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Commit 9b174da8 authored by Deepak Narayanan's avatar Deepak Narayanan
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Merge branch 'pipeline_parallel_main' into 'main' 

Pipeline parallelism and inter-layer model parallelism implementation

See merge request ADLR/megatron-lm!159
parents 3aacd955 6e83649f
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megatron/mpu/data.py
View file @ 9b174da8
......@@ -15,9 +15,9 @@
import torch
from .initialize import get_model_parallel_group
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_src_rank
from .initialize import get_tensor_model_parallel_group
from .initialize import get_tensor_model_parallel_rank
from .initialize import get_tensor_model_parallel_src_rank
_MAX_DATA_DIM = 4
......@@ -36,7 +36,7 @@ def _build_key_size_numel_dictionaries(keys, data):
sizes = [0 for _ in range(max_dim) for _ in keys]
# Pack the sizes on rank zero.
if get_model_parallel_rank() == 0:
if get_tensor_model_parallel_rank() == 0:
offset = 0
for key in keys:
assert data[key].dim() < max_dim, 'you should increase MAX_DATA_DIM'
......@@ -47,8 +47,8 @@ def _build_key_size_numel_dictionaries(keys, data):
# Move to GPU and broadcast.
sizes_cuda = torch.cuda.LongTensor(sizes)
torch.distributed.broadcast(sizes_cuda, get_model_parallel_src_rank(),
group=get_model_parallel_group())
torch.distributed.broadcast(sizes_cuda, get_tensor_model_parallel_src_rank(),
group=get_tensor_model_parallel_group())
# Move back to cpu and unpack.
sizes_cpu = sizes_cuda.cpu()
......@@ -89,7 +89,7 @@ def broadcast_data(keys, data, datatype):
data)
# Pack on rank zero.
if get_model_parallel_rank() == 0:
if get_tensor_model_parallel_rank() == 0:
# Check that all keys have the same data type.
_check_data_types(keys, data, datatype)
# Flatten the data associated with the keys
......@@ -100,9 +100,9 @@ def broadcast_data(keys, data, datatype):
device=torch.cuda.current_device(),
dtype=datatype)
# Boradcast
torch.distributed.broadcast(flatten_data, get_model_parallel_src_rank(),
group=get_model_parallel_group())
# Broadcast
torch.distributed.broadcast(flatten_data, get_tensor_model_parallel_src_rank(),
group=get_tensor_model_parallel_group())
# Unpack
output = {}
......
megatron/mpu/grads.py
View file @ 9b174da8
......@@ -28,8 +28,9 @@ try:
except Exception as e:
print('WARNING: APEX is not installed, multi_tensor_applier will not be available.')
from .initialize import is_pipeline_first_stage
from .initialize import get_model_parallel_group
from .initialize import get_model_parallel_rank
from .initialize import get_tensor_model_parallel_rank
def l2_grad_clipper(parameters, max_norm):
......@@ -43,9 +44,9 @@ def l2_grad_clipper(parameters, max_norm):
parameters_with_grads = list(filter(
lambda p: p.grad is not None, parameters))
# Filter parameters for norm calculations.
mp_rank_is_zero = (get_model_parallel_rank() == 0)
mp_rank_is_zero = (get_tensor_model_parallel_rank() == 0)
parameters_for_norm = list(filter(
lambda p: p.model_parallel or mp_rank_is_zero, parameters_with_grads))
lambda p: p.tensor_model_parallel or mp_rank_is_zero, parameters_with_grads))
# Calculate L2 norm.
norm, _ = multi_tensor_applier(
amp_C.multi_tensor_l2norm,
......@@ -71,7 +72,7 @@ def l2_grad_clipper(parameters, max_norm):
return total_norm
def clip_grad_norm(parameters, max_norm, norm_type=2):
def clip_grad_norm(parameters, max_norm, norm_type=2, parameter_names=None):
"""Clips gradient norm of an iterable of parameters.
This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and
......@@ -90,13 +91,27 @@ def clip_grad_norm(parameters, max_norm, norm_type=2):
"""
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = list(filter(lambda p: p.grad is not None, parameters))
if parameter_names is not None:
filtered_parameters = []
assert len(parameters) == len(parameter_names), \
'length of parameters and parameter_names should be the same'
for p, n in zip(parameters, parameter_names):
if p.grad is not None:
# TODO: Bit hacky; is there a cleaner way to do this?
# Count embedding layer only once (in first stage).
# Don't count the weights a second time in the last stage.
if "embedding" not in n or \
is_pipeline_first_stage():
filtered_parameters.append(p)
parameters = filtered_parameters
else:
parameters = list(filter(lambda p: p.grad is not None, parameters))
max_norm = float(max_norm)
norm_type = float(norm_type)
if norm_type == inf:
total_norm = max(p.grad.data.abs().max() for p in parameters)
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
# Take max across all GPUs.
# Take max across all model-parallel GPUs.
torch.distributed.all_reduce(total_norm_cuda,
op=torch.distributed.ReduceOp.MAX,
group=get_model_parallel_group())
......@@ -105,16 +120,13 @@ def clip_grad_norm(parameters, max_norm, norm_type=2):
if clip_coef < 1:
for p in parameters:
p.grad.data.mul_(clip_coef)
#elif norm_type == 2:
# total_norm = l2_grad_clipper(parameters, max_norm)
else:
total_norm = 0
for p in parameters:
if p.model_parallel or (get_model_parallel_rank() == 0):
if p.tensor_model_parallel or (get_tensor_model_parallel_rank() == 0):
param_norm = torch.linalg.norm(p.grad.data.flatten(), norm_type)
total_norm += param_norm.item() ** norm_type
# Sum across all model parallel GPUs.
# Sum across all model-parallel GPUs.
total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
torch.distributed.all_reduce(total_norm_cuda,
op=torch.distributed.ReduceOp.SUM,
......
megatron/mpu/initialize.py
View file @ 9b174da8
......@@ -21,75 +21,148 @@ import torch
from .utils import ensure_divisibility
# Model parallel group that the current rank belongs to.
# Intra-layer model parallel group that the current rank belongs to.
_TENSOR_MODEL_PARALLEL_GROUP = None
# Inter-layer model parallel group that the current rank belongs to.
_PIPELINE_MODEL_PARALLEL_GROUP = None
# Model parallel group (both intra- and pipeline) that the current rank belongs to.
_MODEL_PARALLEL_GROUP = None
# Embedding group.
_EMBEDDING_GROUP = None
# Data parallel group that the current rank belongs to.
_DATA_PARALLEL_GROUP = None
# These values enable us to change the mpu sizes on the fly.
_MPU_WORLD_SIZE = None
_MPU_RANK = None
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = None
_MPU_TENSOR_MODEL_PARALLEL_RANK = None
_MPU_PIPELINE_MODEL_PARALLEL_RANK = None
# A list of global ranks for each pipeline group to ease calculation of the source
# rank when broadcasting from the first or last pipeline stage
_PIPELINE_GLOBAL_RANKS = None
def is_unitialized():
"""Useful for code segments that may be accessed with or without mpu initialization"""
return _DATA_PARALLEL_GROUP is None
def initialize_model_parallel(model_parallel_size_):
def initialize_model_parallel(tensor_model_parallel_size_=1,
pipeline_model_parallel_size_=1):
"""
Initialize model data parallel groups.
Arguments:
model_parallel_size: number of GPUs used to parallelize model.
Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we
use 2 GPUs to parallelize the model. The present function will
create 4 model parallel groups and 2 data parallel grous as:
4 model parallel groups:
[g0, g1], [g2, g3], [g4, g5], [g6, g7]
2 data parallel groups:
[g0, g2, g4, g6], [g1, g3, g5, g7]
tensor_model_parallel_size: number of GPUs used to parallelize model tensor.
pipeline_model_parallel_size: number of GPUs used to parallelize model pipeline.
Let's say we have a total of 16 GPUs denoted by g0 ... g15 and we
use 2 GPUs to parallelize the model tensor, and 4 GPUs to parallelize
the model pipeline. The present function will
create 8 tensor model-parallel groups, 4 pipeline model-parallel groups
and 8 data-parallel groups as:
8 data_parallel groups:
[g0, g2], [g1, g3], [g4, g6], [g5, g7], [g8, g10], [g9, g11], [g12, g14], [g13, g15]
8 tensor model-parallel groups:
[g0, g1], [g2, g3], [g4, g5], [g6, g7], [g8, g9], [g10, g11], [g12, g13], [g14, g15]
4 pipeline model-parallel groups:
[g0, g4, g8, g12], [g1, g5, g9, g13], [g2, g6, g10, g14], [g3, g7, g11, g15]
Note that for efficiency, the caller should make sure adjacent ranks
are on the same DGX box. For example if we are using 2 DGX-1 boxes
with a total of 16 GPUs, rank 0 to 7 belong to the first box and
ranks 8 to 15 belong to the second box.
"""
if torch.distributed.get_rank() == 0:
print('> initializing model parallel with size {}'.format(
model_parallel_size_))
print('> initializing tensor model parallel with size {}'.format(
tensor_model_parallel_size_))
print('> initializing pipeline model parallel with size {}'.format(
pipeline_model_parallel_size_))
# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size = torch.distributed.get_world_size()
model_parallel_size = min(model_parallel_size_, world_size)
ensure_divisibility(world_size, model_parallel_size)
tensor_model_parallel_size = min(tensor_model_parallel_size_, world_size)
pipeline_model_parallel_size = min(pipeline_model_parallel_size_, world_size)
ensure_divisibility(world_size,
tensor_model_parallel_size * pipeline_model_parallel_size)
data_parallel_size = world_size // (tensor_model_parallel_size *
pipeline_model_parallel_size)
num_tensor_model_parallel_groups = world_size // tensor_model_parallel_size
num_pipeline_model_parallel_groups = world_size // pipeline_model_parallel_size
num_data_parallel_groups = world_size // data_parallel_size
rank = torch.distributed.get_rank()
# Build the data parallel groups.
# Build the data-parallel groups.
global _DATA_PARALLEL_GROUP
assert _DATA_PARALLEL_GROUP is None, \
'data parallel group is already initialized'
for i in range(model_parallel_size):
ranks = range(i, world_size, model_parallel_size)
group = torch.distributed.new_group(ranks)
if i == (rank % model_parallel_size):
_DATA_PARALLEL_GROUP = group
# Build the model parallel groups.
all_data_parallel_group_ranks = []
for i in range(pipeline_model_parallel_size):
start_rank = i * num_pipeline_model_parallel_groups
end_rank = (i + 1) * num_pipeline_model_parallel_groups
for j in range(tensor_model_parallel_size):
ranks = range(start_rank + j, end_rank,
tensor_model_parallel_size)
all_data_parallel_group_ranks.append(list(ranks))
group = torch.distributed.new_group(ranks)
if rank in ranks:
_DATA_PARALLEL_GROUP = group
# Build the model-parallel groups.
global _MODEL_PARALLEL_GROUP
assert _MODEL_PARALLEL_GROUP is None, \
'model parallel group is already initialized'
for i in range(world_size // model_parallel_size):
ranks = range(i * model_parallel_size,
(i + 1) * model_parallel_size)
for i in range(data_parallel_size):
ranks = [data_parallel_group_ranks[i]
for data_parallel_group_ranks in all_data_parallel_group_ranks]
group = torch.distributed.new_group(ranks)
if i == (rank // model_parallel_size):
if rank in ranks:
_MODEL_PARALLEL_GROUP = group
# Build the tensor model-parallel groups.
global _TENSOR_MODEL_PARALLEL_GROUP
assert _TENSOR_MODEL_PARALLEL_GROUP is None, \
'tensor model parallel group is already initialized'
for i in range(num_tensor_model_parallel_groups):
ranks = range(i * tensor_model_parallel_size,
(i + 1) * tensor_model_parallel_size)
group = torch.distributed.new_group(ranks)
if rank in ranks:
_TENSOR_MODEL_PARALLEL_GROUP = group
# Build the pipeline model-parallel groups and embedding groups
# (first and last rank in each pipeline model-parallel group).
global _PIPELINE_MODEL_PARALLEL_GROUP
global _PIPELINE_GLOBAL_RANKS
assert _PIPELINE_MODEL_PARALLEL_GROUP is None, \
'pipeline model parallel group is already initialized'
global _EMBEDDING_GROUP
assert _EMBEDDING_GROUP is None, \
'embedding group is already initialized'
for i in range(num_pipeline_model_parallel_groups):
ranks = range(i, world_size,
num_pipeline_model_parallel_groups)
group = torch.distributed.new_group(ranks)
if rank in ranks:
_PIPELINE_MODEL_PARALLEL_GROUP = group
_PIPELINE_GLOBAL_RANKS = ranks
# Setup embedding group (to exchange gradients between
# first and last stages).
if len(ranks) > 1:
embedding_ranks = [ranks[0], ranks[-1]]
else:
embedding_ranks = ranks
group = torch.distributed.new_group(embedding_ranks)
if rank in embedding_ranks:
_EMBEDDING_GROUP = group
def model_parallel_is_initialized():
"""Check if model and data parallel groups are initialized."""
if _MODEL_PARALLEL_GROUP is None or _DATA_PARALLEL_GROUP is None:
if _TENSOR_MODEL_PARALLEL_GROUP is None or \
_PIPELINE_MODEL_PARALLEL_GROUP is None or \
_DATA_PARALLEL_GROUP is None:
return False
return True
......@@ -101,6 +174,20 @@ def get_model_parallel_group():
return _MODEL_PARALLEL_GROUP
def get_tensor_model_parallel_group():
"""Get the tensor model parallel group the caller rank belongs to."""
assert _TENSOR_MODEL_PARALLEL_GROUP is not None, \
'intra_layer_model parallel group is not initialized'
return _TENSOR_MODEL_PARALLEL_GROUP
def get_pipeline_model_parallel_group():
"""Get the pipeline model parallel group the caller rank belongs to."""
assert _PIPELINE_MODEL_PARALLEL_GROUP is not None, \
'pipeline_model parallel group is not initialized'
return _PIPELINE_MODEL_PARALLEL_GROUP
def get_data_parallel_group():
"""Get the data parallel group the caller rank belongs to."""
assert _DATA_PARALLEL_GROUP is not None, \
......@@ -108,41 +195,97 @@ def get_data_parallel_group():
return _DATA_PARALLEL_GROUP
def set_model_parallel_world_size(world_size):
"""Set the model parallel size"""
global _MPU_WORLD_SIZE
_MPU_WORLD_SIZE = world_size
def get_embedding_group():
"""Get the embedding group the caller rank belongs to."""
assert _EMBEDDING_GROUP is not None, \
'embedding group is not initialized'
return _EMBEDDING_GROUP
def set_tensor_model_parallel_world_size(world_size):
"""Set the tensor model parallel size"""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
_MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE = world_size
def set_pipeline_model_parallel_world_size(world_size):
"""Set the pipeline model parallel size"""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
_MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE = world_size
def get_model_parallel_world_size():
"""Return world size for the model parallel group."""
global _MPU_WORLD_SIZE
if _MPU_WORLD_SIZE is not None:
return _MPU_WORLD_SIZE
return torch.distributed.get_world_size(group=get_model_parallel_group())
def get_tensor_model_parallel_world_size():
"""Return world size for the tensor model parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
if _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_TENSOR_MODEL_PARALLEL_WORLD_SIZE
return torch.distributed.get_world_size(group=get_tensor_model_parallel_group())
def set_model_parallel_rank(rank):
"""Set model parallel rank."""
