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[misc] update pre-commit and run all files (#4752)

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* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format
This commit is contained in:
Hongxin Liu
2023-09-19 14:20:26 +08:00
committed by GitHub
parent 3c6b831c26
commit 079bf3cb26
1268 changed files with 50037 additions and 38444 deletions
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184
examples/language/gpt/titans/model/embed.py
View File

@@ -1,7 +1,6 @@
import torch
import torch.nn.init as init
from torch import Tensor
from torch import distributed as dist
from torch import nn as nn
from torch.nn import functional as F
from torch.nn.parameter import Parameter
@@ -12,7 +11,7 @@ from colossalai.legacy.nn.layer.base_layer import ParallelLayer
from colossalai.legacy.nn.layer.parallel_1d._utils import gather_forward_split_backward, reduce_grad, reduce_input
from colossalai.legacy.nn.layer.parallel_1d.layers import Linear1D_Row
from colossalai.legacy.nn.layer.utils import divide
from colossalai.legacy.registry import LAYERS, LOSSES, MODELS
from colossalai.legacy.registry import LAYERS, LOSSES
from colossalai.utils import get_current_device
@@ -30,13 +29,9 @@ class VocabParallelEmbedding(torch.nn.Module):
will ignore this embedding
"""
def __init__(self,
hidden_size,
vocab_size,
max_sequence_length,
embedding_dropout_prob,
num_tokentypes=0,
dtype=torch.float):
def __init__(
self, hidden_size, vocab_size, max_sequence_length, embedding_dropout_prob, num_tokentypes=0, dtype=torch.float
):
super(VocabParallelEmbedding, self).__init__()
self.hidden_size = hidden_size
@@ -44,11 +39,11 @@ class VocabParallelEmbedding(torch.nn.Module):
# Word embeddings (parallel).
self.word_embeddings = VocabParallelEmbedding1D(vocab_size, self.hidden_size, dtype=dtype)
self._word_embeddings_key = 'word_embeddings'
self._word_embeddings_key = "word_embeddings"
# Position embedding (serial).
self.position_embeddings = torch.nn.Embedding(max_sequence_length, self.hidden_size, dtype=dtype)
self._position_embeddings_key = 'position_embeddings'
self._position_embeddings_key = "position_embeddings"
# Initialize the position embeddings.
# self.init_method(self.position_embeddings.weight)
@@ -56,7 +51,7 @@ class VocabParallelEmbedding(torch.nn.Module):
# Add this as an optional field that can be added through
# method call so we can load a pretrain model without
# token types and add them as needed.
self._tokentype_embeddings_key = 'tokentype_embeddings'
self._tokentype_embeddings_key = "tokentype_embeddings"
if self.num_tokentypes > 0:
self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, self.hidden_size, dtype=dtype)
# Initialize the token-type embeddings.
@@ -83,9 +78,9 @@ class VocabParallelEmbedding(torch.nn.Module):
This allows us to load the model normally and then add this embedding.
"""
if self.tokentype_embeddings is not None:
raise Exception('tokentype embeddings is already initialized')
raise Exception("tokentype embeddings is already initialized")
if torch.distributed.get_rank() == 0:
print('adding embedding for {} tokentypes'.format(num_tokentypes), flush=True)
print("adding embedding for {} tokentypes".format(num_tokentypes), flush=True)
self.num_tokentypes = num_tokentypes
self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, self.hidden_size)
# Initialize the token-type embeddings.
@@ -112,19 +107,16 @@ class VocabParallelEmbedding(torch.nn.Module):
embeddings = self.embedding_dropout(embeddings)
return embeddings
def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
def state_dict_for_save_checkpoint(self, destination=None, prefix="", keep_vars=False):
"""For easy load."""
state_dict_ = {}
state_dict_[self._word_embeddings_key] \
= self.word_embeddings.state_dict(destination, prefix, keep_vars)
state_dict_[self._position_embeddings_key] \
= self.position_embeddings.state_dict(
destination, prefix, keep_vars)
state_dict_[self._word_embeddings_key] = self.word_embeddings.state_dict(destination, prefix, keep_vars)
state_dict_[self._position_embeddings_key] = self.position_embeddings.state_dict(destination, prefix, keep_vars)
if self.num_tokentypes > 0:
state_dict_[self._tokentype_embeddings_key] \
= self.tokentype_embeddings.state_dict(
destination, prefix, keep_vars)
state_dict_[self._tokentype_embeddings_key] = self.tokentype_embeddings.state_dict(
destination, prefix, keep_vars
)
return state_dict_
@@ -138,9 +130,8 @@ class VocabParallelEmbedding(torch.nn.Module):
# for backward compatibility.
state_dict_ = {}
for key in state_dict.keys():
if 'word_embeddings' in key:
state_dict_[key.split('word_embeddings.')[1]] \
= state_dict[key]
if "word_embeddings" in key:
state_dict_[key.split("word_embeddings.")[1]] = state_dict[key]
self.word_embeddings.load_state_dict(state_dict_, strict=strict)
