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DeepSpeed/deepspeed/ops/transformer/transformer.py
Ma, Guokai 9548d48f48 Abstract accelerator (step 2) (#2560) 
* Abstract accelerator (step 2)

* more flex op_builder path for both installation and runtime

* add SpatialInferenceBuilder into cuda_accelerator.py

* use reflection to make cuda_accelerator adapt to CUDA op builder change automatically

* clean up deepspeed/__init__.py

* add comments in cuda_accelerator for no torch path

* Update deepspeed/env_report.py

Change env_report.py according to suggestion

Co-authored-by: Michael Wyatt <mrwyattii@gmail.com>

* reduce the range of try...except for better code clarity

* Add porting for deepspeed/ops/random_ltd/dropping_utils.py

* move accelerator to top directory and create symlink under deepspeed

Co-authored-by: Olatunji Ruwase <olruwase@microsoft.com>
Co-authored-by: Michael Wyatt <mrwyattii@gmail.com>
Co-authored-by: Jeff Rasley <jerasley@microsoft.com>
2023-01-06 23:40:58 -05:00

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'''
Copyright 2020 The Microsoft DeepSpeed Team
'''
import json
import math
import torch
from torch import nn
from torch.autograd import Function
from deepspeed.accelerator import get_accelerator
from deepspeed.ops.op_builder.builder_names import TransformerBuilder, StochasticTransformerBuilder
# Cuda modules will be imported if needed
transformer_cuda_module = None
stochastic_transformer_cuda_module = None
class TransformerConfig():
def __init__(self,
batch_size,
hidden_size,
intermediate_size,
heads,
attn_dropout_ratio,
hidden_dropout_ratio,
num_hidden_layers,
initializer_range):
self.layer_id = -1
self.batch_size = batch_size
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.heads = heads
self.attn_dropout_ratio = attn_dropout_ratio
self.hidden_dropout_ratio = hidden_dropout_ratio
self.num_hidden_layers = num_hidden_layers
self.initializer_range = initializer_range
class DeepSpeedTransformerConfig(TransformerConfig):
"""Initialize the DeepSpeed Transformer Config.
Arguments:
batch_size: The maximum batch size used for running the kernel on each GPU
hidden_size: The hidden size of the transformer layer
intermediate_size: The intermediate size of the feed-forward part of transformer layer
heads: The number of heads in the self-attention of the transformer layer
attn_dropout_ratio: The ratio of dropout for the attention's output
hidden_dropout_ratio: The ratio of dropout for the transformer's output
num_hidden_layers: The number of transformer layers
initializer_range: BERT model's initializer range for initializing parameter data
local_rank: Optional: The rank of GPU running the transformer kernel, it is not required
to use if the model already set the current device, otherwise need to set it
so that the transformer kernel can work on the right device
seed: The random seed for the dropout layers
fp16: Enable half-precision computation
pre_layer_norm: Select between Pre-LN or Post-LN transformer architecture
normalize_invertible: Optional: Enable invertible LayerNorm execution (dropping the input activation),
default is False
gelu_checkpoint: Optional: Enable checkpointing of Gelu activation output to save memory,
default is False
adjust_init_range: Optional: Set as True (default) if the model adjusts the weight initial values of
its self-attention output and layer output, False keeps the initializer_range no change.
See the adjustment below:
output_std = self.config.initializer_range / math.sqrt(2.0 * num_layers)