global _MPU_RANK
_MPU_RANK = rank
def get_pipeline_model_parallel_world_size():
"""Return world size for the pipeline model parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
if _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_WORLD_SIZE
return torch.distributed.get_world_size(group=get_pipeline_model_parallel_group())
def get_model_parallel_rank():
"""Return my rank for the model parallel group."""
global _MPU_RANK
if _MPU_RANK is not None:
return _MPU_RANK
return torch.distributed.get_rank(group=get_model_parallel_group())
def set_tensor_model_parallel_rank(rank):
"""Set tensor model parallel rank."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
_MPU_TENSOR_MODEL_PARALLEL_RANK = rank
def get_model_parallel_src_rank():
"""Calculate the global rank corresponding to a local rank zeor
in the model parallel group."""
def set_pipeline_model_parallel_rank(rank):
"""Set pipeline model parallel rank."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
_MPU_PIPELINE_MODEL_PARALLEL_RANK = rank
def get_tensor_model_parallel_rank():
"""Return my rank for the tensor model parallel group."""
global _MPU_TENSOR_MODEL_PARALLEL_RANK
if _MPU_TENSOR_MODEL_PARALLEL_RANK is not None:
return _MPU_TENSOR_MODEL_PARALLEL_RANK
return torch.distributed.get_rank(group=get_tensor_model_parallel_group())
def get_pipeline_model_parallel_rank():
"""Return my rank for the pipeline model parallel group."""
global _MPU_PIPELINE_MODEL_PARALLEL_RANK
if _MPU_PIPELINE_MODEL_PARALLEL_RANK is not None:
return _MPU_PIPELINE_MODEL_PARALLEL_RANK
return torch.distributed.get_rank(group=get_pipeline_model_parallel_group())
def is_pipeline_first_stage():
"""Return True if in the first pipeline model-parallel stage, False otherwise."""
return get_pipeline_model_parallel_rank() == 0
def is_pipeline_last_stage():
"""Return True if in the last pipeline model-parallel stage, False otherwise."""
return get_pipeline_model_parallel_rank() == (
get_pipeline_model_parallel_world_size() - 1)
def get_tensor_model_parallel_src_rank():
"""Calculate the global rank corresponding to the first local rank
in the tensor model parallel group."""
global_rank = torch.distributed.get_rank()
local_world_size = get_model_parallel_world_size()
local_world_size = get_tensor_model_parallel_world_size()
return (global_rank // local_world_size) * local_world_size
def get_pipeline_model_parallel_last_rank():
assert _PIPELINE_GLOBAL_RANKS is not None, \
"Pipeline parallel group is not initialized"
last_rank_local = get_pipeline_model_parallel_world_size() - 1
return _PIPELINE_GLOBAL_RANKS[last_rank_local]
def get_pipeline_model_parallel_first_rank():
assert _PIPELINE_GLOBAL_RANKS is not None, \
"Pipeline parallel group is not initialized"
return _PIPELINE_GLOBAL_RANKS[0]
def get_data_parallel_world_size():
"""Return world size for the data parallel group."""
......@@ -156,7 +299,9 @@ def get_data_parallel_rank():
def destroy_model_parallel():
"""Set the groups to none."""
global _MODEL_PARALLEL_GROUP
_MODEL_PARALLEL_GROUP = None
global _TENSOR_MODEL_PARALLEL_GROUP
_TENSOR_MODEL_PARALLEL_GROUP = None
global _PIPELINE_MODEL_PARALLEL_GROUP
_PIPELINE_MODEL_PARALLEL_GROUP = None
global _DATA_PARALLEL_GROUP
_DATA_PARALLEL_GROUP = None
megatron/mpu/layers.py
View file @ 9b174da8
......@@ -35,12 +35,12 @@ except Exception as e:
'instead of apex.normalization.FusedLayerNorm!')
from torch.nn import LayerNorm
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_world_size
from .mappings import copy_to_model_parallel_region
from .mappings import gather_from_model_parallel_region
from .mappings import reduce_from_model_parallel_region
from .mappings import scatter_to_model_parallel_region
from .initialize import get_tensor_model_parallel_rank
from .initialize import get_tensor_model_parallel_world_size
from .mappings import copy_to_tensor_model_parallel_region
from .mappings import gather_from_tensor_model_parallel_region
from .mappings import reduce_from_tensor_model_parallel_region
from .mappings import scatter_to_tensor_model_parallel_region
from .random import get_cuda_rng_tracker
from .utils import divide
from .utils import split_tensor_along_last_dim
......@@ -51,7 +51,7 @@ def _initialize_affine_weight_gpu(weight, init_method,
partition_dim, stride=1):
"""Initialize affine weight for model parallel on GPU."""
weight.model_parallel = True
weight.tensor_model_parallel = True
weight.partition_dim = partition_dim
weight.partition_stride = stride
......@@ -68,7 +68,7 @@ def _initialize_affine_weight_cpu(weight, output_size, input_size,
Build the master weight on all processes and scatter
the relevant chunk."""
weight.model_parallel = True
weight.tensor_model_parallel = True
weight.partition_dim = partition_dim
weight.partition_stride = stride
......@@ -85,7 +85,7 @@ def _initialize_affine_weight_cpu(weight, output_size, input_size,
weight_list = torch.split(master_weight, per_partition_per_stride_size,
dim=partition_dim)
rank = get_model_parallel_rank()
world_size = get_model_parallel_world_size()
world_size = get_tensor_model_parallel_world_size()
my_weight_list = weight_list[rank::world_size]
with torch.no_grad():
......@@ -119,12 +119,12 @@ class VocabParallelEmbedding(torch.nn.Module):
self.scale_grad_by_freq = False
self.sparse = False
self._weight = None
self.model_parallel_size = get_model_parallel_world_size()
self.tensor_model_parallel_size = get_tensor_model_parallel_world_size()
# Divide the weight matrix along the vocaburaly dimension.
self.vocab_start_index, self.vocab_end_index = \
VocabUtility.vocab_range_from_global_vocab_size(
self.num_embeddings, get_model_parallel_rank(),
self.model_parallel_size)
self.num_embeddings, get_tensor_model_parallel_rank(),
self.tensor_model_parallel_size)
self.num_embeddings_per_partition = self.vocab_end_index - \
self.vocab_start_index
......@@ -145,7 +145,7 @@ class VocabParallelEmbedding(torch.nn.Module):
partition_dim=0, stride=1)
def forward(self, input_):
if self.model_parallel_size > 1:
if self.tensor_model_parallel_size > 1:
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | \
(input_ >= self.vocab_end_index)
......@@ -160,10 +160,10 @@ class VocabParallelEmbedding(torch.nn.Module):
self.norm_type, self.scale_grad_by_freq,
self.sparse)
# Mask the output embedding.
if self.model_parallel_size > 1:
if self.tensor_model_parallel_size > 1:
output_parallel[input_mask, :] = 0.0
# Reduce across all the model parallel GPUs.
output = reduce_from_model_parallel_region(output_parallel)
output = reduce_from_tensor_model_parallel_region(output_parallel)
return output
......@@ -202,7 +202,7 @@ class ColumnParallelLinear(torch.nn.Module):
self.output_size = output_size
self.gather_output = gather_output
# Divide the weight matrix along the last dimension.
world_size = get_model_parallel_world_size()
world_size = get_tensor_model_parallel_world_size()
self.output_size_per_partition = divide(output_size, world_size)
self.skip_bias_add = skip_bias_add
......@@ -235,7 +235,7 @@ class ColumnParallelLinear(torch.nn.Module):
self.output_size_per_partition,
device=torch.cuda.current_device(),
dtype=args.params_dtype))
self.bias.model_parallel = True
self.bias.tensor_model_parallel = True
self.bias.partition_dim = 0
self.bias.stride = stride
# Always initialize bias to zero.
......@@ -248,14 +248,14 @@ class ColumnParallelLinear(torch.nn.Module):
def forward(self, input_):
# Set up backprop all-reduce.
input_parallel = copy_to_model_parallel_region(input_)
input_parallel = copy_to_tensor_model_parallel_region(input_)
# Matrix multiply.
bias = self.bias if not self.skip_bias_add else None
output_parallel = F.linear(input_parallel, self.weight, bias)
if self.gather_output:
# All-gather across the partitions.
output = gather_from_model_parallel_region(output_parallel)
output = gather_from_tensor_model_parallel_region(output_parallel)
else:
output = output_parallel
output_bias = self.bias if self.skip_bias_add else None
......@@ -304,7 +304,7 @@ class RowParallelLinear(torch.nn.Module):
self.output_size = output_size
self.input_is_parallel = input_is_parallel
# Divide the weight matrix along the last dimension.
world_size = get_model_parallel_world_size()
world_size = get_tensor_model_parallel_world_size()
self.input_size_per_partition = divide(input_size, world_size)
self.skip_bias_add = skip_bias_add
......@@ -348,11 +348,11 @@ class RowParallelLinear(torch.nn.Module):
if self.input_is_parallel:
input_parallel = input_
else:
input_parallel = scatter_to_model_parallel_region(input_)
input_parallel = scatter_to_tensor_model_parallel_region(input_)
# Matrix multiply.
output_parallel = F.linear(input_parallel, self.weight)
# All-reduce across all the partitions.
output_ = reduce_from_model_parallel_region(output_parallel)
output_ = reduce_from_tensor_model_parallel_region(output_parallel)
if not self.skip_bias_add:
output = output_ + self.bias if self.bias is not None else output_
output_bias = None
......
megatron/mpu/mappings.py
View file @ 9b174da8
......@@ -15,7 +15,7 @@
import torch
from .initialize import get_model_parallel_group, get_model_parallel_world_size, get_model_parallel_rank
from .initialize import get_tensor_model_parallel_group, get_tensor_model_parallel_world_size, get_tensor_model_parallel_rank
from .utils import split_tensor_along_last_dim
......@@ -23,11 +23,11 @@ def _reduce(input_):
"""All-reduce the the input tensor across model parallel group."""
# Bypass the function if we are using only 1 GPU.
if get_model_parallel_world_size()==1:
if get_tensor_model_parallel_world_size()==1:
return input_
# All-reduce.
torch.distributed.all_reduce(input_, group=get_model_parallel_group())
torch.distributed.all_reduce(input_, group=get_tensor_model_parallel_group())
return input_
......@@ -36,7 +36,7 @@ def _split(input_):
"""Split the tensor along its last dimension and keep the
corresponding slice."""
world_size = get_model_parallel_world_size()
world_size = get_tensor_model_parallel_world_size()
# Bypass the function if we are using only 1 GPU.
if world_size==1:
return input_
......@@ -45,7 +45,7 @@ def _split(input_):
input_list = split_tensor_along_last_dim(input_, world_size)
# Note: torch.split does not create contiguous tensors by default.
rank = get_model_parallel_rank()
rank = get_tensor_model_parallel_rank()
output = input_list[rank].contiguous()
return output
......@@ -54,18 +54,18 @@ def _split(input_):
def _gather(input_):
"""Gather tensors and concatinate along the last dimension."""
world_size = get_model_parallel_world_size()
world_size = get_tensor_model_parallel_world_size()
# Bypass the function if we are using only 1 GPU.
if world_size==1:
return input_
# Size and dimension.
last_dim = input_.dim() - 1
rank = get_model_parallel_rank()
rank = get_tensor_model_parallel_rank()
tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
tensor_list[rank] = input_
torch.distributed.all_gather(tensor_list, input_, group=get_model_parallel_group())
torch.distributed.all_gather(tensor_list, input_, group=get_tensor_model_parallel_group())
# Note: torch.cat already creates a contiguous tensor.
output = torch.cat(tensor_list, dim=last_dim).contiguous()
......@@ -141,17 +141,17 @@ class _GatherFromModelParallelRegion(torch.autograd.Function):
# Helper functions.
# -----------------
def copy_to_model_parallel_region(input_):
def copy_to_tensor_model_parallel_region(input_):
return _CopyToModelParallelRegion.apply(input_)
def reduce_from_model_parallel_region(input_):
def reduce_from_tensor_model_parallel_region(input_):
return _ReduceFromModelParallelRegion.apply(input_)
def scatter_to_model_parallel_region(input_):
def scatter_to_tensor_model_parallel_region(input_):
return _ScatterToModelParallelRegion.apply(input_)
def gather_from_model_parallel_region(input_):
def gather_from_tensor_model_parallel_region(input_):
return _GatherFromModelParallelRegion.apply(input_)
megatron/mpu/random.py
View file @ 9b174da8
......@@ -28,9 +28,9 @@ from megatron import get_args
from megatron.memory import allocate_mem_buff
from .initialize import get_data_parallel_rank
from .initialize import get_model_parallel_group
from .initialize import get_model_parallel_rank
from .initialize import get_model_parallel_world_size
from .initialize import get_tensor_model_parallel_group
from .initialize import get_tensor_model_parallel_rank
from .initialize import get_tensor_model_parallel_world_size
# Default name for the model parallel rng tracker.
......@@ -45,8 +45,8 @@ def init_checkpointed_activations_memory_buffer():
"""Initializ the memory buffer for the checkpointed activations."""
args = get_args()
per_layer = args.batch_size * args.max_position_embeddings * \
args.hidden_size // args.model_parallel_size
per_layer = args.micro_batch_size * args.max_position_embeddings * \
args.hidden_size // args.tensor_model_parallel_size
assert args.num_layers % args.checkpoint_num_layers == 0, \
'number of layers is not divisible by checkpoint-num-layers'
num_checkpointer_layers = args.num_layers // args.checkpoint_num_layers
......@@ -54,7 +54,7 @@ def init_checkpointed_activations_memory_buffer():
dtype = torch.half
if not args.fp16:
dtype = torch.float
global _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER
assert _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is None, \
'checkpointed activations memory buffer is already allocated.'
......@@ -104,15 +104,15 @@ def _set_cuda_rng_state(new_state, device=-1):
def split_tensor_into_1d_equal_chunks(tensor):
"""Break a tensor into equal 1D chunks."""
data = tensor.view(-1)
partition_size = torch.numel(data) // get_model_parallel_world_size()
start_index = partition_size * get_model_parallel_rank()
partition_size = torch.numel(data) // get_tensor_model_parallel_world_size()
start_index = partition_size * get_tensor_model_parallel_rank()
end_index = start_index + partition_size
return data[start_index:end_index]
def gather_split_1d_tensor(tensor):
"""Opposite of above function, gather values from model parallel ranks."""
world_size = get_model_parallel_world_size()
world_size = get_tensor_model_parallel_world_size()
numel = torch.numel(tensor)
numel_gathered = world_size * numel
gathered = torch.empty(numel_gathered, dtype=tensor.dtype,
......@@ -120,7 +120,7 @@ def gather_split_1d_tensor(tensor):
requires_grad=False)
chunks = [gathered[i*numel:(i+1)*numel] for i in range(world_size)]
torch.distributed.all_gather(chunks, tensor,
group=get_model_parallel_group())
group=get_tensor_model_parallel_group())
return gathered
......@@ -215,15 +215,15 @@ def model_parallel_cuda_manual_seed(seed):
default state: This is for data parallelism and is the same among a
set of model parallel GPUs but different across
different model paralle groups. This is used for
example for dropout in the non-model-parallel regions.
model-parallel state: This state is different among a set of model
example for dropout in the non-tensor-model-parallel regions.
tensor-model-parallel state: This state is different among a set of model
parallel GPUs, but the same across data parallel
groups. This is used for example for dropout in
model parallel regions.