# Position embedding.
@@ -150,9 +141,8 @@ class VocabParallelEmbedding(torch.nn.Module):
# for backward compatibility.
state_dict_ = {}
for key in state_dict.keys():
if 'position_embeddings' in key:
state_dict_[key.split('position_embeddings.')[1]] \
= state_dict[key]
if "position_embeddings" in key:
state_dict_[key.split("position_embeddings.")[1]] = state_dict[key]
self.position_embeddings.load_state_dict(state_dict_, strict=strict)
# Tokentype embedding.
@@ -163,15 +153,14 @@ class VocabParallelEmbedding(torch.nn.Module):
else:
# for backward compatibility.
for key in state_dict.keys():
if 'tokentype_embeddings' in key:
state_dict_[key.split('tokentype_embeddings.')[1]] \
= state_dict[key]
if "tokentype_embeddings" in key:
state_dict_[key.split("tokentype_embeddings.")[1]] = state_dict[key]
if len(state_dict_.keys()) > 0:
self.tokentype_embeddings.load_state_dict(state_dict_, strict=strict)
else:
print('***WARNING*** expected tokentype embeddings in the '
'checkpoint but could not find it',
flush=True)
print(
"***WARNING*** expected tokentype embeddings in the " "checkpoint but could not find it", flush=True
)
class VocabParallelEmbedding1D(torch.nn.Module):
@@ -193,37 +182,41 @@ class VocabParallelEmbedding1D(torch.nn.Module):
# Set the details for compatibility.
self.padding_idx = None
self.max_norm = None
self.norm_type = 2.
self.norm_type = 2.0
self.scale_grad_by_freq = False
self.sparse = False
self._weight = None
self.tensor_model_parallel_size = gpc.tensor_parallel_size
# Divide the weight matrix along the vocabulary dimension.
self.vocab_start_index, self.vocab_end_index = \
VocabUtility.vocab_range_from_global_vocab_size(
self.num_embeddings, gpc.get_local_rank(ParallelMode.PARALLEL_1D),
self.tensor_model_parallel_size)
self.num_embeddings_per_partition = self.vocab_end_index - \
self.vocab_start_index
self.vocab_start_index, self.vocab_end_index = VocabUtility.vocab_range_from_global_vocab_size(
self.num_embeddings, gpc.get_local_rank(ParallelMode.PARALLEL_1D), self.tensor_model_parallel_size
)
self.num_embeddings_per_partition = self.vocab_end_index - self.vocab_start_index
# Allocate weights and initialize.
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
factory_kwargs = {"device": get_current_device(), "dtype": dtype}
self.weight = Parameter(torch.empty(self.num_embeddings_per_partition, self.embedding_dim, **factory_kwargs))
init.uniform_(self.weight, -1, 1)
def forward(self, input_):
if self.tensor_model_parallel_size > 1:
# Build the mask.
input_mask = (input_ < self.vocab_start_index) | \
(input_ >= self.vocab_end_index)
input_mask = (input_ < self.vocab_start_index) | (input_ >= self.vocab_end_index)
# Mask the input.
masked_input = input_.clone() - self.vocab_start_index
masked_input[input_mask] = 0
else:
masked_input = input_
# Get the embeddings.
output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, self.max_norm, self.norm_type,
self.scale_grad_by_freq, self.sparse)
output_parallel = F.embedding(
masked_input,
self.weight,
self.padding_idx,
self.max_norm,
self.norm_type,
self.scale_grad_by_freq,
self.sparse,
)
# Mask the output embedding.
if self.tensor_model_parallel_size > 1:
output_parallel[input_mask, :] = 0.0
@@ -234,7 +227,6 @@ class VocabParallelEmbedding1D(torch.nn.Module):
@LOSSES.register_module
class vocab_parallel_cross_entropy(nn.Module):
def __init__(self):
super().__init__()
@@ -242,20 +234,19 @@ class vocab_parallel_cross_entropy(nn.Module):
"""Helper function for the cross entropy."""
vocab_parallel_logits = vocab_parallel_logits[..., :-1, :].contiguous()
target = target[..., 1:].contiguous()
return _VocabParallelCrossEntropy.apply(vocab_parallel_logits.view(-1, vocab_parallel_logits.size(-1)),
target.view(-1))
return _VocabParallelCrossEntropy.apply(
vocab_parallel_logits.view(-1, vocab_parallel_logits.size(-1)), target.view(-1)
)
class _VocabParallelCrossEntropy(torch.autograd.Function):
@staticmethod
def forward(ctx, vocab_parallel_logits, target):
# Maximum value along vocab dimension across all GPUs.
logits_max = torch.max(vocab_parallel_logits, dim=-1)[0]
torch.distributed.all_reduce(logits_max,
op=torch.distributed.ReduceOp.MAX,
group=gpc.get_group(ParallelMode.PARALLEL_1D))
torch.distributed.all_reduce(
logits_max, op=torch.distributed.ReduceOp.MAX, group=gpc.get_group(ParallelMode.PARALLEL_1D)
)
# Subtract the maximum value.
vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1))
@@ -282,17 +273,17 @@ class _VocabParallelCrossEntropy(torch.autograd.Function):
predicted_logits = predicted_logits_1d.view_as(target)