attn_dropout_checkpoint: Optional: Enable checkpointing of attention dropout to save memory,
default is False
stochastic_mode: Enable for high performance, please note that this flag has some level of
non-determinism and can produce different results on different runs. However, we have seen
that by enabling it, the pretraining tasks such as BERT are not affected and can obtain
a high accuracy level. On the other hand, for the downstream tasks, such as fine-tuning, we recommend
to turn it off in order to be able to reproduce the same result through the regular kernel execution.
return_tuple: Enable if using the return_tuple interface style for sending out the forward results.
training: Enable for training rather than inference.
"""
def __init__(self,
batch_size=-1,
hidden_size=-1,
intermediate_size=-1,
heads=-1,
attn_dropout_ratio=-1,
hidden_dropout_ratio=-1,
num_hidden_layers=-1,
initializer_range=-1,
layer_norm_eps=1e-12,
local_rank=-1,
seed=-1,
fp16=False,
pre_layer_norm=True,
normalize_invertible=False,
gelu_checkpoint=False,
adjust_init_range=True,
attn_dropout_checkpoint=False,
stochastic_mode=False,
return_tuple=False,
training=True):
super(DeepSpeedTransformerConfig,
self).__init__(
batch_size,
hidden_size,
(intermediate_size if intermediate_size > 0 else 4 * hidden_size),
heads,
attn_dropout_ratio,
hidden_dropout_ratio,
num_hidden_layers,
initializer_range)
self.fp16 = fp16
self.pre_layer_norm = pre_layer_norm
self.local_rank = local_rank
self.seed = seed
self.normalize_invertible = normalize_invertible
self.gelu_checkpoint = gelu_checkpoint # True: if higher batch size is required
self.adjust_init_range = adjust_init_range
self.test_gemm = False
self.layer_norm_eps = layer_norm_eps
self.training = training
self.is_grad_enabled = True
self.attn_dropout_checkpoint = attn_dropout_checkpoint
self.stochastic_mode = stochastic_mode
self.return_tuple = return_tuple
@classmethod
def from_dict(cls, json_object):
config = DeepSpeedTransformerConfig()
for key, value in json_object.items():
config.__dict__[key] = value
return config
@classmethod
def from_json_file(cls, json_file):
with open(json_file, "r", encoding='utf-16') as reader:
text = reader.read()
return cls.from_dict(json.loads(text))
class DeepSpeedTransformerFunction(Function):
@staticmethod
def forward(ctx,
input,
input_mask,
self,
grads,
layer_id,
attn_qkvw,
attn_qkvb,
attn_ow,
attn_ob,
attn_nw,
attn_nb,
inter_w,
inter_b,
output_w,
output_b,
norm_w,
norm_b,
config):
cuda_module = stochastic_transformer_cuda_module if config.stochastic_mode else transformer_cuda_module
forward_func = cuda_module.forward_fp16 if config.fp16 else cuda_module.forward_fp32
inp_size = input.size()
if inp_size[1] % 16 != 0:
input = torch.cat((input,
torch.randn((inp_size[0],
(16 - (inp_size[1] % 16)),
inp_size[2]),
device=input.device,
dtype=input.dtype)),
1)
input_mask = torch.cat((input_mask, torch.ones((inp_size[0], input_mask.shape[1], input_mask.shape[2], \
(16 - (inp_size[1] % 16))), device=input_mask.device, dtype=input_mask.dtype) * -10000), 3)
(output,
inp_norm,
qkv_tf,
soft_inp,
ctx_bufB,
attn_o_inp,
add_res,
ff1_inp,
gelu_inp,
ff2_inp,
attn_prob_dropout_mask,
attn_output_dropout_mask,
layer_output_dropout_mask,
attn_layer_norm_var,
attn_layer_norm_mean,
layer_norm_var,
layer_norm_mean) = forward_func(config.layer_id,
input,
input_mask,
attn_qkvw,
attn_qkvb,
attn_ow,
attn_ob,
attn_nw,
attn_nb,
inter_w,
inter_b,
output_w,
output_b,
norm_w,
norm_b,
config.training and config.is_grad_enabled,
config.pre_layer_norm,
