def replace_fn(child):
transformer_config = deepspeed.DeepSpeedTransformerConfig(
batch_size=micro_batch_size,
hidden_size=bert_config.hidden_size,
heads=bert_config.num_attention_heads,
attn_dropout_ratio=bert_config.attention_probs_dropout_prob,
hidden_dropout_ratio=bert_config.hidden_dropout_prob,
num_hidden_layers=bert_config.num_hidden_layers,
initializer_range=bert_config.initializer_range,
layer_norm_eps=bert_config.layer_norm_eps,
seed=seed,
fp16=fp16,
pre_layer_norm=preln,
huggingface=huggingface,
local_rank=local_rank,
training=training)
new_module = deepspeed.DeepSpeedTransformerLayer(transformer_config)
# copy relevant state from child -> new module
qw = child.attention.self.query.weight
qb = child.attention.self.query.bias
kw = child.attention.self.key.weight
kb = child.attention.self.key.bias
vw = child.attention.self.value.weight
vb = child.attention.self.value.bias
qkvw = torch.cat((qw, kw, vw), 0)
qkvb = torch.cat((qb, kb, vb), 0)
#qw.data,kw.data,vw.data = torch.chunk(qkvw, 3, axis=0)
#qb.data,kb.data,vb.data = torch.chunk(qkvb, 3, axis=0)
new_module.attn_qkvw.data = qkvw
new_module.attn_qkvb.data = qkvb
new_module.attn_ow.data = child.attention.output.dense.weight
new_module.attn_ob.data = child.attention.output.dense.bias
if preln:
attention_layernorm = child.PostAttentionLayerNorm
else:
attention_layernorm = child.attention.output.LayerNorm
new_module.attn_nw.data = attention_layernorm.weight
new_module.attn_nb.data = attention_layernorm.bias
if preln:
intermediate_ff = child.intermediate.dense_act
else:
intermediate_ff = child.intermediate.dense
new_module.inter_w.data = intermediate_ff.weight
new_module.inter_b.data = intermediate_ff.bias
new_module.output_w.data = child.output.dense.weight
new_module.output_b.data = child.output.dense.bias
if preln:
transformer_layernorm = child.PreAttentionLayerNorm
else:
transformer_layernorm = child.output.LayerNorm
new_module.norm_w.data = transformer_layernorm.weight
new_module.norm_b.data = transformer_layernorm.bias
return new_module
def replace_fn(child, layer_id=0):
if training:
transformer_config = deepspeed.DeepSpeedTransformerConfig(
batch_size=micro_batch_size,
hidden_size=bert_config.hidden_size,
heads=bert_config.num_attention_heads,
attn_dropout_ratio=bert_config.attention_probs_dropout_prob,
hidden_dropout_ratio=bert_config.hidden_dropout_prob,
num_hidden_layers=bert_config.num_hidden_layers,
initializer_range=bert_config.initializer_range,
seed=seed,
fp16=fp16,
pre_layer_norm=preln,
huggingface=encoder_decoder,
local_rank=local_rank,
training=training)
new_module = deepspeed.DeepSpeedTransformerLayer(
transformer_config)
# copy relevant state from child -> new module
replace_with_policy(new_module, child, policy, preln=preln)
else:
transformer_config = transformer_inference.DeepSpeedInferenceConfig(
hidden_size=hidden_size,
heads=num_attention_heads,
fp16=fp16,
pre_layer_norm=preln,
mp_size=mp_size,
q_int8=quantize,
encoder_decoder=encoder_decoder)
if quantize and quantize_settings is not None:
(quantization_scales, merge_count, mlp_extra_grouping,
quantize_groups) = quantize_settings
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
quantize_scales=quantization_scales[layer_id],
quantize_groups=quantize_groups,
merge_count=merge_count,
mlp_extra_grouping=mlp_extra_grouping)
else:
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
)
# copy relevant state from child -> new module
replace_with_policy(new_module,
child,
policy,
inference=True,
preln=preln)
return new_module
def replace_with_policy(child,
policy_cls,
inference=False,
preln=True,
layer_id=0):
preln = False if policy_cls is HFBertLayerPolicy else preln
if policy_cls is HFBertLayerPolicy:
policy = policy_cls(child, inference=inference, preln=preln)
else:
policy = policy_cls(child, inference=inference)
if inference:
hidden_size, num_attention_heads = policy.get_hidden_heads()
assert num_attention_heads % mp_size == 0,\
"To run the model parallel across the GPUs, the attention_heads require to be divisible by the world_size!" +\
"This is because the attention computation is partitioned evenly among the parallel GPUs."