predicted_logits[target_mask] = 0.0
# All reduce is needed to get the chunks from other GPUs.
torch.distributed.all_reduce(predicted_logits,
op=torch.distributed.ReduceOp.SUM,
group=gpc.get_group(ParallelMode.PARALLEL_1D))
torch.distributed.all_reduce(
predicted_logits, op=torch.distributed.ReduceOp.SUM, group=gpc.get_group(ParallelMode.PARALLEL_1D)
)
# Sum of exponential of logits along vocab dimension across all GPUs.
exp_logits = vocab_parallel_logits
torch.exp(vocab_parallel_logits, out=exp_logits)
sum_exp_logits = exp_logits.sum(dim=-1)
torch.distributed.all_reduce(sum_exp_logits,
op=torch.distributed.ReduceOp.SUM,
group=gpc.get_group(ParallelMode.PARALLEL_1D))
torch.distributed.all_reduce(
sum_exp_logits, op=torch.distributed.ReduceOp.SUM, group=gpc.get_group(ParallelMode.PARALLEL_1D)
)
# Loss = log(sum(exp(logits))) - predicted-logit.
loss = torch.log(sum_exp_logits) - predicted_logits
@@ -304,7 +295,6 @@ class _VocabParallelCrossEntropy(torch.autograd.Function):
@staticmethod
def backward(ctx, grad_output):
# Retrieve tensors from the forward path.
softmax, target_mask, masked_target_1d = ctx.saved_tensors
@@ -316,7 +306,7 @@ class _VocabParallelCrossEntropy(torch.autograd.Function):
# Add the gradient from matching classes.
arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device)
grad_2d[arange_1d, masked_target_1d] -= (1.0 - target_mask.view(-1).float())
grad_2d[arange_1d, masked_target_1d] -= 1.0 - target_mask.view(-1).float()
# Finally elementwise multiplication with the output gradients.
grad_input.mul_(grad_output.unsqueeze(dim=-1))
@@ -326,8 +316,8 @@ class _VocabParallelCrossEntropy(torch.autograd.Function):
class VocabUtility:
"""Split the vocabulary into `world_size` chunks amd return the
first and last index of the vocabulary belonging to the `rank`
partition: Note that indices in [fist, last)"""
first and last index of the vocabulary belonging to the `rank`
partition: Note that indices in [fist, last)"""
@staticmethod
def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, world_size):
@@ -393,11 +383,11 @@ class HiddenParallelEmbedding(torch.nn.Module):
# Word embeddings (parallel).
self.word_embeddings = HiddenParallelEmbedding1D(vocab_size, hidden_size, dtype, padding_idx)
self._word_embeddings_key = 'word_embeddings'
self._word_embeddings_key = "word_embeddings"
# Position embedding (serial).
self.position_embeddings = torch.nn.Embedding(max_sequence_length, self.hidden_size)
self._position_embeddings_key = 'position_embeddings'
self._position_embeddings_key = "position_embeddings"
# Initialize the position embeddings.
# self.init_method(self.position_embeddings.weight)
@@ -405,7 +395,7 @@ class HiddenParallelEmbedding(torch.nn.Module):
# Add this as an optional field that can be added through
# method call so we can load a pretrain model without
# token types and add them as needed.
self._tokentype_embeddings_key = 'tokentype_embeddings'
self._tokentype_embeddings_key = "tokentype_embeddings"
if self.num_tokentypes > 0:
self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, self.hidden_size)
# Initialize the token-type embeddings.
@@ -432,9 +422,9 @@ class HiddenParallelEmbedding(torch.nn.Module):
This allows us to load the model normally and then add this embedding.
"""
if self.tokentype_embeddings is not None:
raise Exception('tokentype embeddings is already initialized')
raise Exception("tokentype embeddings is already initialized")
if torch.distributed.get_rank() == 0:
print('adding embedding for {} tokentypes'.format(num_tokentypes), flush=True)
print("adding embedding for {} tokentypes".format(num_tokentypes), flush=True)
self.num_tokentypes = num_tokentypes
self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, self.hidden_size)
# Initialize the token-type embeddings.
@@ -460,19 +450,16 @@ class HiddenParallelEmbedding(torch.nn.Module):
embeddings = self.embedding_dropout(embeddings)
return embeddings
def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False):
def state_dict_for_save_checkpoint(self, destination=None, prefix="", keep_vars=False):
"""For easy load."""
state_dict_ = {}
state_dict_[self._word_embeddings_key] \
= self.word_embeddings.state_dict(destination, prefix, keep_vars)
state_dict_[self._position_embeddings_key] \
= self.position_embeddings.state_dict(
destination, prefix, keep_vars)
state_dict_[self._word_embeddings_key] = self.word_embeddings.state_dict(destination, prefix, keep_vars)
state_dict_[self._position_embeddings_key] = self.position_embeddings.state_dict(destination, prefix, keep_vars)
if self.num_tokentypes > 0:
state_dict_[self._tokentype_embeddings_key] \
= self.tokentype_embeddings.state_dict(
destination, prefix, keep_vars)
state_dict_[self._tokentype_embeddings_key] = self.tokentype_embeddings.state_dict(
destination, prefix, keep_vars
)
return state_dict_
@@ -486,9 +473,8 @@ class HiddenParallelEmbedding(torch.nn.Module):
# for backward compatibility.
state_dict_ = {}
for key in state_dict.keys():
if 'word_embeddings' in key:
state_dict_[key.split('word_embeddings.')[1]] \
= state_dict[key]
if "word_embeddings" in key:
state_dict_[key.split("word_embeddings.")[1]] = state_dict[key]
self.word_embeddings.load_state_dict(state_dict_, strict=strict)