config.attn_dropout_checkpoint,
config.normalize_invertible,
config.gelu_checkpoint)
# For testing only.
if grads is not None:
for i in [2]:
attn_qkvw.register_hook(
lambda x,
i=i,
self=self: grads.append([
x[i * attn_ow.size(0):(i + 1) * attn_ow.size(0)],
("Q_W" if i == 0 else "K_W" if i == 1 else "V_W")
]))
for i in [2]:
attn_qkvb.register_hook(
lambda x,
i=i,
self=self: grads.append([
x[i * attn_ow.size(0):(i + 1) * attn_ow.size(0)],
("Q_B" if i == 0 else "K_B" if i == 1 else "V_B")
]))
attn_ow.register_hook(lambda x, self=self: grads.append([x, "O_W"]))
attn_ob.register_hook(lambda x, self=self: grads.append([x, "O_B"]))
attn_nw.register_hook(lambda x, self=self: grads.append([x, "N2_W"]))
attn_nb.register_hook(lambda x, self=self: grads.append([x, "N2_B"]))
inter_w.register_hook(lambda x, self=self: grads.append([x, "int_W"]))
inter_b.register_hook(lambda x, self=self: grads.append([x, "int_B"]))
output_w.register_hook(lambda x, self=self: grads.append([x, "out_W"]))
output_b.register_hook(lambda x, self=self: grads.append([x, "out_B"]))
norm_w.register_hook(lambda x, self=self: grads.append([x, "norm_W"]))
norm_b.register_hook(lambda x, self=self: grads.append([x, "norm_B"]))
if config.is_grad_enabled and config.training:
if (config.pre_layer_norm and config.normalize_invertible):
ctx.save_for_backward(input_mask,
attn_qkvw,
attn_qkvb,
attn_ow,
attn_ob,
attn_nw,
attn_nb,
inter_w,
inter_b,
output_w,
output_b,
norm_w,
norm_b)
else:
ctx.save_for_backward(output,
input,
input_mask,
attn_qkvw,
attn_qkvb,
attn_ow,
attn_ob,
attn_nw,
attn_nb,
inter_w,
inter_b,
output_w,
output_b,
norm_w,
norm_b)
ctx.config = config
if (config.pre_layer_norm or not config.normalize_invertible):
ctx.inp_norm = inp_norm
ctx.qkv_tf = qkv_tf
ctx.soft_inp = soft_inp
if not config.attn_dropout_checkpoint:
ctx.ctx_bufB = ctx_bufB
ctx.attn_o_inp = attn_o_inp
if not config.normalize_invertible:
ctx.add_res = add_res
ctx.attn_layer_norm_mean = attn_layer_norm_mean
ctx.layer_norm_mean = layer_norm_mean
ctx.ff1_inp = ff1_inp
if not config.gelu_checkpoint:
ctx.gelu_inp = gelu_inp
ctx.ff2_inp = ff2_inp
ctx.attn_prob_dropout_mask = attn_prob_dropout_mask
ctx.attn_output_dropout_mask = attn_output_dropout_mask
ctx.layer_output_dropout_mask = layer_output_dropout_mask
ctx.attn_layer_norm_var = attn_layer_norm_var
ctx.layer_norm_var = layer_norm_var
if inp_size[1] % 16 != 0:
output = torch.narrow(output, 1, 0, inp_size[1])
if config.return_tuple:
return (output, ) # outputs -> (output) : outputs[0] = output
else:
return output
@staticmethod
def backward(ctx, grad_output):
bsz = grad_output.shape[0]
grad_output_shape = grad_output.size()
if grad_output_shape[1] % 16 != 0:
grad_output = torch.cat((grad_output, torch.zeros((bsz, (16 - (grad_output_shape[1] % 16)), \
grad_output_shape[2]), device=grad_output.device, dtype=grad_output.dtype)), 1)
assert ctx.config.training
if (ctx.config.pre_layer_norm and ctx.config.normalize_invertible):
(input_mask,
attn_qkvw,
attn_qkvb,
attn_ow,
attn_ob,
attn_nw,
attn_nb,
inter_w,
inter_b,
output_w,
output_b,
norm_w,
norm_b) = ctx.saved_tensors
else:
(output,
input,
input_mask,
attn_qkvw,
attn_qkvb,
attn_ow,
attn_ob,
attn_nw,
attn_nb,
inter_w,
inter_b,
output_w,
output_b,
norm_w,
norm_b) = ctx.saved_tensors
cuda_module = stochastic_transformer_cuda_module if ctx.config.stochastic_mode else transformer_cuda_module
backward_func = cuda_module.backward_fp16 if ctx.config.fp16 else cuda_module.backward_fp32
(grad_input,
grad_attn_qkvw,
grad_attn_qkvb,
grad_attn_ow,
grad_attn_ob,