attn_linear_layer, qkvw, qkvb, dense_w, dense_b, scale_attention = policy.attention(
)
mlp_linear_layer, _h4h_w, _h4h_b, _4hh_w, _4hh_b = policy.mlp()
attn_nw, attn_nb, input_nw, input_nb = policy.layerNorm()
if quantize:
if policy_cls is not HFBertLayerPolicy:
qkvw = qkvw.to(torch.int8)
dense_w = dense_w.to(torch.int8)
_h4h_w = _h4h_w.to(torch.int8)
_4hh_w = _4hh_w.to(torch.int8)
elif fp16:
qkvw = qkvw.half()
dense_w = dense_w.half()
_h4h_w = _h4h_w.half()
_4hh_w = _4hh_w.half()
if quantize or fp16:
dense_b = dense_b.half()
_h4h_b = _h4h_b.half()
_4hh_b = _4hh_b.half()
attn_nw = attn_nw.half()
attn_nb = attn_nb.half()
input_nw = input_nw.half()
input_nb = input_nb.half()
mp_replace = ReplaceWithTensorSlicing(mp_group=mp_group)
if inference:
transformer_config = transformer_inference.DeepSpeedInferenceConfig(
hidden_size=hidden_size,
heads=num_attention_heads,
layer_norm_eps=config.layer_norm_eps if hasattr(
config, 'layer_norm_eps') else 1e-12,
fp16=fp16,
pre_layer_norm=preln,
mp_size=mp_size,
q_int8=quantize,
encoder_decoder=(True if policy_cls is HFBertLayerPolicy else
False),
triangular_masking=(policy_cls is not HFBertLayerPolicy),
local_attention=((config.attention_layers[layer_id]
== "local") if hasattr(
config, 'attention_layers') else False),
window_size=(config.window_size if hasattr(
config, 'window_size') else 1))
if quantize and quantize_settings is not None:
(quantization_scales, merge_count, mlp_extra_grouping,
quantize_groups) = quantize_settings
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
quantize_scales=quantization_scales[layer_id],
quantize_groups=quantize_groups,
merge_count=merge_count,
mlp_extra_grouping=mlp_extra_grouping,
qkv_merging=(policy_cls is HFBertLayerPolicy))
if quantize and qkvw.dtype != torch.int8:
quantize_bits = 8
quantizer = WeightQuantization()
if policy_cls is HFBertLayerPolicy:
data_quantized, _ = quantizer.quantize_data(
qkvw, quantize_bits, quantize_groups * 3)
else:
data_quantized, _ = quantizer.quantize_data(
qkvw, quantize_bits, quantize_groups)
qkvw.copy_(data_quantized)
qkvw = qkvw.to(torch.int8)
else:
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
)