# Position embedding.
@@ -498,9 +484,8 @@ class HiddenParallelEmbedding(torch.nn.Module):
# for backward compatibility.
state_dict_ = {}
for key in state_dict.keys():
if 'position_embeddings' in key:
state_dict_[key.split('position_embeddings.')[1]] \
= state_dict[key]
if "position_embeddings" in key:
state_dict_[key.split("position_embeddings.")[1]] = state_dict[key]
self.position_embeddings.load_state_dict(state_dict_, strict=strict)
# Tokentype embedding.
@@ -511,15 +496,14 @@ class HiddenParallelEmbedding(torch.nn.Module):
else:
# for backward compatibility.
for key in state_dict.keys():
if 'tokentype_embeddings' in key:
state_dict_[key.split('tokentype_embeddings.')[1]] \
= state_dict[key]
if "tokentype_embeddings" in key:
state_dict_[key.split("tokentype_embeddings.")[1]] = state_dict[key]
if len(state_dict_.keys()) > 0:
self.tokentype_embeddings.load_state_dict(state_dict_, strict=strict)
else:
print('***WARNING*** expected tokentype embeddings in the '
'checkpoint but could not find it',
flush=True)
print(
"***WARNING*** expected tokentype embeddings in the " "checkpoint but could not find it", flush=True
)
class HiddenParallelEmbedding1D(torch.nn.Module):
@@ -542,21 +526,21 @@ class HiddenParallelEmbedding1D(torch.nn.Module):
# Set the details for compatibility.
self.padding_idx = padding_idx
self.max_norm = None
self.norm_type = 2.
self.norm_type = 2.0
self.scale_grad_by_freq = False
self.sparse = False
self._weight = None
# Allocate weights and initialize.
factory_kwargs = {'device': get_current_device(), 'dtype': dtype}
factory_kwargs = {"device": get_current_device(), "dtype": dtype}
self.weight = Parameter(torch.empty(num_embeddings, embed_dim_per_partition, **factory_kwargs))
init.uniform_(self.weight, -1, 1)
def forward(self, input_):
# Get the embeddings.
output_parallel = F.embedding(input_, self.weight, self.padding_idx, self.max_norm, self.norm_type,
self.scale_grad_by_freq, self.sparse)
output_parallel = F.embedding(
input_, self.weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse
)
# Reduce across all the model parallel GPUs.
output = gather_forward_split_backward(output_parallel, ParallelMode.PARALLEL_1D, dim=-1)
@@ -584,11 +568,9 @@ class HiddenParallelGPTLMHead1D(ParallelLayer):
# self.embedding = HiddenParallelEmbedding1D(vocab_size, hidden_size, dtype, padding_idx)
# (hidden_size/q, vocab_size)
self.synced_embed = False
self.head = Linear1D_Row(in_features=embed_dim,
out_features=vocab_size,
bias=False,
dtype=dtype,
parallel_input=False)
self.head = Linear1D_Row(
in_features=embed_dim, out_features=vocab_size, bias=False, dtype=dtype, parallel_input=False
)
def forward(self, x: Tensor) -> Tensor:
if self.synced_embed:

252
examples/language/gpt/titans/model/gpt1d.py
View File

@@ -18,18 +18,21 @@ from colossalai.legacy.utils.activation_checkpoint import checkpoint
from colossalai.utils import checkpoint
__all__ = [
'GPTMLP1D', 'GPTSelfAttention1D', 'GPTTransformerLayer1D', 'FusedGPTSelfAttention1D', 'FusedGPTTransformerLayer1D'
"GPTMLP1D",
"GPTSelfAttention1D",
"GPTTransformerLayer1D",
"FusedGPTSelfAttention1D",
"FusedGPTTransformerLayer1D",
]
class GPTMLP1D(ParallelLayer):
def __init__(
self,
in_features: int,
mlp_ratio: int,
act_func: str = 'gelu',
dropout_prob: float = 0.,
act_func: str = "gelu",
dropout_prob: float = 0.0,
dtype=None,
checkpoint: bool = False,
skip_bias_add: bool = False,
@@ -82,7 +85,6 @@ class GPTMLP1D(ParallelLayer):
class GenericGPTSelfAttention1D(ParallelLayer):
def __init__(
self,
hidden_size: int,
@@ -118,8 +120,10 @@ class GenericGPTSelfAttention1D(ParallelLayer):
def _forward(self, hidden_states: Tensor, attention_mask=None) -> Tensor:
query_key_value = self.query_key_value(hidden_states)
new_qkv_shape = query_key_value.shape[:-1] + \
(self.num_attention_heads_per_partition, 3 * self.attention_head_size)
new_qkv_shape = query_key_value.shape[:-1] + (
self.num_attention_heads_per_partition,
3 * self.attention_head_size,
)
query_key_value = query_key_value.view(new_qkv_shape)
query_key_value = query_key_value.permute((0, 2, 1, 3))
query_layer, key_layer, value_layer = torch.chunk(query_key_value, 3, dim=-1)
@@ -152,28 +156,32 @@ class GenericGPTSelfAttention1D(ParallelLayer):
class GPTSelfAttention1D(GenericGPTSelfAttention1D):
def __init__(self,
hidden_size: int,
num_attention_heads: int,
attention_dropout_prob: float,
hidden_dropout_prob: float,
dtype=None,
checkpoint: bool = False,
max_position_embeddings=1024):
