity == 'full':
hidden_states = self._checkpointed_forward(
hidden_states, attention_mask, encoder_output,
enc_dec_attn_mask, rotary_pos_emb, is_first_microbatch)
else:
forward_kwargs = {
'encoder_output': encoder_output,
'enc_dec_attn_mask': enc_dec_attn_mask,
'inference_params': inference_params,
'rotary_pos_emb': rotary_pos_emb,
}
if self.transformer_impl == 'transformer_engine':
forward_kwargs['is_first_microbatch'] =\
is_first_microbatch
forward_kwargs['checkpoint_core_attention'] =\
self.checkpoint_core_attention
for index in range(self.num_layers):
layer = self._get_layer(index)
hidden_states = layer(hidden_states, attention_mask,
**forward_kwargs)
# Skip counter update for eval and activation checkpointing
if torch.is_grad_enabled() and self.training:
self.microbatch_count += 1
# Final layer norm.
if self.post_process and self.post_layer_norm:
hidden_states = self.final_layernorm(hidden_states)
return hidden_states