"""
# 2718 is just for fun and any POSITIVE value will work.
offset = seed + 2718
model_parallel_seed = offset + get_model_parallel_rank()
tensor_model_parallel_seed = offset + get_tensor_model_parallel_rank()
# Data parallel gets the original seed.
data_parallel_seed = seed
......@@ -231,15 +231,15 @@ def model_parallel_cuda_manual_seed(seed):
print('> initializing model parallel cuda seeds on global rank {}, '
'model parallel rank {}, and data parallel rank {} with '
'model parallel seed: {} and data parallel seed: {}'.format(
torch.distributed.get_rank(), get_model_parallel_rank(),
get_data_parallel_rank(), model_parallel_seed,
torch.distributed.get_rank(), get_tensor_model_parallel_rank(),
get_data_parallel_rank(), tensor_model_parallel_seed,
data_parallel_seed), flush=True)
_CUDA_RNG_STATE_TRACKER.reset()
# Set the default state.
torch.cuda.manual_seed(data_parallel_seed)
# and model parallel state.
_CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME,
model_parallel_seed)
tensor_model_parallel_seed)
class CheckpointFunction(torch.autograd.Function):
......@@ -268,11 +268,11 @@ class CheckpointFunction(torch.autograd.Function):
args[0].data = split_tensor_into_1d_equal_chunks(args[0].data)
args[0].data = _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER.add(
args[0].data)
# Store everything.
ctx.save_for_backward(*args)
return outputs
@staticmethod
......
megatron/mpu/tests/test_cross_entropy.py
View file @ 9b174da8
......@@ -47,7 +47,7 @@ def mpu_cross_entropy(batch_size, seq_length, vocab_size,
identity = IdentityLayer((batch_size, seq_length, vocab_size),
scale=logits_scale).cuda()
logits = identity()
logits_parallel = mpu.scatter_to_model_parallel_region(logits)
logits_parallel = mpu.scatter_to_tensor_model_parallel_region(logits)
target = torch.cuda.LongTensor(
size=(batch_size, seq_length)).random_(0, vocab_size)
loss = vocab_parallel_cross_entropy(logits_parallel, target).mean()
......@@ -55,20 +55,20 @@ def mpu_cross_entropy(batch_size, seq_length, vocab_size,
return loss, identity.weight.grad
def test_cross_entropy(model_parallel_size):
def test_cross_entropy(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing cross entropy with model parallel size {} ...'.
format(model_parallel_size))
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
batch_size = 13
seq_length = 17
vocab_size_per_partition = 11
logits_scale = 1000.0
vocab_size = vocab_size_per_partition * model_parallel_size
vocab_size = vocab_size_per_partition * tensor_model_parallel_size
seed = 1234
loss_torch, grad_torch = torch_cross_entropy(batch_size, seq_length,
......@@ -89,7 +89,7 @@ def test_cross_entropy(model_parallel_size):
assert error < 1.0e-6
# Reset groups
mpu.destroy_model_parallel()
mpu.destroy_tensor_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
......@@ -101,8 +101,8 @@ if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test cross entropy')
test_cross_entropy(model_parallel_size)
model_parallel_size *= 2
test_cross_entropy(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
megatron/mpu/tests/test_data.py
View file @ 9b174da8
......@@ -24,15 +24,15 @@ import sys
sys.path.append("../..")
def test_boradcast_data(model_parallel_size):
def test_broadcast_data(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing boradcast_data with model parallel size {} ...'.
format(model_parallel_size))
print('> testing broadcast_data with model parallel size {} ...'.
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
mpu.initialize_model_parallel(tensor_model_parallel_size)
torch.manual_seed(1234 + mpu.get_data_parallel_rank())
model_parallel_size = mpu.get_model_parallel_world_size()
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
key_size_t = {'key1': [7, 11],
'key2': [8, 2, 1],
......@@ -48,7 +48,7 @@ def test_boradcast_data(model_parallel_size):
data_t[key] = data[key].clone()
data['keyX'] = torch.FloatTensor(size=(5, )).random_(0, 1000)
data_t['keyX'] = data['keyX'].clone()
if mpu.get_model_parallel_rank() != 0:
if mpu.get_tensor_model_parallel_rank() != 0:
data = None
data_utils._check_data_types(keys, data_t, torch.int64)
......@@ -69,7 +69,7 @@ def test_boradcast_data(model_parallel_size):
assert data_b[key].sub(tensor).abs().max() == 0
# Reset groups
mpu.destroy_model_parallel()
mpu.destroy_tensor_model_parallel()
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
......@@ -81,8 +81,8 @@ if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
print_separator('test test boradcast data')
test_boradcast_data(model_parallel_size)
model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test test broadcast data')
test_broadcast_data(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
megatron/mpu/tests/test_initialize.py
View file @ 9b174da8
......@@ -21,15 +21,15 @@ import sys
sys.path.append("../..")
def test_initialize_model_parallel(model_parallel_size):
def test_initialize_model_parallel(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing initialize_model_parallel with size {} ...'.format(
model_parallel_size))
model_parallel_size_ = min(model_parallel_size,
tensor_model_parallel_size))
tensor_model_parallel_size_ = min(tensor_model_parallel_size,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(model_parallel_size_)
mpu.initialize_model_parallel(tensor_model_parallel_size_)
assert mpu.model_parallel_is_initialized()
# Checks.
......@@ -38,15 +38,15 @@ def test_initialize_model_parallel(model_parallel_size):
assert rank == torch.distributed.get_rank(group=group)
# Model parallel.
world_size = model_parallel_size_
rank = torch.distributed.get_rank() % model_parallel_size_
assert world_size == mpu.get_model_parallel_world_size()
assert rank == mpu.get_model_parallel_rank()
check(mpu.get_model_parallel_group(), world_size, rank)
world_size = tensor_model_parallel_size_
rank = torch.distributed.get_rank() % tensor_model_parallel_size_
assert world_size == mpu.get_tensor_model_parallel_world_size()
assert rank == mpu.get_tensor_model_parallel_rank()
check(mpu.get_tensor_model_parallel_group(), world_size, rank)
# Data parallel.
world_size = torch.distributed.get_world_size() // model_parallel_size_
rank = torch.distributed.get_rank() // model_parallel_size
world_size = torch.distributed.get_world_size() // tensor_model_parallel_size_
rank = torch.distributed.get_rank() // tensor_model_parallel_size
assert world_size == mpu.get_data_parallel_world_size()
assert rank == mpu.get_data_parallel_rank()
check(mpu.get_data_parallel_group(), world_size, rank)
......@@ -59,20 +59,20 @@ def test_initialize_model_parallel(model_parallel_size):
print('>> passed the test :-)')
def test_get_model_parallel_src_rank(model_parallel_size_):
def test_get_tensor_model_parallel_src_rank(tensor_model_parallel_size_):
if torch.distributed.get_rank() == 0:
print('> testing get_model_parallel_src_rank with size {} ...'.format(
model_parallel_size_))
model_parallel_size = min(model_parallel_size_,
print('> testing get_tensor_model_parallel_src_rank with size {} ...'.format(
tensor_model_parallel_size_))
tensor_model_parallel_size = min(tensor_model_parallel_size_,
torch.distributed.get_world_size())
assert not mpu.model_parallel_is_initialized()
mpu.initialize_model_parallel(model_parallel_size)
mpu.initialize_model_parallel(tensor_model_parallel_size)
assert mpu.model_parallel_is_initialized()
# Checks
src_rank = torch.distributed.get_rank() - mpu.get_model_parallel_rank()
assert mpu.get_model_parallel_src_rank() == src_rank
src_rank = torch.distributed.get_rank() - mpu.get_tensor_model_parallel_rank()
assert mpu.get_tensor_model_parallel_src_rank() == src_rank
# Reset groups
mpu.destroy_model_parallel()
......@@ -86,10 +86,10 @@ if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test initialize model parallel')
test_initialize_model_parallel(model_parallel_size)
test_initialize_model_parallel(tensor_model_parallel_size)
print_separator('test model parallel source rank')
test_get_model_parallel_src_rank(model_parallel_size)
model_parallel_size *= 2
test_get_tensor_model_parallel_src_rank(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
megatron/mpu/tests/test_layers.py
View file @ 9b174da8
......@@ -26,14 +26,14 @@ import sys
sys.path.append("../..")
def test_parallel_embedding(model_parallel_size):
def test_parallel_embedding(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing parallel embedding with model parallel size {} ...'.
format(model_parallel_size))
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
batch_size = 17
seq_length = 23
......@@ -80,16 +80,16 @@ def test_parallel_embedding(model_parallel_size):
assert error < 1.0e-12, 'error: {}'.format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
hidden_size // model_parallel_size,
1)[mpu.get_model_parallel_rank()]
hidden_size // tensor_model_parallel_size,
1)[mpu.get_tensor_model_parallel_rank()]
error = embedding_parallel.weight.grad.sub(weight_grad_orig).abs().max()
print(' error in grad (parallel) on global rank {}: {}'.format(
torch.distributed.get_rank(), error))
assert error < 1.0e-12, 'error: {}'.format(error)
weight_grad_orig = torch.split(embedding_original.weight.grad,
vocab_size // model_parallel_size,
0)[mpu.get_model_parallel_rank()]
vocab_size // tensor_model_parallel_size,
0)[mpu.get_tensor_model_parallel_rank()]
error = embedding_vocab_parallel.weight.grad.sub(
weight_grad_orig).abs().max()
print(' error in grad (vocab parallel) on global rank {}: {}'.format(
......@@ -104,19 +104,19 @@ def test_parallel_embedding(model_parallel_size):
print('>> passed the test :-)')
def test_initialize_affine_weight(model_parallel_size):
def test_initialize_affine_weight(tensor_model_parallel_size):
mpu.initialize_model_parallel(model_parallel_size)
mpu.initialize_model_parallel(tensor_model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing initialize_affine_weight with model parallel '
'size: {}'.format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
'size: {}'.format(tensor_model_parallel_size))
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
input_size = input_size_coeff * tensor_model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
output_size = output_size_coeff * tensor_model_parallel_size
# ---------------
# Column parallel
......@@ -131,7 +131,7 @@ def test_initialize_affine_weight(model_parallel_size):
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_model_parallel_rank()
rank = mpu.get_tensor_model_parallel_rank()
my_weight = torch.split(master_weight, output_size_coeff,
dim=0)[rank].contiguous().clone()
......@@ -154,7 +154,7 @@ def test_initialize_affine_weight(model_parallel_size):
set_random_seed(seed)
master_weight = torch.empty(output_size, input_size)
torch.nn.init.normal_(master_weight)
rank = mpu.get_model_parallel_rank()
rank = mpu.get_tensor_model_parallel_rank()
my_weight = torch.split(master_weight, input_size_coeff,
dim=1)[rank].contiguous().clone()
......@@ -183,20 +183,20 @@ class IdentityLayer2D(torch.nn.Module):
return self.weight
def test_column_parallel_linear(model_parallel_size):
def test_column_parallel_linear(tensor_model_parallel_size):
mpu.initialize_model_parallel(model_parallel_size)
mpu.initialize_model_parallel(tensor_model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing ColumnParallelLinear with model parallel '
'size: {}'.format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
'size: {}'.format(tensor_model_parallel_size))
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
input_size = input_size_coeff * tensor_model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
output_size = output_size_coeff * tensor_model_parallel_size
batch_size = 7
# Network
......@@ -219,7 +219,7 @@ def test_column_parallel_linear(model_parallel_size):
dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1)
dLdX = torch.matmul(dLdY, A)
rank = mpu.get_model_parallel_rank()
rank = mpu.get_tensor_model_parallel_rank()
my_dLdA = torch.split(dLdA, output_size_coeff,
dim=0)[rank].contiguous().clone()
error = my_dLdA.sub(linear_layer.weight.grad).abs().max()
......@@ -250,20 +250,20 @@ def test_column_parallel_linear(model_parallel_size):
print(' >> passed the test :-)')
def test_row_parallel_linear(model_parallel_size):
def test_row_parallel_linear(tensor_model_parallel_size):
mpu.initialize_model_parallel(model_parallel_size)
mpu.initialize_model_parallel(tensor_model_parallel_size)
if torch.distributed.get_rank() == 0:
print('> testing RowParallelLinear with model parallel '
'size: {}'.format(model_parallel_size))
model_parallel_size = mpu.get_model_parallel_world_size()
'size: {}'.format(tensor_model_parallel_size))
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
input_size_coeff = 13
input_size = input_size_coeff * model_parallel_size
input_size = input_size_coeff * tensor_model_parallel_size
output_size_coeff = 17
output_size = output_size_coeff * model_parallel_size
output_size = output_size_coeff * tensor_model_parallel_size
batch_size = 7
# Network
......@@ -286,7 +286,7 @@ def test_row_parallel_linear(model_parallel_size):
dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1)
dLdX = torch.matmul(dLdY, A)
rank = mpu.get_model_parallel_rank()
rank = mpu.get_tensor_model_parallel_rank()
my_dLdA = torch.split(dLdA, input_size_coeff,
dim=1)[rank].contiguous().clone()
error = my_dLdA.sub(linear_layer.weight.grad).abs().max()
......@@ -325,11 +325,11 @@ class IdentityLayer3D(torch.nn.Module):
return self.weight
def parallel_self_attention(model_parallel_size, num_att_heads_per_partition,
def parallel_self_attention(tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size,
sequence_length):
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
......@@ -352,17 +352,17 @@ def parallel_self_attention(model_parallel_size, num_att_heads_per_partition,
# Backward
loss.backward()
rank = mpu.get_model_parallel_rank()
rank = mpu.get_tensor_model_parallel_rank()
mpu.destroy_model_parallel()
return rank, hidden_size, model_parallel_size, loss, \
return rank, hidden_size, tensor_model_parallel_size, loss, \
attention_layer, identity_layer
def test_parallel_self_attention(model_parallel_size):
def test_parallel_self_attention(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing ParallelSelfAttention with model parallel '
'size: {}'.format(model_parallel_size))
'size: {}'.format(tensor_model_parallel_size))
num_att_heads_per_partition = 3
hidden_size_per_att_head = 7
......@@ -370,14 +370,14 @@ def test_parallel_self_attention(model_parallel_size):
batch_size = 5
sequence_length = 13
rank_1, hideen_size_1, model_parallel_size_1, loss_1, \
rank_1, hideen_size_1, tensor_model_parallel_size_1, loss_1, \
attention_layer_1, identity_layer_1 = parallel_self_attention(
1, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size, sequence_length)
rank, hidden_size, model_parallel_size, loss, \
rank, hidden_size, tensor_model_parallel_size, loss, \
attention_layer, identity_layer = parallel_self_attention(
model_parallel_size, num_att_heads_per_partition,
tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, dropout_prob, batch_size, sequence_length)
assert hideen_size_1 == hidden_size
......@@ -389,7 +389,7 @@ def test_parallel_self_attention(model_parallel_size):
my_lin_grad_list = torch.split(
attention_layer_1.query_key_value.weight.grad,
hidden_size // model_parallel_size, 0)[rank::model_parallel_size]
hidden_size // tensor_model_parallel_size, 0)[rank::tensor_model_parallel_size]
my_lin_grad = torch.cat(my_lin_grad_list, dim=0)
error = my_lin_grad.sub(
attention_layer.query_key_value.weight.grad).abs().max()
......@@ -410,11 +410,11 @@ def test_parallel_self_attention(model_parallel_size):
print(' >> passed the test :-)')
def parallel_transformer(model_parallel_size, num_att_heads_per_partition,
def parallel_transformer(tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length):
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed = 12345
set_random_seed(seed)
......@@ -440,31 +440,31 @@ def parallel_transformer(model_parallel_size, num_att_heads_per_partition,
# Backward
loss.backward()
rank = mpu.get_model_parallel_rank()
rank = mpu.get_tensor_model_parallel_rank()
mpu.destroy_model_parallel()
return rank, hidden_size, model_parallel_size, loss, \
return rank, hidden_size, tensor_model_parallel_size, loss, \
transformer_layer, identity_layer
def test_parallel_transformer_layer(model_parallel_size):
def test_parallel_transformer_layer(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing ParallelTransformerLayer with model parallel '
'size: {}'.format(model_parallel_size))
'size: {}'.format(tensor_model_parallel_size))
num_att_heads_per_partition = 3
hidden_size_per_att_head = 7
batch_size = 5
sequence_length = 13
rank_1, hidden_size_1, model_parallel_size_1, loss_1, \
rank_1, hidden_size_1, tensor_model_parallel_size_1, loss_1, \
transformer_layer_1, identity_layer_1 = parallel_transformer(
1, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length)
rank, hidden_size, model_parallel_size, loss, \
rank, hidden_size, tensor_model_parallel_size, loss, \
transformer_layer, identity_layer = parallel_transformer(
model_parallel_size, num_att_heads_per_partition,
tensor_model_parallel_size, num_att_heads_per_partition,
hidden_size_per_att_head, batch_size, sequence_length)
error = loss_1.sub(loss).abs().max()
......@@ -494,37 +494,37 @@ if __name__ == '__main__':
world_size = torch.distributed.get_world_size()
print_separator('test initialize affine weight')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_initialize_affine_weight(model_parallel_size)
model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_initialize_affine_weight(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
model_parallel_size = 1
while model_parallel_size <= world_size:
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test parallel embedding')
test_parallel_embedding(model_parallel_size)
model_parallel_size *= 2
test_parallel_embedding(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test column-parallel linear')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_column_parallel_linear(model_parallel_size)
model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_column_parallel_linear(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test row-parallel linear')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_row_parallel_linear(model_parallel_size)
model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_row_parallel_linear(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test parallel self-attention')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_parallel_self_attention(model_parallel_size)
model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_parallel_self_attention(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
print_separator('test parallel transformer')
model_parallel_size = 1
while model_parallel_size <= world_size:
test_parallel_transformer_layer(model_parallel_size)
model_parallel_size *= 2
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
test_parallel_transformer_layer(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
megatron/mpu/tests/test_random.py
View file @ 9b174da8
......@@ -21,14 +21,14 @@ import sys
sys.path.append("../..")