super().__init__(hidden_size,
num_attention_heads,
attention_dropout_prob,
hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings)
def __init__(
self,
hidden_size: int,
num_attention_heads: int,
attention_dropout_prob: float,
hidden_dropout_prob: float,
dtype=None,
checkpoint: bool = False,
max_position_embeddings=1024,
):
super().__init__(
hidden_size,
num_attention_heads,
attention_dropout_prob,
hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
)
self.softmax = nn.Softmax(dim=-1)
max_positions = max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones((max_positions, max_positions),
dtype=torch.uint8)).view(1, 1, max_positions, max_positions),
torch.tril(torch.ones((max_positions, max_positions), dtype=torch.uint8)).view(
1, 1, max_positions, max_positions
),
)
self.register_buffer("masked_bias", torch.tensor(-1e4))
@@ -181,7 +189,7 @@ class GPTSelfAttention1D(GenericGPTSelfAttention1D):
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
# causal mask
query_length, key_length = query_layer.size(-2), key_layer.size(-2)
causal_mask = self.bias[:, :, key_length - query_length:key_length, :key_length].bool()
causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length].bool()
attention_scores = torch.where(causal_mask, attention_scores, self.masked_bias.to(attention_scores))
if attention_mask is not None:
# Apply the attention mask
@@ -191,50 +199,56 @@ class GPTSelfAttention1D(GenericGPTSelfAttention1D):
class FusedGPTSelfAttention1D(GenericGPTSelfAttention1D):
def __init__(self,
hidden_size: int,
num_attention_heads: int,
attention_dropout_prob: float,
hidden_dropout_prob: float,
dtype=None,
checkpoint: bool = False,
max_position_embeddings=1024):
super().__init__(hidden_size,
num_attention_heads,
attention_dropout_prob,
hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings)
self.softmax = kernel.FusedScaleMaskSoftmax(input_in_fp16=True,
input_in_bf16=False,
attn_mask_type=AttnMaskType.causal,
scaled_masked_softmax_fusion=True,
mask_func=None,
softmax_in_fp32=True,
scale=math.sqrt(self.attention_head_size))
def __init__(
self,
hidden_size: int,
num_attention_heads: int,
attention_dropout_prob: float,
hidden_dropout_prob: float,
dtype=None,
checkpoint: bool = False,
max_position_embeddings=1024,
):
super().__init__(
hidden_size,
num_attention_heads,
attention_dropout_prob,
hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
)
self.softmax = kernel.FusedScaleMaskSoftmax(
input_in_fp16=True,
input_in_bf16=False,
attn_mask_type=AttnMaskType.causal,
scaled_masked_softmax_fusion=True,
mask_func=None,
softmax_in_fp32=True,
scale=math.sqrt(self.attention_head_size),
)
def softmax_forward(self, attention_scores, attention_mask, query_layer, key_layer):
return self.softmax(attention_scores, attention_mask)
class GenericGPTTransformerLayer1D(ParallelLayer):
def __init__(self,
hidden_size: int,
num_attention_heads: int,
act_func: str = 'gelu',
mlp_ratio: float = 4.0,
attention_dropout_prob: float = 0.,
hidden_dropout_prob: float = 0.,
dtype=None,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
attention=None,
layer_norm=None):
def __init__(
self,
hidden_size: int,
num_attention_heads: int,
act_func: str = "gelu",
mlp_ratio: float = 4.0,
attention_dropout_prob: float = 0.0,
hidden_dropout_prob: float = 0.0,
dtype=None,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
attention=None,
layer_norm=None,
):
super().__init__()
self.checkpoint = checkpoint
self.dtype = dtype
@@ -288,62 +302,68 @@ class GenericGPTTransformerLayer1D(ParallelLayer):
class GPTTransformerLayer1D(GenericGPTTransformerLayer1D):
def __init__(self,
hidden_size: int,
num_attention_heads: int,
act_func: str = 'gelu',
mlp_ratio: float = 4,
attention_dropout_prob: float = 0,
hidden_dropout_prob: float = 0,
dtype=None,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 0.00001,
apply_post_layer_norm: bool = False):
def __init__(
self,
hidden_size: int,
num_attention_heads: int,
act_func: str = "gelu",
mlp_ratio: float = 4,
attention_dropout_prob: float = 0,
hidden_dropout_prob: float = 0,
dtype=None,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 0.00001,
apply_post_layer_norm: bool = False,
):
attention = GPTSelfAttention1D
layer_norm = nn.LayerNorm
super().__init__(hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attention_dropout_prob,
hidden_dropout_prob=hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm,
attention=attention,
layer_norm=layer_norm)
super().__init__(
hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attention_dropout_prob,
hidden_dropout_prob=hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm,
attention=attention,
layer_norm=layer_norm,
)
class FusedGPTTransformerLayer1D(GenericGPTTransformerLayer1D):
def __init__(self,
hidden_size: int,
num_attention_heads: int,
act_func: str = 'gelu',
mlp_ratio: float = 4,
attention_dropout_prob: float = 0,
hidden_dropout_prob: float = 0,
dtype=None,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 0.00001,
apply_post_layer_norm: bool = False):
def __init__(
self,
hidden_size: int,
num_attention_heads: int,
act_func: str = "gelu",
mlp_ratio: float = 4,
attention_dropout_prob: float = 0,
hidden_dropout_prob: float = 0,
dtype=None,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 0.00001,
apply_post_layer_norm: bool = False,
):
attention = FusedGPTSelfAttention1D
layer_norm = kernel.LayerNorm
super().__init__(hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attention_dropout_prob,
hidden_dropout_prob=hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm,
attention=attention,
layer_norm=layer_norm)
super().__init__(
hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attention_dropout_prob,
hidden_dropout_prob=hidden_dropout_prob,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm,
attention=attention,
layer_norm=layer_norm,
)

321
examples/language/gpt/titans/model/pipeline_gpt1d.py
View File

@@ -17,17 +17,16 @@ from .embed import HiddenParallelEmbedding, HiddenParallelGPTLMHead1D, VocabPara
from .gpt1d import FusedGPTTransformerLayer1D, GPTTransformerLayer1D
__all__ = [
'GPT2_small_pipeline_1D',
'GPT2_exlarge_pipeline_1D',
'GPT3_pipeline_1D',
'GPT2_exlarge_pipeline_hybrid',
'GPT2_small_pipeline_hybrid',
'GPT3_pipeline_hybrid',
"GPT2_small_pipeline_1D",
"GPT2_exlarge_pipeline_1D",
"GPT3_pipeline_1D",
"GPT2_exlarge_pipeline_hybrid",
"GPT2_small_pipeline_hybrid",
"GPT3_pipeline_hybrid",
]
class GenericPipelineGPT(nn.Module):
def __init__(self, embedding=None, blocks=None, norm=None, head=None) -> None:
super().__init__()
self.embedding = embedding
@@ -44,7 +43,7 @@ class GenericPipelineGPT(nn.Module):
batch_size = hidden_states.shape[0]
attention_mask = attention_mask.view(batch_size, -1)
attention_mask = attention_mask[:, None, None, :]
attention_mask = attention_mask.to(dtype=hidden_states.dtype) # fp16 compatibility
attention_mask = attention_mask.to(dtype=hidden_states.dtype) # fp16 compatibility
attention_mask = (1.0 - attention_mask) * -10000.0
for block in self.blocks:
hidden_states, attention_mask = block(hidden_states, attention_mask)
@@ -54,25 +53,26 @@ class GenericPipelineGPT(nn.Module):
class PipelineGPT1D(GenericPipelineGPT):
def __init__(self,
num_layers: int = 12,
hidden_size: int = 768,
num_attention_heads: int = 12,
vocab_size: int = 50304,
embed_drop_rate: float = 0.,
act_func: str = 'gelu',
mlp_ratio: int = 4.0,
attn_drop_rate: float = 0.,
drop_rate: float = 0.,
dtype: torch.dtype = torch.float,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
first: bool = False,
last: bool = False,
embed_split_hidden=False):
def __init__(
self,
num_layers: int = 12,
hidden_size: int = 768,
num_attention_heads: int = 12,
vocab_size: int = 50304,
embed_drop_rate: float = 0.0,
act_func: str = "gelu",
mlp_ratio: int = 4.0,
attn_drop_rate: float = 0.0,
drop_rate: float = 0.0,
dtype: torch.dtype = torch.float,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
first: bool = False,
last: bool = False,
embed_split_hidden=False,
):
embedding = None
norm = None
head = None
@@ -83,19 +83,24 @@ class PipelineGPT1D(GenericPipelineGPT):
head_cls = HiddenParallelGPTLMHead1D
if first:
embedding = embed_cls(hidden_size, vocab_size, max_position_embeddings, embed_drop_rate, dtype=dtype)
blocks = nn.ModuleList([
GPTTransformerLayer1D(hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attn_drop_rate,
hidden_dropout_prob=drop_rate,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm) for _ in range(num_layers)
])
blocks = nn.ModuleList(
[
GPTTransformerLayer1D(
hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attn_drop_rate,
hidden_dropout_prob=drop_rate,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm,
)
for _ in range(num_layers)
]
)
if last:
norm = nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
head = head_cls(vocab_size=vocab_size, embed_dim=hidden_size, dtype=dtype)
@@ -103,25 +108,26 @@ class PipelineGPT1D(GenericPipelineGPT):