grad_attn_nw,
grad_attn_nb,
grad_inter_w,
grad_inter_b,
grad_output_w,
grad_output_b,
grad_norm_w,
grad_norm_b) = backward_func(
ctx.config.layer_id,
grad_output,
(ctx.inp_norm if (ctx.config.pre_layer_norm
and ctx.config.normalize_invertible) else output),
(ctx.inp_norm if (ctx.config.pre_layer_norm
or not ctx.config.normalize_invertible) else input),
ctx.qkv_tf,
ctx.soft_inp,
(ctx.soft_inp if ctx.config.attn_dropout_checkpoint else ctx.ctx_bufB),
ctx.attn_o_inp,
(ctx.ff1_inp if ctx.config.normalize_invertible else ctx.add_res),
ctx.ff1_inp,
(ctx.ff2_inp if ctx.config.gelu_checkpoint else ctx.gelu_inp),
ctx.ff2_inp,
ctx.attn_prob_dropout_mask,
ctx.attn_output_dropout_mask,
ctx.layer_output_dropout_mask,
ctx.attn_layer_norm_var,
ctx.attn_layer_norm_mean,
ctx.layer_norm_var,
ctx.layer_norm_mean,
(ctx.inp_norm if (ctx.config.pre_layer_norm
and ctx.config.normalize_invertible) else input),
input_mask,
attn_qkvw,
attn_qkvb,
attn_ow,
attn_ob,
attn_nw,
attn_nb,
inter_w,
inter_b,
output_w,
output_b,
norm_w,
norm_b)
# This appears to be an effective way to release context memory
ctx.qkv_tf = None
ctx.soft_inp = None
ctx.ctx_bufB = None
ctx.gelu_inp = None
ctx.ff2_inp = None
ctx.attn_o_inp = None
ctx.ff1_inp = None
ctx.add_res = None
ctx.inp_norm = None
ctx.config = None
ctx.attn_layer_norm_mean = None
ctx.layer_norm_mean = None
ctx.attn_prob_dropout_mask = None
ctx.attn_output_dropout_mask = None
ctx.layer_output_dropout_mask = None
ctx.attn_layer_norm_var = None
ctx.layer_norm_var = None
if grad_output_shape[1] % 16 != 0:
grad_input = torch.narrow(grad_input, 1, 0, grad_output_shape[1])
return (grad_input,
None,
None,
None,
None,
grad_attn_qkvw,
grad_attn_qkvb,
grad_attn_ow,
grad_attn_ob,
grad_attn_nw,
grad_attn_nb,
grad_inter_w,
grad_inter_b,
grad_output_w,
grad_output_b,
grad_norm_w,
grad_norm_b,
None)
class DeepSpeedTransformerLayer(nn.Module):
"""Initialize the DeepSpeed Transformer Layer.
Static variable:
layer_id: The layer-index counter starting from 0 and incrementing by 1 every time a layer object is instantiated,
e.g. if a model has 24 transformer layers, layer_id goes from 0 to 23.
Arguments:
config: An object of DeepSpeedTransformerConfig
initial_weights: Optional: Only used for unit test
initial_biases: Optional: Only used for unit test
"""
layer_id = 0
def __init__(self, config, initial_weights=None, initial_biases=None):
super(DeepSpeedTransformerLayer, self).__init__()
self.config = config
self.config.layer_id = DeepSpeedTransformerLayer.layer_id
DeepSpeedTransformerLayer.layer_id = DeepSpeedTransformerLayer.layer_id + 1
print("DeepSpeed Transformer config is ", self.config.__dict__)
if self.config.local_rank >= 0:
get_accelerator().set_device(self.config.local_rank)
if initial_weights is None and initial_biases is None:
self.attn_qkvw = nn.Parameter(
torch.Tensor(self.config.hidden_size * 3,
self.config.hidden_size))
self.attn_qkvb = nn.Parameter(torch.Tensor(self.config.hidden_size * 3))
self.attn_ow = nn.Parameter(
torch.Tensor(self.config.hidden_size,
self.config.hidden_size))
self.attn_ob = nn.Parameter(torch.Tensor(self.config.hidden_size))
self.attn_nw = nn.Parameter(torch.Tensor(self.config.hidden_size))
self.attn_nb = nn.Parameter(torch.Tensor(self.config.hidden_size))
self.inter_w = nn.Parameter(
torch.Tensor(self.config.intermediate_size,
self.config.hidden_size))
self.inter_b = nn.Parameter(torch.Tensor(self.config.intermediate_size))
self.output_w = nn.Parameter(
torch.Tensor(self.config.hidden_size,
self.config.intermediate_size))