def test_set_cuda_rng_state(model_parallel_size):
def test_set_cuda_rng_state(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing set_rng_state with size {} ...'.
format(model_parallel_size))
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
size = 123
seed = 1234
......@@ -83,14 +83,14 @@ def test_set_cuda_rng_state(model_parallel_size):
print('>> passed the test :-)')
def test_cuda_rng_tracker(model_parallel_size):
def test_cuda_rng_tracker(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing cuda rng tracker with size {} ...'.
format(model_parallel_size))
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
seed_1 = 1234
seed_2 = 4321
......@@ -154,20 +154,20 @@ def test_cuda_rng_tracker(model_parallel_size):
print('>> passed the test :-)')
def test_model_parallel_cuda_manual_seed(model_parallel_size):
def test_model_parallel_cuda_manual_seed(tensor_model_parallel_size):
if torch.distributed.get_rank() == 0:
print('> testing model parallel cuda manual seed with size {} ...'.
format(model_parallel_size))
format(tensor_model_parallel_size))
mpu.initialize_model_parallel(model_parallel_size)
model_parallel_size = mpu.get_model_parallel_world_size()
mpu.initialize_model_parallel(tensor_model_parallel_size)
tensor_model_parallel_size = mpu.get_tensor_model_parallel_world_size()
mpu.model_parallel_cuda_manual_seed(12345)
assert torch.cuda.initial_seed() == 12345
with mpu.get_cuda_rng_tracker().fork():
assert torch.cuda.initial_seed() == (12345 + 2718 +
mpu.get_model_parallel_rank())
mpu.get_tensor_model_parallel_rank())
# Reset the tracker
mpu.get_cuda_rng_tracker().reset()
......@@ -185,20 +185,20 @@ if __name__ == '__main__':
initialize_distributed()
world_size = torch.distributed.get_world_size()
model_parallel_size = 1
while model_parallel_size <= world_size:
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test set rng state')
test_set_cuda_rng_state(model_parallel_size)
model_parallel_size *= 2
test_set_cuda_rng_state(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
model_parallel_size = 1
while model_parallel_size <= world_size:
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test cuda rng tracker')
test_cuda_rng_tracker(model_parallel_size)
model_parallel_size *= 2
test_cuda_rng_tracker(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
model_parallel_size = 1
while model_parallel_size <= world_size:
tensor_model_parallel_size = 1
while tensor_model_parallel_size <= world_size:
print_separator('test model parallel cuda manual seed')
test_model_parallel_cuda_manual_seed(model_parallel_size)
model_parallel_size *= 2
test_model_parallel_cuda_manual_seed(tensor_model_parallel_size)
tensor_model_parallel_size *= 2
megatron/text_generation_utils.py
View file @ 9b174da8
......@@ -26,6 +26,7 @@ import torch.nn.functional as F
from megatron import get_args
from megatron import get_tokenizer
from megatron import mpu
from megatron.training import communicate
from megatron.utils import get_ltor_masks_and_position_ids
......@@ -35,7 +36,7 @@ def get_batch(context_tokens):
tokenizer = get_tokenizer()
# Move to GPU.
tokens = context_tokens.view(args.batch_size, -1).contiguous().cuda()
tokens = context_tokens.view(args.micro_batch_size, -1).contiguous().cuda()
# Get the attention mask and postition ids.
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
tokens,
......@@ -88,14 +89,14 @@ def generate_samples_input_from_file(model):
# Read the sample file and open the output file.
assert args.sample_input_file is not None, \
'sample input file is not provided.'
if mpu.get_model_parallel_rank() == 0:
if mpu.is_pipeline_first_stage() and mpu.get_tensor_model_parallel_rank() == 0:
fname = open(args.sample_input_file, "r")
all_raw_text = fname.readlines()
input_count = len(all_raw_text)
input_pos = 0
if args.sample_output_file is None:
sample_output_file = args.sample_input_file + ".out"
print('could not find `sample-output-file`, setting '
print('`sample-output-file` not specified, setting '
'it to {}'.format(sample_output_file))
else:
sample_output_file = args.sample_output_file
......@@ -105,14 +106,16 @@ def generate_samples_input_from_file(model):
model.eval()
with torch.no_grad():
while True:
torch.distributed.barrier(group=mpu.get_model_parallel_group())
terminate_runs = 0
raw_text_len = 0
if mpu.get_model_parallel_rank() == 0:
if mpu.is_pipeline_first_stage() \
and mpu.get_tensor_model_parallel_rank() == 0:
raw_text = all_raw_text[input_pos]
input_pos += 1
if input_pos == input_count:
raw_text = "stop"
raw_text_len = len(raw_text)
if "stop" in raw_text:
terminate_runs = 1
......@@ -127,38 +130,60 @@ def generate_samples_input_from_file(model):
continue
else:
context_tokens = tokenizer.tokenize("EMPTY TEXT")
context_length = len(context_tokens)
context_length = 0
terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs])
torch.distributed.broadcast(terminate_runs_tensor,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
terminate_runs = terminate_runs_tensor[0].item()
input_info = [terminate_runs, raw_text_len, context_length]
input_info_tensor = torch.cuda.LongTensor(input_info)
torch.distributed.all_reduce(input_info_tensor,
group=mpu.get_model_parallel_group())
terminate_runs = input_info_tensor[0].item()
raw_text_len = input_info_tensor[1].item()
context_length = input_info_tensor[2].item()
if terminate_runs == 1:
return
# For pipeline parallel we send context tokens to other stages
# so they get the lengths correct
if mpu.get_tensor_model_parallel_rank() == 0 \
and args.pipeline_model_parallel_size > 1:
if mpu.is_pipeline_first_stage():
src = mpu.get_pipeline_model_parallel_first_rank()
group = mpu.get_pipeline_model_parallel_group()
context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
torch.distributed.broadcast(context_tokens_tensor, src, group)
else:
src = mpu.get_pipeline_model_parallel_first_rank()
group = mpu.get_pipeline_model_parallel_group()
context_tokens_tensor = torch.empty(context_length,
dtype=torch.int64,
device=torch.device("cuda"))
torch.distributed.broadcast(context_tokens_tensor, src, group)
context_tokens = context_tokens_tensor.cpu().numpy().tolist()
token_stream = get_token_stream(model, [context_tokens])
for _, decode_tokens in enumerate(token_stream):
decode_tokens, _ = decode_tokens
decode_tokens = decode_tokens[0].cpu().numpy().tolist()
pass
if mpu.get_model_parallel_rank() == 0:
os.system('clear')
print("\nContext:", raw_text, flush=True)
trim_decode_tokens = tokenizer.detokenize(
decode_tokens)[len(raw_text):]
print("\nMegatron-LM:", trim_decode_tokens, flush=True)
if mpu.get_tensor_model_parallel_rank() == 0:
if mpu.is_pipeline_first_stage():
os.system('clear')
print("\nContext:", raw_text, flush=True)
fname_out.write("\nContext:")
fname_out.write(raw_text)
fname_out.write("\n\nMegatron-LM:")
fname_out.write(trim_decode_tokens)
fname_out.write("\n")
fname_out.write("\nContext:")
fname_out.write(raw_text)
raw_text = None
decode_tokens, _ = decode_tokens
decode_tokens = decode_tokens[0].cpu().numpy().tolist()
trim_decode_tokens = tokenizer.detokenize(
decode_tokens)[raw_text_len:]
print("\nMegatron-LM:", trim_decode_tokens, flush=True)
fname_out.write("\n\nMegatron-LM:")
fname_out.write(trim_decode_tokens)
fname_out.write("\n")
torch.distributed.barrier(group=mpu.get_model_parallel_group())
raw_text = None
context_count += 1
......@@ -171,15 +196,17 @@ def generate_samples_interactive(model, print_frequency=24):
model.eval()
with torch.no_grad():
while True:
torch.distributed.barrier(group=mpu.get_model_parallel_group())
terminate_runs = 0
raw_text_len = 0
if mpu.get_model_parallel_rank() == 0:
if mpu.is_pipeline_first_stage() \
and mpu.get_tensor_model_parallel_rank() == 0:
os.system('clear')
raw_text = input("\nContext prompt (stop to exit) >>> ")
while not raw_text:
print('Prompt should not be empty!')
raw_text = input("\nContext prompt (stop to exit) >>> ")
raw_text_len = len(raw_text)
if "stop" in raw_text:
terminate_runs = 1
......@@ -194,43 +221,71 @@ def generate_samples_interactive(model, print_frequency=24):
continue
else:
context_tokens = tokenizer.tokenize("EMPTY TEXT")
context_length = len(context_tokens)
context_length = 0
terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs])
torch.distributed.broadcast(terminate_runs_tensor,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
terminate_runs = terminate_runs_tensor[0].item()
input_info = [terminate_runs, raw_text_len, context_length]
input_info_tensor = torch.cuda.LongTensor(input_info)
torch.distributed.all_reduce(input_info_tensor,
group=mpu.get_model_parallel_group())
terminate_runs = input_info_tensor[0].item()
raw_text_len = input_info_tensor[1].item()
context_length = input_info_tensor[2].item()
if terminate_runs == 1:
return
# For pipeline parallel we send context tokens to other stages
# so they get the lengths correct
if mpu.get_tensor_model_parallel_rank() == 0 \
and args.pipeline_model_parallel_size > 1:
if mpu.is_pipeline_first_stage():
src = mpu.get_pipeline_model_parallel_first_rank()
group = mpu.get_pipeline_model_parallel_group()
context_tokens_tensor = torch.cuda.LongTensor(context_tokens)
torch.distributed.broadcast(context_tokens_tensor, src, group)
else:
src = mpu.get_pipeline_model_parallel_first_rank()
group = mpu.get_pipeline_model_parallel_group()
context_tokens_tensor = torch.empty(context_length,
dtype=torch.int64,
device=torch.device("cuda"))
torch.distributed.broadcast(context_tokens_tensor, src, group)
context_tokens = context_tokens_tensor.cpu().numpy().tolist()
token_stream = get_token_stream(model, [context_tokens])
for counter, decode_tokens in enumerate(token_stream):
if counter % print_frequency != 0 \
or mpu.get_tensor_model_parallel_rank() != 0 \
or not mpu.is_pipeline_first_stage():
continue
os.system('clear')
print("\nContext:", raw_text, flush=True)
decode_tokens, _ = decode_tokens
decode_tokens = decode_tokens[0].cpu().numpy().tolist()
trim_decode_tokens = tokenizer.detokenize(
decode_tokens)[raw_text_len:]
print("\nMegatron-LM:", trim_decode_tokens, flush=True)
if mpu.get_model_parallel_rank() == 0 and \
counter % print_frequency == 0:
os.system('clear')
print("\nContext:", raw_text, flush=True)
trim_decode_tokens = tokenizer.detokenize(
decode_tokens)[len(raw_text):]
print("\nMegatron-LM:", trim_decode_tokens, flush=True)
if mpu.get_model_parallel_rank() == 0:
if mpu.is_pipeline_first_stage() \
and mpu.get_tensor_model_parallel_rank() == 0:
os.system('clear')
print("\nContext:", raw_text, flush=True)
if not isinstance(decode_tokens, list):
decode_tokens, _ = decode_tokens
decode_tokens = decode_tokens[0].cpu().numpy().tolist()
trim_decode_tokens = tokenizer.detokenize(
decode_tokens)[len(raw_text):]
decode_tokens)[raw_text_len:]
print("\nMegatron-LM:", trim_decode_tokens, flush=True)
input("\nPress Enter to continue >>>")
raw_text = None
torch.distributed.barrier(group=mpu.get_model_parallel_group())
context_count += 1
if mpu.get_model_parallel_rank() == 0:
input("\nPress any key to continue >>>")
def generate_samples_unconditional(model):
......@@ -240,29 +295,38 @@ def generate_samples_unconditional(model):
num_samples = args.num_samples
context_tokens = [[tokenizer.eod]
for _ in range(args.batch_size)]
for _ in range(args.micro_batch_size)]
ctr = 0
while True:
start_time = time.time()
for token_stream in get_token_stream(model,
copy.deepcopy(context_tokens)):
pass
if ctr % args.log_interval == 0:
print('Avg s/batch:',
(time.time() - start_time) / min(args.log_interval, ctr + 1))
start_time = time.time()
length = len(token_stream)
token_batch = token_stream[0].cpu().numpy().tolist()
length_batch = token_stream[1].cpu().numpy().tolist()
for tokens, length in zip(token_batch, length_batch):
tokens = tokens[1:length - 1]
text = tokenizer.detokenize(tokens)
is_finished = length < args.seq_length - 1
datum = {'text': text, 'length': length - 1, 'finished': is_finished}
yield datum
ctr += 1
if ctr >= num_samples:
break
if mpu.is_pipeline_last_stage() and \
mpu.get_tensor_model_parallel_rank() == 0:
if ctr % args.log_interval == 0:
print('Avg s/batch:',
(time.time() - start_time) / min(args.log_interval, ctr + 1))
start_time = time.time()
length = len(token_stream)
token_batch = token_stream[0].cpu().numpy().tolist()
length_batch = token_stream[1].cpu().numpy().tolist()
assert len(length_batch) == args.micro_batch_size
for tokens, length in zip(token_batch, length_batch):
tokens = tokens[1:length - 1]
text = tokenizer.detokenize(tokens)
is_finished = length < args.seq_length - 1
datum = {'text': text, 'length': length - 1, 'finished': is_finished}
yield datum
ctr += 1
if ctr >= num_samples:
break
else:
for _ in range(args.micro_batch_size):
yield None
ctr += 1
if ctr >= num_samples:
break
if ctr >= num_samples:
break
......@@ -273,7 +337,9 @@ def generate_and_write_samples_unconditional(model):
assert args.genfile is not None
with open(args.genfile, 'w') as f:
for datum in generate_samples_unconditional(model):
f.write(json.dumps(datum) + '\n')
if mpu.is_pipeline_last_stage() and \
mpu.get_tensor_model_parallel_rank() == 0:
f.write(json.dumps(datum) + '\n')
def pad_batch(batch, pad_id, args):
......@@ -299,11 +365,11 @@ def get_token_stream(model, context_tokens):
context_length_tensor = torch.cuda.LongTensor(context_lengths)
torch.distributed.broadcast(context_length_tensor,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
mpu.get_tensor_model_parallel_src_rank(),
group=mpu.get_tensor_model_parallel_group())
torch.distributed.broadcast(context_tokens_tensor,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
mpu.get_tensor_model_parallel_src_rank(),
group=mpu.get_tensor_model_parallel_group())
context_length = context_length_tensor.min().item()
tokens, attention_mask, position_ids = get_batch(context_tokens_tensor)
......@@ -313,7 +379,10 @@ def get_token_stream(model, context_tokens):
attention_mask, position_ids)
for tokens, lengths in batch_token_iterator:
context_length += 1
yield tokens[:, :context_length], lengths
if tokens is not None:
yield tokens[:, :context_length], lengths
else:
yield None, None
def switch(val1, val2, boolean):
......@@ -322,6 +391,66 @@ def switch(val1, val2, boolean):
return (1 - boolean) * val1 + boolean * val2
def forward_step(model, tokens, position_ids, attention_mask, tokentype_ids,
layer_past=None, get_key_value=None,
forward_method_parallel_output=None):
# Hidden size changes when not using recompute, need to tell communicate()
# the correct size
args = get_args()
orig_seq_length = args.seq_length
args.seq_length = tokens.shape[1]
if not mpu.is_pipeline_first_stage():
input_tensor, _ = communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_forward=True,
recv_backward=False)
else:
input_tensor = None
# Forward pass through the model.
if mpu.is_pipeline_first_stage():
assert input_tensor is None
if mpu.is_pipeline_last_stage():
output_tensor = model(tokens, position_ids, attention_mask,
tokentype_ids=tokentype_ids,
layer_past=layer_past,
get_key_value=get_key_value,
forward_method_parallel_output=forward_method_parallel_output)
else:
output_tensor = model(tokens, position_ids, attention_mask,
tokentype_ids=tokentype_ids,
layer_past=layer_past,
get_key_value=get_key_value)
elif mpu.is_pipeline_last_stage():
assert input_tensor is not None
output_tensor = model(input_tensor, attention_mask,
layer_past=layer_past,
get_key_value=get_key_value,
forward_method_parallel_output=forward_method_parallel_output)
else:
assert input_tensor is not None
output_tensor = model(input_tensor, attention_mask,
layer_past=layer_past,
get_key_value=get_key_value)
if get_key_value:
output_tensor, layer_past = output_tensor
if not mpu.is_pipeline_last_stage():
communicate(tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_forward=False,
recv_backward=False)
args.seq_length = orig_seq_length
if get_key_value:
return output_tensor, layer_past
return output_tensor
def sample_sequence_batch(model, context_tokens, context_lengths,
attention_mask, position_ids,
maxlen=None, type_ids=None):
......@@ -349,14 +478,15 @@ def sample_sequence_batch(model, context_tokens, context_lengths,
lengths = torch.ones([batch_size]).long().cuda() * maxlen
while context_length <= (maxlen):
if args.recompute:
logits = model(tokens,
position_ids,
attention_mask,
tokentype_ids=type_ids,
forward_method_parallel_output=False)
logits = logits[:, context_length - 1, :]
output = forward_step(model, tokens,
position_ids,
attention_mask,
tokentype_ids=type_ids,
forward_method_parallel_output=False)
if mpu.is_pipeline_last_stage():
assert output is not None
logits = output[:, context_length - 1, :]
else:
types2use = None
if counter == 0:
......@@ -372,41 +502,65 @@ def sample_sequence_batch(model, context_tokens, context_lengths,
if type_ids is not None:
types2use = type_ids[:, context_length - 1].view(
batch_size, -1)
logits, layer_past = model(tokens2use,
positions2use,
attention_mask,
layer_past=layer_past,
get_key_value=True,
tokentype_ids=types2use,
forward_method_parallel_output=False)
logits = logits[:, -1].view(batch_size, -1).contiguous()
if args.greedy:
prev = torch.argmax(logits, dim=-1).view(-1)
output, layer_past = forward_step(model, tokens2use,
positions2use,
attention_mask,
layer_past=layer_past,
get_key_value=True,
tokentype_ids=types2use,
forward_method_parallel_output=False)
if mpu.is_pipeline_last_stage():
assert output is not None
logits = output[:, -1].view(batch_size, -1).contiguous()
if mpu.is_pipeline_last_stage():
if args.greedy:
prev = torch.argmax(logits, dim=-1).view(-1)
else:
logits = logits.float()
logits /= args.temperature
logits = top_k_logits(logits, top_k=args.top_k,
top_p=args.top_p)
log_probs = F.softmax(logits, dim=-1)
prev = torch.multinomial(log_probs, num_samples=1).view(-1)
started = context_lengths <= context_length
new_tokens = switch(
tokens[:, context_length].view(-1), prev, started)
tokens[:, context_length] = new_tokens
src = mpu.get_pipeline_model_parallel_last_rank()
group = mpu.get_embedding_group()
torch.distributed.broadcast(new_tokens, src, group)
done_token = (prev == eos_id).byte() & started.byte()
just_finished = (done_token & ~is_done).bool()
lengths[just_finished.view(-1)] = context_length
is_done = is_done | done_token
done = torch.all(is_done)
src = mpu.get_pipeline_model_parallel_last_rank()
group = mpu.get_pipeline_model_parallel_group()
torch.distributed.broadcast(done, src, group)
yield tokens, lengths
else:
logits = logits.float()
logits /= args.temperature
logits = top_k_logits(logits, top_k=args.top_k,
top_p=args.top_p)
log_probs = F.softmax(logits, dim=-1)
prev = torch.multinomial(log_probs, num_samples=1).view(-1)
print_logits = []
for p in prev:
print_logits.append([logits[i, p].item()
for i in range(batch_size)])
started = context_lengths <= context_length
tokens[:, context_length] = switch(
tokens[:, context_length].view(-1), prev, started)
context_length += 1
counter += 1
if mpu.is_pipeline_first_stage():
src = mpu.get_pipeline_model_parallel_last_rank()
group = mpu.get_embedding_group()
new_tokens = torch.empty_like(tokens[:, context_length])
torch.distributed.broadcast(new_tokens, src, group)
tokens[:, context_length] = new_tokens
yield tokens, None
else:
yield None, None
done_token = (prev == eos_id).byte() & started.byte()
just_finished = (done_token & ~is_done).bool()
lengths[just_finished.view(-1)] = context_length
is_done = is_done | done_token
done = torch.all(is_done)
done = torch.cuda.ByteTensor([0])
src = mpu.get_pipeline_model_parallel_last_rank()
group = mpu.get_pipeline_model_parallel_group()
torch.distributed.broadcast(done, src, group)
yield tokens, lengths
context_length += 1
counter += 1
if done:
break
megatron/tokenizer/tokenizer.py
View file @ 9b174da8
......@@ -56,7 +56,7 @@ def _vocab_size_with_padding(orig_vocab_size, args):
after = orig_vocab_size
multiple = args.make_vocab_size_divisible_by * \
args.model_parallel_size
args.tensor_model_parallel_size
while (after % multiple) != 0:
after += 1
if args.rank == 0:
......
megatron/training.py
View file @ 9b174da8
......@@ -18,6 +18,10 @@
from datetime import datetime
import math
import sys
import time
# The earliest we can measure the start time.
_TRAIN_START_TIME = time.time()
import torch
from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP
from apex.optimizers import FusedAdam as Adam
......@@ -25,13 +29,19 @@ from apex.optimizers import FusedAdam as Adam
from megatron import get_args
from megatron import get_timers
from megatron import get_tensorboard_writer
from megatron import get_current_global_batch_size
from megatron import get_num_microbatches
from megatron import is_last_rank
from megatron import update_num_microbatches
from megatron import mpu
from megatron import print_rank_0
from megatron import print_rank_last
from megatron.checkpointing import load_checkpoint
from megatron.checkpointing import save_checkpoint
from megatron.fp16 import FP16_Module
from megatron.fp16 import FP16_Optimizer
from megatron.initialize import initialize_megatron
from megatron.initialize import write_args_to_tensorboard
from megatron.learning_rates import AnnealingLR
from megatron.model import DistributedDataParallel as LocalDDP
from megatron.model import get_params_for_weight_decay_optimization
......@@ -41,6 +51,13 @@ from megatron.data.data_loaders import build_pretraining_data_loader
from megatron.utils import report_memory
def print_datetime(string):
"""Note that this call will sync across all ranks."""
torch.distributed.barrier()
time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print_rank_0('[' + string + '] datetime: {} '.format(time_str))
def pretrain(train_valid_test_dataset_provider, model_provider,
forward_step_func, extra_args_provider=None, args_defaults={}):
"""Main training program.
......@@ -71,6 +88,18 @@ def pretrain(train_valid_test_dataset_provider, model_provider,
initialize_megatron(extra_args_provider=extra_args_provider,
args_defaults=args_defaults)
# Adjust the startup time so it reflects the largest value.
# This will be closer to what scheduler will see (outside of
# image ... launches.
global _TRAIN_START_TIME
start_time_tensor = torch.cuda.FloatTensor([_TRAIN_START_TIME])
torch.distributed.all_reduce(start_time_tensor,
op=torch.distributed.ReduceOp.MIN)
_TRAIN_START_TIME = start_time_tensor.item()
print_rank_0('time to initialize megatron (seconds): {:.3f}'.format(
time.time() - _TRAIN_START_TIME))
print_datetime('after megatron is initialized')
args = get_args()
timers = get_timers()
......@@ -78,6 +107,8 @@ def pretrain(train_valid_test_dataset_provider, model_provider,
timers('model and optimizer').start()
model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider)
timers('model and optimizer').stop()
print_datetime('after model, optimizer, and learning rate '
'scheduler are built')
# Data stuff.
timers('train/valid/test data iterators').start()
......@@ -85,6 +116,7 @@ def pretrain(train_valid_test_dataset_provider, model_provider,
= build_train_valid_test_data_iterators(
train_valid_test_dataset_provider)
timers('train/valid/test data iterators').stop()
print_datetime('after dataloaders are built')
# Print setup timing.
print_rank_0('done with setups ...')
......@@ -96,6 +128,7 @@ def pretrain(train_valid_test_dataset_provider, model_provider,
iteration = train(forward_step_func,
model, optimizer, lr_scheduler,
train_data_iterator, valid_data_iterator)
print_datetime('after training is done')
if args.do_valid:
prefix = 'the end of training for val data'
......@@ -113,6 +146,35 @@ def pretrain(train_valid_test_dataset_provider, model_provider,
test_data_iterator, model,
0, True)
def update_train_iters(args):
# For iteration-based training, we don't need to do anything
if args.train_iters:
return
# Constant batch size with sample-based training.
if args.rampup_batch_size is None:
args.train_iters = args.train_samples // args.global_batch_size
else:
# Sample based training with rampup batch size.
iterations = 0
consumed_samples = 0
# Rampup phase.
while consumed_samples <= int(args.rampup_batch_size[2]):
update_num_microbatches(consumed_samples, consistency_check=False)
consumed_samples += get_current_global_batch_size()
iterations += 1
# Reset
update_num_microbatches(0, consistency_check=False)
# Constant phase
# Note that we throw away any partial last batch.
iterations += (args.train_samples - consumed_samples) // \
args.global_batch_size
args.train_iters = iterations
print_rank_0('setting training iterations to {}'.format(args.train_iters))
def get_model(model_provider_func):
"""Build the model."""
......@@ -123,8 +185,10 @@ def get_model(model_provider_func):
# Print number of parameters.
if mpu.get_data_parallel_rank() == 0:
print(' > number of parameters on model parallel rank {}: {}'.format(
mpu.get_model_parallel_rank(),
print(' > number of parameters on (tensor, pipeline) '
'model parallel rank ({}, {}): {}'.format(
mpu.get_tensor_model_parallel_rank(),
mpu.get_pipeline_model_parallel_rank(),
sum([p.nelement() for p in model.parameters()])), flush=True)
# GPU allocation.
......@@ -134,7 +198,6 @@ def get_model(model_provider_func):
if args.fp16:
model = FP16_Module(model)
# Wrap model for distributed training."""
if args.DDP_impl == 'torch':
i = torch.cuda.current_device()
model = torchDDP(model, device_ids=[i], output_device=i,
......@@ -160,8 +223,8 @@ def get_optimizer(model):
# Add model parallel attribute if it is not set.
for param_group in param_groups:
for param in param_group['params']:
if not hasattr(param, 'model_parallel'):
param.model_parallel = False
if not hasattr(param, 'tensor_model_parallel'):
param.tensor_model_parallel = False
# Use Adam.
optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay,
......@@ -184,22 +247,39 @@ def get_learning_rate_scheduler(optimizer):
"""Build the learning rate scheduler."""
args = get_args()
# Add linear learning rate scheduler.
if args.lr_decay_iters is not None:
num_iters = args.lr_decay_iters
# Iteration-based training.
if args.train_iters:
if args.lr_decay_iters is None:
args.lr_decay_iters = args.train_iters
decay_steps = args.lr_decay_iters * args.global_batch_size
if args.lr_warmup_fraction is not None:
warmup_steps = args.lr_warmup_fraction * decay_steps
else:
warmup_steps = args.lr_warmup_iters * args.global_batch_size
# Sample-based training.
elif args.train_samples:
# We need to set training iters for later use. Technically
# we need to adjust the training samples too (due to last
# batch being incomplete) but we leave it as is for now.
update_train_iters(args)
if args.lr_decay_samples is None:
args.lr_decay_samples = args.train_samples
decay_steps = args.lr_decay_samples
if args.lr_warmup_fraction is not None:
warmup_steps = args.lr_warmup_fraction * decay_steps
else:
warmup_steps = args.lr_warmup_samples
else:
num_iters = args.train_iters
num_iters = max(1, num_iters)
init_step = 0
warmup_iter = args.warmup * num_iters
raise Exception(
'either train-iters or train-samples should be provided.')