class FusedPipelineGPT1D(GenericPipelineGPT):
def __init__(self,
num_layers: int = 12,
hidden_size: int = 768,
num_attention_heads: int = 12,
vocab_size: int = 50304,
embed_drop_rate: float = 0.,
act_func: str = 'gelu',
mlp_ratio: int = 4.0,
attn_drop_rate: float = 0.,
drop_rate: float = 0.,
dtype: torch.dtype = torch.float,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
first: bool = False,
last: bool = False,
embed_split_hidden=False):
def __init__(
self,
num_layers: int = 12,
hidden_size: int = 768,
num_attention_heads: int = 12,
vocab_size: int = 50304,
embed_drop_rate: float = 0.0,
act_func: str = "gelu",
mlp_ratio: int = 4.0,
attn_drop_rate: float = 0.0,
drop_rate: float = 0.0,
dtype: torch.dtype = torch.float,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
first: bool = False,
last: bool = False,
embed_split_hidden=False,
):
embedding = None
norm = None
head = None
@@ -132,19 +138,24 @@ class FusedPipelineGPT1D(GenericPipelineGPT):
head_cls = HiddenParallelGPTLMHead1D
if first:
embedding = embed_cls(hidden_size, vocab_size, max_position_embeddings, embed_drop_rate, dtype=dtype)
blocks = nn.ModuleList([
FusedGPTTransformerLayer1D(hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attn_drop_rate,
hidden_dropout_prob=drop_rate,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm) for _ in range(num_layers)
])
blocks = nn.ModuleList(
[
FusedGPTTransformerLayer1D(
hidden_size,
num_attention_heads,
act_func=act_func,
mlp_ratio=mlp_ratio,
attention_dropout_prob=attn_drop_rate,
hidden_dropout_prob=drop_rate,
dtype=dtype,
checkpoint=checkpoint,
max_position_embeddings=max_position_embeddings,
layer_norm_epsilon=layer_norm_epsilon,
apply_post_layer_norm=apply_post_layer_norm,
)
for _ in range(num_layers)
]
)
if last:
norm = kernel.LayerNorm(hidden_size, eps=layer_norm_epsilon)
head = head_cls(vocab_size=vocab_size, embed_dim=hidden_size, dtype=dtype)
@@ -153,7 +164,7 @@ class FusedPipelineGPT1D(GenericPipelineGPT):
def forward(self, hidden_states=None, input_ids=None, attention_mask=None):
if self.embedding is not None:
hidden_states = self.embedding(input_ids=input_ids)
attention_mask = attention_mask.to(dtype=hidden_states.dtype) # fp16 compatibility
attention_mask = attention_mask.to(dtype=hidden_states.dtype) # fp16 compatibility
for block in self.blocks:
hidden_states, attention_mask = block(hidden_states, attention_mask)
if self.norm is not None:
@@ -162,44 +173,48 @@ class FusedPipelineGPT1D(GenericPipelineGPT):
class PipelineGPTHybrid(GenericPipelineGPT):
def __init__(self,
num_layers: int = 12,
hidden_size: int = 768,
num_attention_heads: int = 12,
vocab_size: int = 50304,
embed_drop_rate: float = 0.,
act_func: str = 'gelu',
mlp_ratio: int = 4,
attn_drop_rate: float = 0.,
drop_rate: float = 0.,
dtype: torch.dtype = torch.float,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
first: bool = False,
last: bool = False,
embed_split_hidden=False):
def __init__(
self,
num_layers: int = 12,
hidden_size: int = 768,
num_attention_heads: int = 12,
vocab_size: int = 50304,
embed_drop_rate: float = 0.0,
act_func: str = "gelu",
mlp_ratio: int = 4,
attn_drop_rate: float = 0.0,
drop_rate: float = 0.0,
dtype: torch.dtype = torch.float,
checkpoint: bool = False,
max_position_embeddings: int = 1024,
layer_norm_epsilon: float = 1e-5,
apply_post_layer_norm: bool = False,
first: bool = False,
last: bool = False,
embed_split_hidden=False,
):
embedding = None
norm = None
head = None
if first:
embedding = col_gpt.GPTEmbedding(hidden_size,
vocab_size,
max_position_embeddings,
dropout=embed_drop_rate,
dtype=dtype)
blocks = nn.ModuleList([
col_gpt.GPTBlock(hidden_size,
num_attention_heads,
mlp_ratio=mlp_ratio,
attention_dropout=attn_drop_rate,
dropout=drop_rate,
dtype=dtype,
checkpoint=checkpoint,
activation=nn.functional.gelu) for _ in range(num_layers)
])
embedding = col_gpt.GPTEmbedding(
hidden_size, vocab_size, max_position_embeddings, dropout=embed_drop_rate, dtype=dtype
)
blocks = nn.ModuleList(
[
col_gpt.GPTBlock(
hidden_size,
num_attention_heads,
mlp_ratio=mlp_ratio,
attention_dropout=attn_drop_rate,
dropout=drop_rate,
dtype=dtype,
checkpoint=checkpoint,
activation=nn.functional.gelu,
)
for _ in range(num_layers)
]
)
if last:
norm = col_nn.LayerNorm(hidden_size, eps=layer_norm_epsilon)
# head = col_gpt.GPTLMHead(vocab_size=vocab_size,
@@ -215,7 +230,7 @@ def _filter_kwargs(func, kwargs):
return {k: v for k, v in kwargs.items() if k in sig.parameters}