self.output_b = nn.Parameter(torch.Tensor(self.config.hidden_size))
self.norm_w = nn.Parameter(torch.Tensor(self.config.hidden_size))
self.norm_b = nn.Parameter(torch.Tensor(self.config.hidden_size))
self.init_transformer_weights(self.config.adjust_init_range)
else:
# For testing only.
q = initial_weights[0].data
k = initial_weights[1].data
v = initial_weights[2].data
self.attn_qkvw = nn.Parameter(torch.cat((q, k, v)))
#self.attn_qkvw[i * self.config.hidden_size:(i + 1) * self.config.hidden_size] = \
# initial_weights[i].clone()
#torch.empty_like(initial_weights[i]).data.copy_(initial_weights[i].data)
self.attn_qkvb = nn.Parameter(torch.Tensor(self.config.hidden_size * 3))
self.attn_qkvb.data.zero_()
self.attn_ow = initial_weights[3]
self.attn_ob = initial_biases[3]
self.attn_nw = initial_weights[4]
self.attn_nb = initial_biases[4]
self.inter_w = initial_weights[5]
self.inter_b = initial_biases[5]
self.output_w = initial_weights[6]
self.output_b = initial_biases[6]
self.norm_w = initial_weights[7]
self.norm_b = initial_biases[7]
# Load cuda modules if needed
global transformer_cuda_module, stochastic_transformer_cuda_module
if transformer_cuda_module is None and not self.config.stochastic_mode:
transformer_cuda_module = get_accelerator().create_op_builder(
TransformerBuilder).load()
if stochastic_transformer_cuda_module is None and self.config.stochastic_mode:
stochastic_transformer_cuda_module = get_accelerator().create_op_builder(
StochasticTransformerBuilder).load()
# create the layer in cuda kernels.
cuda_module = stochastic_transformer_cuda_module if self.config.stochastic_mode else transformer_cuda_module
create_layer_func = cuda_module.create_transformer_layer_fp16 if self.config.fp16 else cuda_module.create_transformer_layer_fp32
create_layer_func(self.config.layer_id,
self.config.batch_size,
self.config.hidden_size,
self.config.heads,
self.config.intermediate_size,
self.config.attn_dropout_ratio,
self.config.hidden_dropout_ratio,
self.config.layer_norm_eps,
self.config.seed,
self.config.pre_layer_norm,
self.config.test_gemm,
self.config.attn_dropout_checkpoint,
self.config.normalize_invertible,
self.config.gelu_checkpoint,
self.config.stochastic_mode)
def init_transformer_weights(self, adjust_init_range=False):
num_layers = self.config.num_hidden_layers
output_std = self.config.initializer_range
if adjust_init_range and self.config.local_rank == 0:
print("Accounting for accumulation on the residual path")
output_std = self.config.initializer_range / math.sqrt(2.0 * num_layers)
self.attn_qkvw.data.normal_(mean=0.0, std=self.config.initializer_range)
self.attn_qkvb.data.zero_()
self.attn_ow.data.normal_(mean=0.0, std=output_std)
self.attn_ob.data.zero_()
self.attn_nw.data.fill_(1.0)
self.attn_nb.data.zero_()
self.inter_w.data.normal_(mean=0.0, std=self.config.initializer_range)
self.inter_b.data.zero_()
self.output_w.data.normal_(mean=0.0, std=output_std)
self.output_b.data.zero_()
self.norm_w.data.fill_(1.0)
self.norm_b.data.zero_()
def forward(self,
hidden_states,
attention_mask=None,
head_mask=None,
layer_head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
grads=None):
self.config.is_grad_enabled = torch.is_grad_enabled()
return DeepSpeedTransformerFunction.apply(hidden_states,
attention_mask,
self,
grads,
self.config.layer_id,
self.attn_qkvw,
self.attn_qkvb,
self.attn_ow,
self.attn_ob,
self.attn_nw,
self.attn_nb,
self.inter_w,
self.inter_b,
self.output_w,
self.output_b,
self.norm_w,
self.norm_b,
self.config)
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