lr_scheduler = AnnealingLR(
optimizer,
max_lr=args.lr,
min_lr=args.min_lr,
warmup_steps=warmup_iter,
decay_steps=num_iters,
warmup_steps=warmup_steps,
decay_steps=decay_steps,
decay_style=args.lr_decay_style,
num_steps=init_step,
use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler,
override_lr_scheduler=args.override_lr_scheduler)
......@@ -215,10 +295,22 @@ def setup_model_and_optimizer(model_provider_func):
lr_scheduler = get_learning_rate_scheduler(optimizer)
if args.load is not None:
timers = get_timers()
# Extra barrier is added to make sure all ranks report the
# max time.
torch.distributed.barrier()
timers('load checkpoint').start()
args.iteration = load_checkpoint(model, optimizer, lr_scheduler)
torch.distributed.barrier()
timers('load checkpoint').stop()
timers.log(['load checkpoint'])
else:
args.iteration = 0
# We only support local DDP with multiple micro-batches.
if get_num_microbatches() > 1:
assert args.DDP_impl == 'local'
# get model without FP16 and/or TorchDDP wrappers
unwrapped_model = model
while hasattr(unwrapped_model, 'module'):
......@@ -232,26 +324,304 @@ def setup_model_and_optimizer(model_provider_func):
return model, optimizer, lr_scheduler
def backward_step(optimizer, model, loss):
def communicate(tensor_send_next, tensor_send_prev, recv_forward, recv_backward):
"""Communicate tensors between stages using torch.distributed.ring_exchange(.) API."""
args = get_args()
# Create placeholder tensors for receive in forward and backward directions
# if needed.
tensor_recv_prev = None
tensor_recv_next = None
tensor_shape = (args.seq_length, args.micro_batch_size, args.hidden_size)
if recv_forward:
tensor_recv_prev = torch.empty(tensor_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=args.params_dtype)
if recv_backward:
tensor_recv_next = torch.empty(tensor_shape,
requires_grad=True,
device=torch.cuda.current_device(),
dtype=args.params_dtype)
# Send tensors in both the forward and backward directions as appropriate.
torch.distributed.ring_exchange(tensor_send_prev=tensor_send_prev,
tensor_recv_prev=tensor_recv_prev,
tensor_send_next=tensor_send_next,
tensor_recv_next=tensor_recv_next,
group=mpu.get_pipeline_model_parallel_group())
return tensor_recv_prev, tensor_recv_next
def backward_step(optimizer, model, input_tensor, output_tensor, output_tensor_grad):
"""Backward step."""
args = get_args()
timers = get_timers()
# Retain the grad on the input_tensor.
if input_tensor is not None:
input_tensor.retain_grad()
# Backward pass.
timers('backward-backward').start()
optimizer.zero_grad(set_grads_to_None=True)
if args.fp16:
optimizer.backward(loss, update_master_grads=False)
optimizer.backward(output_tensor, update_master_grads=False,
output_tensor_grad=output_tensor_grad)
else:
torch.autograd.backward(output_tensor, grad_tensors=output_tensor_grad)
# Collect the grad of the input_tensor.
input_tensor_grad = None
if input_tensor is not None:
input_tensor_grad = input_tensor.grad
return input_tensor_grad
def forward_step_with_communication(forward_step_func, data_iterator, model,
input_tensors, output_tensors,
losses_reduced, timers):
args = get_args()
if not mpu.is_pipeline_first_stage():
timers('forward-recv').start()
input_tensor, _ = communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_forward=True,
recv_backward=False)
timers('forward-recv').stop()
else:
loss.backward()
timers('backward-backward').stop()
input_tensor = None
# Forward model for one step.
timers('forward-compute').start()
output_tensor = forward_step_func(data_iterator, model, input_tensor)
timers('forward-compute').stop()
if mpu.is_pipeline_last_stage():
loss, loss_reduced = output_tensor
output_tensor = loss / get_num_microbatches()
losses_reduced.append(loss_reduced)
else:
timers('forward-send').start()
communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_forward=False,
recv_backward=False)
timers('forward-send').stop()
input_tensors.append(input_tensor)
output_tensors.append(output_tensor)
def backward_step_with_communication(optimizer, model, input_tensors, output_tensors, timers):
input_tensor = input_tensors.pop(0)
output_tensor = output_tensors.pop(0)
if mpu.is_pipeline_last_stage():
output_tensor_grad = None
else:
timers('backward-recv').start()
_, output_tensor_grad = communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_forward=False,
recv_backward=True)
timers('backward-recv').stop()
# Backward pass for one step.
timers('backward-compute').start()
input_grad_tensor = \
backward_step(optimizer, model, input_tensor, output_tensor, output_tensor_grad)
timers('backward-compute').stop()
if not mpu.is_pipeline_first_stage():
timers('backward-send').start()
communicate(
tensor_send_next=None,
tensor_send_prev=input_grad_tensor,
recv_forward=False,
recv_backward=False)
timers('backward-send').stop()
def forward_and_backward_steps_with_communication(forward_step_func, data_iterator, model,
optimizer,
input_tensor, last_microbatch,
input_tensors, output_tensors,
losses_reduced, timers):
args = get_args()
# Forward model for one step.
timers('forward-compute').start()
output_tensor = forward_step_func(data_iterator, model, input_tensor)
timers('forward-compute').stop()
if mpu.is_pipeline_last_stage():
loss, loss_reduced = output_tensor
output_tensor = loss / get_num_microbatches()
output_tensor_grad = None
losses_reduced.append(loss_reduced)
else:
timers('forward-send-backward-recv').start()
_, output_tensor_grad = communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_forward=False,
recv_backward=True)
timers('forward-send-backward-recv').stop()
input_tensors.append(input_tensor)
output_tensors.append(output_tensor)
input_tensor = input_tensors.pop(0)
output_tensor = output_tensors.pop(0)
# Backward pass for one step.
timers('backward-compute').start()
input_grad_tensor = \
backward_step(optimizer, model, input_tensor, output_tensor, output_tensor_grad)
timers('backward-compute').stop()
if not mpu.is_pipeline_first_stage():
timers('backward-send-forward-recv').start()
input_tensor, _ = communicate(
tensor_send_next=None,
tensor_send_prev=input_grad_tensor,
recv_forward=(not last_microbatch),
recv_backward=False)
timers('backward-send-forward-recv').stop()
else:
input_tensor = None
return input_tensor
def forward_backward_no_pipelining(forward_step_func, data_iterator, model,
optimizer, timers):
"""Run forward and backward passes without inter-stage communication."""
args = get_args()
losses_reduced = []
for i in range(get_num_microbatches()):
timers('forward-compute').start()
loss, loss_reduced = forward_step_func(data_iterator, model, input_tensor=None)
output_tensor = loss / get_num_microbatches()
losses_reduced.append(loss_reduced)
timers('forward-compute').stop()
timers('backward-compute').start()
output_tensor_grad = None
backward_step(optimizer, model, input_tensor=None,
output_tensor=output_tensor, output_tensor_grad=None)
timers('backward-compute').stop()
return losses_reduced
def forward_backward_pipelining(forward_step_func, data_iterator, model,
optimizer, timers):
"""Run 1F1B schedule, with communication and warmup + cooldown microbatches as needed."""
args = get_args()
# Compute number of warmup microbatches.
num_microbatches = get_num_microbatches()
num_warmup_microbatches = \
(mpu.get_pipeline_model_parallel_world_size() -
mpu.get_pipeline_model_parallel_rank() - 1)
num_warmup_microbatches = min(
num_warmup_microbatches,
num_microbatches)
num_microbatches_remaining = \
num_microbatches - num_warmup_microbatches
input_tensors = []
output_tensors = []
losses_reduced = []
# Run warmup forward passes.
for i in range(num_warmup_microbatches):
forward_step_with_communication(
forward_step_func, data_iterator, model,
input_tensors, output_tensors,
losses_reduced, timers)
# Before running 1F1B, need to receive first forward tensor.
# If all microbatches are run in warmup / cooldown phase, then no need to
# receive this tensor here.
if num_microbatches_remaining > 0:
if mpu.is_pipeline_first_stage():
input_tensor = None
else:
timers('forward-recv').start()
input_tensor, _ = communicate(tensor_send_next=None,
tensor_send_prev=None,
recv_forward=True,
recv_backward=False)
timers('forward-recv').stop()
# Run 1F1B.
for i in range(num_microbatches_remaining):
last_iteration = (i == (num_microbatches_remaining - 1))
input_tensor = \
forward_and_backward_steps_with_communication(forward_step_func, data_iterator, model,
optimizer,
input_tensor, last_iteration,
input_tensors, output_tensors,
losses_reduced, timers)
# Run cooldown backward passes.
for i in range(num_warmup_microbatches):
backward_step_with_communication(
optimizer, model, input_tensors, output_tensors, timers)
return losses_reduced
def train_step(forward_step_func, data_iterator,
model, optimizer, lr_scheduler):
"""Single training step."""
args = get_args()
timers = get_timers()
# Set grad to zero.
if args.fp16:
optimizer.zero_grad(set_grads_to_None=True)
else:
optimizer.zero_grad()
if mpu.get_pipeline_model_parallel_world_size() > 1:
losses_reduced = forward_backward_pipelining(
forward_step_func, data_iterator, model, optimizer, timers)
else:
losses_reduced = forward_backward_no_pipelining(
forward_step_func, data_iterator, model, optimizer, timers)
# All-reduce if needed.
if args.DDP_impl == 'local':
timers('backward-allreduce').start()
timers('backward-params-all-reduce').start()
model.allreduce_params(reduce_after=False,
fp32_allreduce=args.fp32_allreduce)
timers('backward-allreduce').stop()
timers('backward-params-all-reduce').stop()
# All-reduce word_embeddings' grad across first and last stages to ensure
# that word_embeddings parameters stay in sync.
# This should only run for models that support pipelined model parallelism
# (BERT and GPT-2).
timers('backward-embedding-all-reduce').start()
if (mpu.is_pipeline_first_stage() or mpu.is_pipeline_last_stage()) and \
mpu.get_pipeline_model_parallel_world_size() > 1:
unwrapped_model = model
while isinstance(unwrapped_model, (torchDDP, LocalDDP, FP16_Module)):
unwrapped_model = unwrapped_model.module
if unwrapped_model.share_word_embeddings:
word_embeddings_weight = unwrapped_model.word_embeddings_weight()
torch.distributed.all_reduce(word_embeddings_weight.grad,
group=mpu.get_embedding_group())
timers('backward-embedding-all-reduce').stop()
# Update master gradients.
timers('backward-master-grad').start()
......@@ -261,30 +631,20 @@ def backward_step(optimizer, model, loss):
# Clipping gradients helps prevent the exploding gradient.
timers('backward-clip-grad').start()
if args.clip_grad > 0:
if args.clip_grad > 0.:
if not args.fp16:
mpu.clip_grad_norm(model.parameters(), args.clip_grad)
named_parameters = model.named_parameters()
parameters = []
parameter_names = []
for parameter_name, parameter in model.named_parameters():
parameters.append(parameter)
parameter_names.append(parameter_name)
mpu.clip_grad_norm(parameters, args.clip_grad,
parameter_names=parameter_names)
else:
optimizer.clip_master_grads(args.clip_grad)
timers('backward-clip-grad').stop()
def train_step(forward_step_func, data_iterator,
model, optimizer, lr_scheduler):
"""Single training step."""
args = get_args()
timers = get_timers()
# Forward model for one step.
timers('forward').start()
loss, loss_reduced = forward_step_func(data_iterator, model)
timers('forward').stop()
# Calculate gradients, reduce across processes, and clip.
timers('backward').start()
backward_step(optimizer, model, loss)
timers('backward').stop()
# Update parameters.
timers('optimizer').start()
optimizer.step()
......@@ -293,11 +653,21 @@ def train_step(forward_step_func, data_iterator,
# Update learning rate.
skipped_iter = 0
if not (args.fp16 and optimizer.overflow):
lr_scheduler.step()
increment = get_num_microbatches() * \
args.micro_batch_size * \
args.data_parallel_size
lr_scheduler.step(increment=increment)
else:
skipped_iter = 1
return loss_reduced, skipped_iter
if mpu.is_pipeline_last_stage():
# Average loss across microbatches.
loss_reduced = {}
for key in losses_reduced[0]:
losses_reduced_for_key = [x[key] for x in losses_reduced]
loss_reduced[key] = sum(losses_reduced_for_key) / len(losses_reduced_for_key)
return loss_reduced, skipped_iter
return {}, skipped_iter
def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
......@@ -307,12 +677,21 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
timers = get_timers()
writer = get_tensorboard_writer()
# Update losses.
# Advanced, skipped, and Nan iterations.
advanced_iters_key = 'advanced iterations'
skipped_iters_key = 'skipped iterations'
nan_iters_key = 'nan iterations'
# Advanced iterations.
if not skipped_iter:
total_loss_dict[advanced_iters_key] = total_loss_dict.get(
advanced_iters_key, 0) + 1
else:
if advanced_iters_key not in total_loss_dict:
total_loss_dict[advanced_iters_key] = 0
# Skipped iterations.
total_loss_dict[skipped_iters_key] = total_loss_dict.get(
skipped_iters_key, 0) + skipped_iter
got_nan_key = 'got nan'
# Update losses and set nan iterations
got_nan = False
for key in loss_dict:
if not skipped_iter:
......@@ -324,9 +703,8 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
value == -float('inf') or \
value != value
got_nan = got_nan or is_nan
total_loss_dict[got_nan_key] = total_loss_dict.get(
got_nan_key, 0) + int(got_nan)
total_loss_dict[nan_iters_key] = total_loss_dict.get(
nan_iters_key, 0) + int(got_nan)
# Logging.
timers_to_log = []
......@@ -334,43 +712,66 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
def add_to_logging(name):
if name in timers.timers:
timers_to_log.append(name)
add_to_logging('forward')
add_to_logging('backward')
add_to_logging('backward-backward')
add_to_logging('backward-allreduce')
add_to_logging('forward-compute')
add_to_logging('forward-recv')
add_to_logging('forward-send')
add_to_logging('forward-send-backward-recv')
add_to_logging('backward-compute')
add_to_logging('backward-recv')
add_to_logging('backward-send')
add_to_logging('backward-send-forward-recv')
add_to_logging('backward-master-grad')
add_to_logging('backward-params-all-reduce')
add_to_logging('backward-embedding-all-reduce')
add_to_logging('backward-clip-grad')
add_to_logging('optimizer')
add_to_logging('batch generator')
add_to_logging('batch-generator')
# Calculate batch size.
batch_size = args.micro_batch_size * args.data_parallel_size * \
get_num_microbatches()
total_iterations = total_loss_dict[advanced_iters_key] + \
total_loss_dict[skipped_iters_key]