def _build_generic_gpt_pipeline_1d(module_cls, num_layers, num_chunks, device=torch.device('cuda'), **kwargs):
def _build_generic_gpt_pipeline_1d(module_cls, num_layers, num_chunks, device=torch.device("cuda"), **kwargs):
logger = get_dist_logger()
if gpc.is_initialized(ParallelMode.PIPELINE):
@@ -233,10 +248,10 @@ def _build_generic_gpt_pipeline_1d(module_cls, num_layers, num_chunks, device=to
parts = partition_uniform(num_layers, pipeline_size, num_chunks)[pipeline_rank]
models = []
for start, end in parts:
kwargs['num_layers'] = end - start
kwargs['first'] = start == 0
kwargs['last'] = end == num_layers
logger.info(f'Rank{rank} build layer {start}-{end}, {end-start}/{num_layers} layers')
kwargs["num_layers"] = end - start
kwargs["first"] = start == 0
kwargs["last"] = end == num_layers
logger.info(f"Rank{rank} build layer {start}-{end}, {end-start}/{num_layers} layers")
chunk = module_cls(**_filter_kwargs(module_cls.__init__, kwargs)).to(device)
if wrapper is not None:
@@ -253,70 +268,82 @@ def _build_generic_gpt_pipeline_1d(module_cls, num_layers, num_chunks, device=to
numel = 0
for _, param in model.named_parameters(recurse=True):
numel += param.numel()
logger.info(f'Rank{rank}/{pipeline_rank} model size = {numel * 2 / 1e9} GB')
logger.info(f"Rank{rank}/{pipeline_rank} model size = {numel * 2 / 1e9} GB")
return model
def _build_gpt_pipeline_1d(num_layers, num_chunks, device=torch.device('cuda'), fused=False, **kwargs):
def _build_gpt_pipeline_1d(num_layers, num_chunks, device=torch.device("cuda"), fused=False, **kwargs):
model = FusedPipelineGPT1D if fused else PipelineGPT1D
return _build_generic_gpt_pipeline_1d(model, num_layers, num_chunks, device, **kwargs)
def _build_gpt_pipeline_hybrid(num_layers, num_chunks, device=torch.device('cuda'), **kwargs):
def _build_gpt_pipeline_hybrid(num_layers, num_chunks, device=torch.device("cuda"), **kwargs):
return _build_generic_gpt_pipeline_1d(PipelineGPTHybrid, num_layers, num_chunks, device, **kwargs)
def GPT2_small_pipeline_1D(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False, fused=False):
cfg = dict(hidden_size=768,
num_attention_heads=12,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden)
cfg = dict(
hidden_size=768,
num_attention_heads=12,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden,
)
return _build_gpt_pipeline_1d(12, num_chunks, fused=fused, **cfg)
def GPT2_exlarge_pipeline_1D(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False, fused=False):
cfg = dict(hidden_size=1600,
num_attention_heads=32,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden)
cfg = dict(
hidden_size=1600,
num_attention_heads=32,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden,
)
return _build_gpt_pipeline_1d(48, num_chunks, fused=fused, **cfg)
def GPT3_pipeline_1D(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False, fused=False):
cfg = dict(hidden_size=12288,
num_attention_heads=96,
checkpoint=checkpoint,
max_position_embeddings=2048,
dtype=dtype,
embed_split_hidden=embed_split_hidden)
cfg = dict(
hidden_size=12288,
num_attention_heads=96,
checkpoint=checkpoint,
max_position_embeddings=2048,
dtype=dtype,
embed_split_hidden=embed_split_hidden,
)
return _build_gpt_pipeline_1d(96, num_chunks, fused=fused, **cfg)
def GPT2_exlarge_pipeline_hybrid(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False):
cfg = dict(hidden_size=1600,
num_attention_heads=32,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden)
cfg = dict(
hidden_size=1600,
num_attention_heads=32,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden,
)
return _build_gpt_pipeline_hybrid(48, num_chunks, **cfg)
def GPT2_small_pipeline_hybrid(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False):
cfg = dict(hidden_size=768,
num_attention_heads=12,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden)
cfg = dict(
hidden_size=768,
num_attention_heads=12,
checkpoint=checkpoint,
dtype=dtype,
embed_split_hidden=embed_split_hidden,
)
return _build_gpt_pipeline_hybrid(12, num_chunks, **cfg)
def GPT3_pipeline_hybrid(num_chunks=1, checkpoint=False, dtype=torch.float, embed_split_hidden=False):
cfg = dict(hidden_size=12288,
num_attention_heads=96,
checkpoint=checkpoint,
max_position_embeddings=2048,
dtype=dtype,
embed_split_hidden=embed_split_hidden)
cfg = dict(
hidden_size=12288,
num_attention_heads=96,
checkpoint=checkpoint,
max_position_embeddings=2048,
dtype=dtype,
embed_split_hidden=embed_split_hidden,
)
return _build_gpt_pipeline_hybrid(96, num_chunks, **cfg)