# Tensorboard values.
if writer and torch.distributed.get_rank() == 0:
writer.add_scalar('learning_rate', learning_rate, iteration)
if writer and is_last_rank():
writer.add_scalar('learning-rate', learning_rate, iteration)
writer.add_scalar('learning-rate vs samples', learning_rate,
args.consumed_train_samples)
writer.add_scalar('batch-size', batch_size, iteration)
writer.add_scalar('batch-size vs samples', batch_size,
args.consumed_train_samples)
for key in loss_dict:
writer.add_scalar(key, loss_dict[key], iteration)
writer.add_scalar(key , loss_dict[key], iteration)
writer.add_scalar(key + ' vs samples', loss_dict[key],
args.consumed_train_samples)
if args.fp16:
writer.add_scalar('loss_scale', loss_scale, iteration)
normalizer = iteration % args.log_interval
if normalizer == 0:
normalizer = args.log_interval
writer.add_scalar('loss-scale', loss_scale, iteration)
writer.add_scalar('loss-scale vs samples', loss_scale,
args.consumed_train_samples)
timers.write(timers_to_log, writer, iteration,
normalizer=normalizer)
normalizer=total_iterations)
if iteration % args.log_interval == 0:
elapsed_time = timers('interval time').elapsed()
elapsed_time_per_iteration = elapsed_time / total_iterations
if writer and torch.distributed.get_rank() == 0:
writer.add_scalar('iteration_time',
elapsed_time / args.log_interval, iteration)
log_string = ' iteration {:8d}/{:8d} |'.format(iteration,
args.train_iters)
writer.add_scalar('iteration-time',
elapsed_time_per_iteration, iteration)
log_string = ' iteration {:8d}/{:8d} |'.format(
iteration, args.train_iters)
log_string += ' consumed samples: {:12d} |'.format(
args.consumed_train_samples)
log_string += ' elapsed time per iteration (ms): {:.1f} |'.format(
elapsed_time * 1000.0 / args.log_interval)
elapsed_time_per_iteration * 1000.0)
log_string += ' learning rate: {:.3E} |'.format(learning_rate)
num_iterations = max(
1, args.log_interval - total_loss_dict[skipped_iters_key])
log_string += ' global batch size: {:5d} |'.format(batch_size)
for key in total_loss_dict:
if key not in [skipped_iters_key, got_nan_key]:
avg = total_loss_dict[key].item() / float(num_iterations)
if key not in [advanced_iters_key, skipped_iters_key,
nan_iters_key]:
avg = total_loss_dict[key].item() / \
float(max(1, total_loss_dict[advanced_iters_key]))
if avg > 0.0:
log_string += ' {}: {:.6E} |'.format(key, avg)
total_loss_dict[key] = torch.cuda.FloatTensor([0.0])
......@@ -379,24 +780,41 @@ def training_log(loss_dict, total_loss_dict, learning_rate, iteration,
log_string += ' number of skipped iterations: {:3d} |'.format(
total_loss_dict[skipped_iters_key])
log_string += ' number of nan iterations: {:3d} |'.format(
total_loss_dict[got_nan_key])
total_loss_dict[nan_iters_key])
total_loss_dict[advanced_iters_key] = 0
total_loss_dict[skipped_iters_key] = 0
total_loss_dict[got_nan_key] = 0
print_rank_0(log_string)
if report_memory_flag:
report_memory('after {} iterations'.format(iteration))
total_loss_dict[nan_iters_key] = 0
print_rank_last(log_string)
if report_memory_flag and learning_rate > 0.:
# Report memory after optimizer state has been initialized.
report_memory('(after {} iterations)'.format(iteration))
report_memory_flag = False
timers.log(timers_to_log, normalizer=args.log_interval)
return report_memory_flag
def save_checkpoint_and_time(iteration, model, optimizer, lr_scheduler):
timers = get_timers()
# Extra barrier is added to make sure
# all ranks report the max time.
torch.distributed.barrier()
timers('save checkpoint').start()
save_checkpoint(iteration, model, optimizer, lr_scheduler)
torch.distributed.barrier()
timers('save checkpoint').stop()
timers.log(['save checkpoint'])
def train(forward_step_func, model, optimizer, lr_scheduler,
train_data_iterator, valid_data_iterator):
"""Train the model function."""
args = get_args()
timers = get_timers()
# Write args to tensorboard
write_args_to_tensorboard()
# Turn on training mode which enables dropout.
model.train()
......@@ -407,8 +825,10 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
iteration = args.iteration
timers('interval time').start()
print_datetime('before the start of training step')
report_memory_flag = True
while iteration < args.train_iters:
update_num_microbatches(args.consumed_train_samples)
loss_dict, skipped_iter = train_step(forward_step_func,
train_data_iterator,
model,
......@@ -416,7 +836,8 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
lr_scheduler)
iteration += 1
args.consumed_train_samples += mpu.get_data_parallel_world_size() * \
args.batch_size
args.micro_batch_size * \
get_num_microbatches()
# Logging.
loss_scale = None
......@@ -434,9 +855,13 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
lr_scheduler)
# Checkpointing
saved_checkpoint = False
if args.save and args.save_interval and \
iteration % args.save_interval == 0:
save_checkpoint(iteration, model, optimizer, lr_scheduler)
save_checkpoint_and_time(iteration, model, optimizer,
lr_scheduler)
saved_checkpoint = True
# Evaluation
if args.eval_interval and iteration % args.eval_interval == 0 and \
......@@ -446,14 +871,31 @@ def train(forward_step_func, model, optimizer, lr_scheduler,
valid_data_iterator, model,
iteration, False)
# Exiting based on duration
if args.exit_duration_in_mins:
train_time = (time.time() - _TRAIN_START_TIME) / 60.0
done_cuda = torch.cuda.IntTensor(
[train_time > args.exit_duration_in_mins])
torch.distributed.all_reduce(
done_cuda, op=torch.distributed.ReduceOp.MAX)
done = done_cuda.item()
if done:
if not saved_checkpoint:
save_checkpoint_and_time(iteration, model, optimizer,
lr_scheduler)
print_datetime('exiting program after {} minutes'.format(train_time))
sys.exit()
# Exiting based on iterations
if args.exit_interval and iteration % args.exit_interval == 0:
if not saved_checkpoint:
save_checkpoint_and_time(iteration, model, optimizer,
lr_scheduler)
torch.distributed.barrier()
time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
rank = torch.distributed.get_rank()
print_rank_0('rank: {} | time: {} | exiting the program at '
'iteration {}'.format(rank, time_str, iteration))
print_datetime('exiting program at iteration {}'.format(iteration))
sys.exit()
return iteration
......@@ -473,23 +915,44 @@ def evaluate(forward_step_func, data_iterator, model, verbose=False):
if verbose and iteration % args.log_interval == 0:
print_rank_0('Evaluating iter {}/{}'.format(iteration,
args.eval_iters))
# Forward evaluation.
_, loss_dict = forward_step_func(data_iterator, model)
for _ in range(get_num_microbatches()):
if not mpu.is_pipeline_first_stage():
input_tensor, _ = communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_forward=True,
recv_backward=False)
else:
input_tensor = None
# Forward evaluation.
output_tensor = forward_step_func(data_iterator, model, input_tensor)
if mpu.is_pipeline_last_stage():
_, loss_dict = output_tensor
# Reduce across processes.
for key in loss_dict:
total_loss_dict[key] = total_loss_dict.get(key, torch.cuda.FloatTensor([0.0])) + \
loss_dict[key]
else:
communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_forward=False,
recv_backward=False)
args.consumed_valid_samples += mpu.get_data_parallel_world_size() \
* args.batch_size
# Reduce across processes.
for key in loss_dict:
total_loss_dict[key] = total_loss_dict.get(key, 0.) + \
loss_dict[key]
* args.micro_batch_size \
* get_num_microbatches()
# Move model back to the train mode.
model.train()
for key in total_loss_dict:
total_loss_dict[key] /= args.eval_iters
total_loss_dict[key] /= args.eval_iters * get_num_microbatches()
return total_loss_dict
def evaluate_and_print_results(prefix, forward_step_func,
data_iterator, model,
iteration, verbose=False):
......@@ -509,9 +972,9 @@ def evaluate_and_print_results(prefix, forward_step_func,
writer.add_scalar('{} ppl'.format(key), ppl, iteration)
length = len(string) + 1
print_rank_0('-' * length)
print_rank_0(string)
print_rank_0('-' * length)
print_rank_last('-' * length)
print_rank_last(string)
print_rank_last('-' * length)
def build_train_valid_test_data_iterators(
......@@ -523,26 +986,31 @@ def build_train_valid_test_data_iterators(
print_rank_0('> building train, validation, and test datasets ...')
# Rank and global batch size.
data_parallel_size = mpu.get_data_parallel_world_size()
global_batch_size = args.batch_size * data_parallel_size
# Backward compatibility, assume fixed batch size.
if args.iteration > 0 and args.consumed_train_samples == 0:
args.consumed_train_samples = args.iteration * global_batch_size
assert args.train_samples is None, \
'only backward compatiblity support for iteration-based training'
args.consumed_train_samples = args.iteration * args.global_batch_size
if args.iteration > 0 and args.consumed_valid_samples == 0:
assert args.train_samples is None, \
'only backward compatiblity support for iteration-based training'
args.consumed_valid_samples = (args.iteration // args.eval_interval) * \
args.eval_iters * global_batch_size
args.eval_iters * args.global_batch_size
# Data loader only on rank 0 of each model parallel group.
if mpu.get_model_parallel_rank() == 0:
if mpu.get_tensor_model_parallel_rank() == 0:
# Number of train/valid/test samples.
train_iters = args.train_iters
eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
if args.train_samples:
train_samples = args.train_samples
else:
train_samples = args.train_iters * args.global_batch_size
eval_iters = (args.train_iters // args.eval_interval + 1) * \
args.eval_iters
test_iters = args.eval_iters
train_val_test_num_samples = [train_iters * global_batch_size,
eval_iters * global_batch_size,
test_iters * global_batch_size]
train_val_test_num_samples = [train_samples,
eval_iters * args.global_batch_size,
test_iters * args.global_batch_size]
print_rank_0(' > datasets target sizes (minimum size):')
print_rank_0(' train: {}'.format(train_val_test_num_samples[0]))
print_rank_0(' validation: {}'.format(train_val_test_num_samples[1]))
......@@ -571,12 +1039,12 @@ def build_train_valid_test_data_iterators(
# Broadcast num tokens.
torch.distributed.broadcast(flags,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
mpu.get_tensor_model_parallel_src_rank(),
group=mpu.get_tensor_model_parallel_group())
args.do_train = flags[0].item()
args.do_valid = flags[1].item()
args.do_test = flags[2].item()
# Build iterators.
if train_dataloader is not None:
train_data_iterator = iter(train_dataloader)
......
megatron/utils.py
View file @ 9b174da8
......@@ -27,14 +27,16 @@ from megatron.checkpointing import save_checkpoint
from megatron.fp16 import FP16_Optimizer
def reduce_losses(losses):
def average_losses_across_data_parallel_group(losses):
"""Reduce a tensor of losses across all GPUs."""
reduced_losses = torch.cat(
averaged_losses = torch.cat(
[loss.clone().detach().view(1) for loss in losses])
torch.distributed.all_reduce(reduced_losses)
reduced_losses = reduced_losses / torch.distributed.get_world_size()
torch.distributed.all_reduce(averaged_losses,
group=mpu.get_data_parallel_group())
averaged_losses = averaged_losses / \
torch.distributed.get_world_size(group=mpu.get_data_parallel_group())
return reduced_losses
return averaged_losses
def report_memory(name):
......@@ -48,14 +50,15 @@ def report_memory(name):
string += ' | reserved: {}'.format(torch.cuda.memory_reserved() / mega_bytes)
string += ' | max reserved: {}'.format(
torch.cuda.max_memory_reserved() / mega_bytes)
print_rank_0(string)
if mpu.get_data_parallel_rank() == 0:
print("[Rank {}] {}".format(torch.distributed.get_rank(), string), flush=True)
def print_params_min_max_norm(optimizer, iteration):
"""Print min, max, and norm of all parameters."""
index = 0
rank = torch.distributed.get_rank()
string = 'iteration, rank, index, model-parallel,min, max, norm\n'
string = 'iteration, rank, index, tensor-model-parallel, min, max, norm\n'
optimizer_ = optimizer
if isinstance(optimizer, FP16_Optimizer):
optimizer_ = optimizer.optimizer
......@@ -66,7 +69,7 @@ def print_params_min_max_norm(optimizer, iteration):
max_ = param.data.max()
norm = torch.linalg.norm(param.data)
string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format(
iteration, rank, index, int(param.model_parallel))
iteration, rank, index, int(param.tensor_model_parallel))
string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm)
print(string, flush=True)
......@@ -96,11 +99,11 @@ def get_ltor_masks_and_position_ids(data,
"""Build masks and position id for left to right model."""
# Extract batch size and sequence length.
batch_size, seq_length = data.size()
micro_batch_size, seq_length = data.size()
# Attention mask (lower triangular).
if reset_attention_mask:
att_mask_batch = batch_size
att_mask_batch = micro_batch_size
else:
att_mask_batch = 1
attention_mask = torch.tril(torch.ones(
......@@ -122,7 +125,7 @@ def get_ltor_masks_and_position_ids(data,
if reset_position_ids or reset_attention_mask:
# Loop through the batches:
for b in range(batch_size):
for b in range(micro_batch_size):
# Find indecies where EOD token is.
eod_index = position_ids[b, data[b] == eod_token]
......
pretrain_bert.py
View file @ 9b174da8
......@@ -23,9 +23,9 @@ from megatron import print_rank_0
from megatron import get_timers
from megatron import mpu
from megatron.data.dataset_utils import build_train_valid_test_datasets
from megatron.model import BertModel
from megatron.model import BertModel, BertModelFirstStage, BertModelIntermediateStage, BertModelLastStage
from megatron.training import pretrain
from megatron.utils import reduce_losses
from megatron.utils import average_losses_across_data_parallel_group
def model_provider():
......@@ -33,10 +33,25 @@ def model_provider():
print_rank_0('building BERT model ...')
model = BertModel(
num_tokentypes=2,
add_binary_head=True,
parallel_output=True)
args = get_args()
if mpu.get_pipeline_model_parallel_world_size() > 1:
# Determine model based on position of stage in pipeline.
if mpu.is_pipeline_first_stage():
model = BertModelFirstStage(
num_tokentypes=2)
elif mpu.is_pipeline_last_stage():
model = BertModelLastStage(
num_tokentypes=2,
add_binary_head=True,
parallel_output=True)
else:
model = BertModelIntermediateStage(
num_tokentypes=2)
else:
model = BertModel(
num_tokentypes=2,
add_binary_head=True,
parallel_output=True)
return model
......@@ -66,34 +81,51 @@ def get_batch(data_iterator):
return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask
def forward_step(data_iterator, model):
def forward_step(data_iterator, model, input_tensor):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch generator').start()
timers('batch-generator').start()
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask \
= get_batch(data_iterator)
timers('batch generator').stop()
timers('batch-generator').stop()
# Forward pass through the model.
if mpu.is_pipeline_first_stage():
assert input_tensor is None
if mpu.is_pipeline_last_stage():
output_tensor = model(tokens, padding_mask, tokentype_ids=types,
lm_labels=lm_labels)
else:
output_tensor = model(tokens, padding_mask, tokentype_ids=types)
elif mpu.is_pipeline_last_stage():
assert input_tensor is not None
output_tensor = model(input_tensor, padding_mask, lm_labels=lm_labels)
else:
assert input_tensor is not None
output_tensor = model(input_tensor, padding_mask)
# Forward model. lm_labels
lm_loss_, sop_logits = model(tokens, padding_mask,
tokentype_ids=types,
lm_labels=lm_labels)
if mpu.is_pipeline_last_stage():
lm_loss_, sop_logits = output_tensor
sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(),
sentence_order.view(-1),
ignore_index=-1)
sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(),
sentence_order.view(-1),
ignore_index=-1)
sop_loss = sop_loss.float()
lm_loss = torch.sum(
lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
lm_loss_ = lm_loss_.float()
loss_mask = loss_mask.float()
lm_loss = torch.sum(
lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
loss = lm_loss + sop_loss
loss = lm_loss + sop_loss
reduced_losses = reduce_losses([lm_loss, sop_loss])
averaged_losses = average_losses_across_data_parallel_group([lm_loss, sop_loss])
return loss, {'lm loss': reduced_losses[0], 'sop loss': reduced_losses[1]}
return loss, {'lm loss': averaged_losses[0], 'sop loss': averaged_losses[1]}
return output_tensor
def train_valid_test_datasets_provider(train_val_test_num_samples):
......
pretrain_gpt2.py
View file @ 9b174da8
......@@ -23,16 +23,28 @@ from megatron import get_timers
from megatron import get_tokenizer
from megatron import mpu
from megatron.data.gpt2_dataset import build_train_valid_test_datasets
from megatron.model import GPT2Model
from megatron.model import GPT2Model, GPT2ModelFirstStage, GPT2ModelIntermediateStage, GPT2ModelLastStage
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import reduce_losses
from megatron.utils import average_losses_across_data_parallel_group
def model_provider():
"""Build the model."""
print_rank_0('building GPT2 model ...')
model = GPT2Model(num_tokentypes=0, parallel_output=True)
args = get_args()
if mpu.get_pipeline_model_parallel_world_size() > 1:
# Determine model based on position of stage in pipeline.
if mpu.is_pipeline_first_stage():
model = GPT2ModelFirstStage(num_tokentypes=0)
elif mpu.is_pipeline_last_stage():
model = GPT2ModelLastStage(
num_tokentypes=0, parallel_output=True)
else:
model = GPT2ModelIntermediateStage(
num_tokentypes=0)
else:
model = GPT2Model(num_tokentypes=0, parallel_output=True)
return model
......@@ -69,25 +81,42 @@ def get_batch(data_iterator):
return tokens, labels, loss_mask, attention_mask, position_ids
def forward_step(data_iterator, model):
def forward_step(data_iterator, model, input_tensor):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch generator').start()
timers('batch-generator').start()
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
timers('batch generator').stop()
# Forward model.
losses = model(tokens, position_ids, attention_mask, labels=labels)
loss_mask = loss_mask.view(-1)
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
timers('batch-generator').stop()
# Forward pass through the model.
if mpu.is_pipeline_first_stage():
assert input_tensor is None
if mpu.is_pipeline_last_stage():
output_tensor = model(tokens, position_ids, attention_mask,
labels=labels)
else:
output_tensor = model(tokens, position_ids, attention_mask)
elif mpu.is_pipeline_last_stage():
assert input_tensor is not None
output_tensor = model(input_tensor, attention_mask, labels=labels)
else:
assert input_tensor is not None
output_tensor = model(input_tensor, attention_mask)
if mpu.is_pipeline_last_stage():
losses = output_tensor.float()
loss_mask = loss_mask.view(-1).float()
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
reduced_loss = reduce_losses([loss])
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
return loss, {'lm loss': reduced_loss[0]}
return loss, {'lm loss': averaged_loss[0]}
return output_tensor
def train_valid_test_datasets_provider(train_val_test_num_samples):
......
pretrain_ict.py
View file @ 9b174da8
......@@ -25,12 +25,13 @@ from megatron import get_timers
from megatron import mpu
from megatron.data.dataset_utils import build_train_valid_test_datasets
from megatron.training import pretrain
from megatron.utils import reduce_losses
from megatron.utils import average_losses_across_data_parallel_group
from megatron.model.realm_model import general_ict_model_provider
from megatron.data.realm_dataset_utils import get_ict_batch
def pretrain_ict_model_provider():
args = get_args()
return general_ict_model_provider(False, False)
......@@ -72,22 +73,22 @@ class AllgatherFromDataParallelRegion(torch.autograd.Function):
return output
def forward_step(data_iterator, model):
def forward_step(data_iterator, model, input_tensor):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch generator').start()
timers('batch-generator').start()
query_tokens, query_pad_mask, \
block_tokens, block_pad_mask, block_indices = get_ict_batch(data_iterator)
timers('batch generator').stop()
timers('batch-generator').stop()
# Forward model.
query_logits, block_logits = model(query_tokens, query_pad_mask, block_tokens, block_pad_mask)
local_batch_size = query_logits.shape[0]
global_batch_size = dist.get_world_size() * local_batch_size # recall we assert that model_parallel_size == 1
micro_batch_size = query_logits.shape[0]
global_batch_size = dist.get_world_size() * micro_batch_size # recall we assert that tensor_model_parallel_size == 1
all_query_logits = AllgatherFromDataParallelRegion.apply(query_logits)
all_block_logits = AllgatherFromDataParallelRegion.apply(block_logits)
......@@ -102,11 +103,12 @@ def forward_step(data_iterator, model):
topk_accs = [topk_accuracy(int(k)) for k in args.report_topk_accuracies]
retrieval_loss = torch.nn.CrossEntropyLoss()(retrieval_scores, torch.arange(global_batch_size).long().cuda())
reduced_losses = reduce_losses([retrieval_loss, *topk_accs])
retrieval_loss = retrieval_loss.float()
averaged_losses = average_losses_across_data_parallel_group([retrieval_loss, *topk_accs])
# create stats_dict with retrieval loss and all specified top-k accuracies
topk_acc_dict = {'top{}_acc'.format(k): v for k, v in zip(args.report_topk_accuracies, reduced_losses[1:])}
stats_dict = dict(retrieval_loss=reduced_losses[0], **topk_acc_dict)
topk_acc_dict = {'top{}_acc'.format(k): v for k, v in zip(args.report_topk_accuracies, averaged_losses[1:])}
stats_dict = dict(retrieval_loss=averaged_losses[0], **topk_acc_dict)
return retrieval_loss, stats_dict
......
tasks/eval_utils.py
View file @ 9b174da8
......@@ -21,8 +21,9 @@ import time
import torch
from megatron import get_args
from megatron import print_rank_0
from megatron import print_rank_last, is_last_rank
from megatron import mpu
from megatron.training import communicate
from tasks.finetune_utils import build_data_loader
from tasks.finetune_utils import process_batch
......@@ -37,12 +38,12 @@ def accuracy_func_provider(single_dataset_provider):
for datapath in datapaths:
dataset = single_dataset_provider(datapath)
dataloader = build_data_loader(
dataset, args.batch_size, num_workers=args.num_workers,
dataset, args.micro_batch_size, num_workers=args.num_workers,
drop_last=(mpu.get_data_parallel_world_size() > 1))
dataloaders.append((dataset.dataset_name, dataloader))
def metrics_func(model, epoch, output_predictions=False):
print_rank_0('calculating metrics ...')
print_rank_last('calculating metrics ...')
correct = 0
total = 0
if output_predictions:
......@@ -60,25 +61,26 @@ def accuracy_func_provider(single_dataset_provider):
names += '_' + name
correct += correct_ans
total += total_count
percent = float(correct) * 100.0 / float(total)
print_rank_0(' >> |epoch: {}| overall: correct / total = {} / {} = '
'{:.4f} %'.format(epoch, correct, total, percent))
if is_last_rank():
percent = float(correct) * 100.0 / float(total)
print(' >> |epoch: {}| overall: correct / total = {} / {} = '
'{:.4f} %'.format(epoch, correct, total, percent))
if output_predictions and torch.distributed.get_rank() == 0:
if output_predictions and is_last_rank():
assert args.load is not None
filename = os.path.join(args.load, names + '.pt')
torch.save(named_predictions, filename)
return metrics_func
def calculate_correct_answers(name, model, dataloader,
epoch, output_predictions):
"""Calculate correct over total answers and return prediction if the
`output_predictions` is true."""
args = get_args()
start_time = time.time()
model.eval()
saved_batch_size = args.micro_batch_size
with torch.no_grad():
# For all the batches in the dataset.
total = 0
......@@ -92,36 +94,79 @@ def calculate_correct_answers(name, model, dataloader,
for _, batch in enumerate(dataloader):
# Run the model forward.
tokens, types, labels_, attention_mask = process_batch(batch)
logits = model(tokens, attention_mask, types)
# Add output predictions.
if output_predictions:
softmaxes.extend(torch.nn.Softmax(dim=-1)(
logits.float()).data.cpu().numpy().tolist())
labels.extend(labels_.data.cpu().numpy().tolist())
ids.extend(batch['uid'].cpu().numpy().tolist())
# Compute the correct answers.
predicted = torch.argmax(logits, dim=-1)
corrects = (predicted == labels_)
# Add to the counters.
total += labels_.size(0)
correct += corrects.sum().item()
# For evaluation only mode we use drop_last = False to get all the
# samples, which means we might not have a full batch, so we
# adjust batch_size here to actual batch size of data
actual_batch_size = len(labels_)
# ... applying sample_multiplier if necessary
ds = dataloader.dataset
if hasattr(ds, 'sample_multiplier'):
actual_batch_size *= ds.sample_multiplier
args.micro_batch_size = actual_batch_size
if not mpu.is_pipeline_first_stage():
input_tensor, _ = communicate(
tensor_send_next=None,
tensor_send_prev=None,
recv_forward=True,
recv_backward=False)
else:
input_tensor = None
# Forward model.
if mpu.is_pipeline_first_stage():
assert input_tensor is None
output_tensor = model(tokens, attention_mask, tokentype_ids=types)
else:
assert input_tensor is not None
output_tensor = model(input_tensor, attention_mask)
if mpu.is_pipeline_last_stage():
logits = output_tensor
# Add output predictions.
if output_predictions:
softmaxes.extend(torch.nn.Softmax(dim=-1)(
logits.float()).data.cpu().numpy().tolist())
labels.extend(labels_.data.cpu().numpy().tolist())
ids.extend(batch['uid'].cpu().numpy().tolist())
# Compute the correct answers.
predicted = torch.argmax(logits, dim=-1)
corrects = (predicted == labels_)
# Add to the counters.
total += labels_.size(0)
correct += corrects.sum().item()
else:
communicate(
tensor_send_next=output_tensor,
tensor_send_prev=None,
recv_forward=False,
recv_backward=False)
model.train()
args.micro_batch_size = saved_batch_size
# Reduce.
unreduced = torch.cuda.LongTensor([correct, total])
torch.distributed.all_reduce(unreduced,
group=mpu.get_data_parallel_group())
# Print on screen.
correct_ans = unreduced[0].item()
total_count = unreduced[1].item()
percent = float(correct_ans) * 100.0 / float(total_count)
elapsed_time = time.time() - start_time
print_rank_0(' > |epoch: {}| metrics for {}: correct / total '
'= {} / {} = {:.4f} %, elapsed time (sec): {:.3f}'.format(
epoch, name, correct_ans, total_count,
percent, elapsed_time))
if mpu.is_pipeline_last_stage():
unreduced = torch.cuda.LongTensor([correct, total])
torch.distributed.all_reduce(unreduced,
group=mpu.get_data_parallel_group())
# Print on screen.
correct_ans = unreduced[0].item()
total_count = unreduced[1].item()
percent = float(correct_ans) * 100.0 / float(total_count)
elapsed_time = time.time() - start_time
print_rank_last(' > |epoch: {}| metrics for {}: correct / total '
'= {} / {} = {:.4f} %, elapsed time (sec): {:.3f}'.format(
epoch, name, correct_ans, total_count,
percent, elapsed_time))
if output_predictions:
return correct_ans, total_count, (softmaxes, labels, ids)
return correct_ans, total_count
if output_predictions:
return correct_ans, total_count, (softmaxes, labels, ids)
return correct_ans, total_count
return 0, 0, ()
return 0, 0
tasks/finetune_utils.py
View file @ 9b174da8
......@@ -28,7 +28,7 @@ from megatron.training import setup_model_and_optimizer
from megatron.training import train_step
from megatron.training import training_log
from megatron.utils import check_adlr_autoresume_termination
from megatron.utils import reduce_losses
from megatron.utils import average_losses_across_data_parallel_group
def process_batch(batch):
......@@ -45,33 +45,42 @@ def process_batch(batch):
return tokens, types, labels, attention_mask
def _cross_entropy_forward_step(batch, model):
def _cross_entropy_forward_step(batch, model, input_tensor):
"""Simple forward step with cross-entropy loss."""
timers = get_timers()
# Get the batch.
timers('batch generator').start()
timers('batch-generator').start()
try:
batch_ = next(batch)
except BaseException:
batch_ = batch
tokens, types, labels, attention_mask = process_batch(batch_)
timers('batch generator').stop()
timers('batch-generator').stop()
# Forward model.
logits = model(tokens, attention_mask, types)
if mpu.is_pipeline_first_stage():
assert input_tensor is None
output_tensor = model(tokens, attention_mask, tokentype_ids=types)
else:
assert input_tensor is not None
output_tensor = model(input_tensor, attention_mask)
if mpu.is_pipeline_last_stage():
logits = output_tensor
# Cross-entropy loss.
loss_func = torch.nn.CrossEntropyLoss()
loss = loss_func(logits.contiguous().float(), labels)
# Cross-entropy loss.
loss_func = torch.nn.CrossEntropyLoss()
loss = loss_func(logits.contiguous().float(), labels)
# Reduce loss for logging.
reduced_loss = reduce_losses([loss])
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
return loss, {'lm loss': reduced_loss[0]}
return loss, {'lm loss': averaged_loss[0]}
return output_tensor
def build_data_loader(dataset, batch_size, num_workers, drop_last):
def build_data_loader(dataset, micro_batch_size, num_workers, drop_last):
"""Data loader. Note that batch-size is the local (per GPU) batch-size."""
# Sampler.
......@@ -82,7 +91,7 @@ def build_data_loader(dataset, batch_size, num_workers, drop_last):
# Data loader. Note that batch size is the per GPU batch size.
data_loader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
batch_size=micro_batch_size,
sampler=sampler,
shuffle=False,
num_workers=num_workers,
......@@ -109,17 +118,26 @@ def _build_train_valid_dataloaders(train_dataset, valid_dataset):
print_rank_0('building train and validation dataloaders ...')
# Training dataset.
train_dataloader = build_data_loader(train_dataset, args.batch_size,
train_dataloader = build_data_loader(train_dataset, args.micro_batch_size,
args.num_workers, not args.keep_last)
# Set the training iterations.
args.train_iters_per_epoch = len(train_dataloader)
args.train_iters = args.epochs * args.train_iters_per_epoch
# Validation dataset. For this dataset, we do not need to set up
# shuffling so we can just use a simple infinite loop.
valid_dataloader_ = build_data_loader(valid_dataset, args.batch_size,
valid_dataloader_ = build_data_loader(valid_dataset, args.micro_batch_size,
args.num_workers, not args.keep_last)
valid_dataloader = _build_infinite_size_dataloader(valid_dataloader_)
# Now that we've built the data loaders, set batch_size arguments
# to the actual batch size the model will see for this dataset.
# This is necessary so pipeline transfers know what size they are
# and the LR schedule, which is based on samples seen, gets set
# correctly.
if hasattr(train_dataset, 'sample_multiplier'):
args.micro_batch_size *= train_dataset.sample_multiplier
args.global_batch_size *= train_dataset.sample_multiplier
return train_dataloader, valid_dataloader
......@@ -211,6 +229,8 @@ def finetune(train_valid_datasets_provider, model_provider,
train_dataset, valid_dataset = train_valid_datasets_provider()
train_dataloader, valid_dataloader = _build_train_valid_dataloaders(
train_dataset, valid_dataset)
else:
args.train_iters = 0
timers('train/valid/test dataset/dataloder').stop()
# Build calback function.
......@@ -255,5 +275,4 @@ def finetune(train_valid_datasets_provider, model_provider,
if end_of_epoch_callback is not None:
print_rank_0('evaluation only mode, setting epoch to -1')
end_of_epoch_callback(model, epoch=-1, output_predictions=True)
print_rank_0('done :-)')
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