new_module.config.scale_attention = scale_attention
# we want the weights in [input, output] shape
# linear layer is created with [input, output] shape
# transpose it here to reduce inference cost!
def transpose(data):
data.view(-1).copy_(
data.transpose(-1, -2).contiguous().view(-1))
data = data.reshape(data.shape[-1], data.shape[-2])
return data
if attn_linear_layer:
qkvw = transpose(qkvw.data)
dense_w = transpose(dense_w)
if mlp_linear_layer:
_h4h_w = transpose(_h4h_w)
_4hh_w = transpose(_4hh_w)
attn_block = new_module.attention
attn_block.attn_qkvw.data = mp_replace.qkv_copy(
attn_block.attn_qkvw.data, qkvw)
if qkvb is not None:
if fp16:
qkvb = qkvb.half()
attn_block.attn_qkvb.data = mp_replace.qkv_copy(
attn_block.attn_qkvb.data, qkvb)
else:
attn_block.attn_qkvb = qkvb
attn_block.attn_ow.data = mp_replace.copy(attn_block.attn_ow.data,
dense_w)
attn_block.attn_ob.data = mp_replace.copy(attn_block.attn_ob.data,
dense_b)
mpl_block = new_module.mlp
mpl_block.inter_w.data = mp_replace.copy(mpl_block.inter_w.data,
_h4h_w)
mpl_block.inter_b.data = mp_replace.copy(mpl_block.inter_b.data,
_h4h_b)
mpl_block.output_w.data = mp_replace.copy(mpl_block.output_w.data,
_4hh_w)
mpl_block.output_b.data = mp_replace.copy(mpl_block.output_b.data,
_4hh_b)
new_module.mlp.attn_nw.data = attn_nw.to(
torch.cuda.current_device())
new_module.mlp.attn_nb.data = attn_nb.to(
torch.cuda.current_device())
new_module.norm_w.data = input_nw.to(torch.cuda.current_device())
new_module.norm_b.data = input_nb.to(torch.cuda.current_device())
else:
transformer_config = deepspeed.DeepSpeedTransformerConfig(
batch_size=micro_batch_size,
hidden_size=config.hidden_size,
heads=config.num_attention_heads,
attn_dropout_ratio=config.attention_probs_dropout_prob,
hidden_dropout_ratio=config.hidden_dropout_prob,
num_hidden_layers=config.num_hidden_layers,
initializer_range=config.initializer_range,
layer_norm_eps=config.layer_norm_eps if hasattr(
config, 'layer_norm_eps') else 1e-12,
seed=seed,
fp16=fp16,
pre_layer_norm=(False
if policy_cls is HFBertLayerPolicy else preln),
huggingface=encoder_decoder,
local_rank=local_rank,
stochastic_mode=stochastic_mode,
normalize_invertible=True,
training=training)
new_module = deepspeed.DeepSpeedTransformerLayer(
transformer_config)
new_module.attn_qkvw.data = qkvw
new_module.attn_qkvb.data = qkvb
new_module.attn_ow.data = dense_w
new_module.attn_ob.data = dense_b
new_module.attn_nw.data = attn_nw
new_module.attn_nb.data = attn_nb
new_module.norm_w.data = input_nw
new_module.norm_b.data = input_nb
new_module.inter_w.data = _h4h_w
new_module.inter_b.data = _h4h_b
new_module.output_w.data = _4hh_w
new_module.output_b.data = _4hh_b
return new_module
def replace_with_policy(child,
policy_cls,
triangular_masking,
inference=False,
preln=True,
layer_id=0):
preln = False if policy_cls is HFBertLayerPolicy else preln
if policy_cls is HFBertLayerPolicy:
policy = policy_cls(child, inference=inference, preln=preln)
else:
policy = policy_cls(child, inference=inference)
if inference:
hidden_size, num_attention_heads = policy.get_hidden_heads()
assert num_attention_heads % mp_size == 0,\
"To run the model parallel across the GPUs, the attention_heads require to be divisible by the world_size!" +\
"This is because the attention computation is partitioned evenly among the parallel GPUs."
from deepspeed.moe.layer import MoE
moe = False
if isinstance(child.mlp, MoE):
num_experts = child.mlp.num_experts
moe = True
attn_linear_layer, qkvw, qkvb, dense_w, dense_b, scale_attention = policy.attention()
if not moe or moe_type == 'standard':
mlp_linear_layer, _h4h_w, _h4h_b, _4hh_w, _4hh_b = policy.mlp()
else:
mlp_linear_layer, _h4h_w, _h4h_b, _4hh_w, _4hh_b, \
_res_h4h_w, _res_h4h_b, _res_4hh_w, _res_4hh_b, _res_coef = policy.mlp(moe_type)
attn_nw, attn_nb, input_nw, input_nb = policy.layerNorm()
if quantize:
if policy_cls is not HFBertLayerPolicy:
qkvw = qkvw.to(torch.int8)
dense_w = dense_w.to(torch.int8)
_h4h_w = [moe_w1.to(torch.int8)
for moe_w1 in _h4h_w] if moe else _h4h_w.to(torch.int8)
_4hh_w = [moe_w1.to(torch.int8)
for moe_w1 in _4hh_w] if moe else _4hh_w.to(torch.int8)
elif fp16:
qkvw = qkvw.half()
dense_w = dense_w.half()
_h4h_w = [moe_w1.half() for moe_w1 in _h4h_w] if moe else _h4h_w.half()
_4hh_w = [moe_w1.half() for moe_w1 in _4hh_w] if moe else _4hh_w.half()
if quantize or fp16:
qkvb = qkvb if qkvb is None else qkvb.half()
dense_b = dense_b if dense_b is None else dense_b.half()
_h4h_b = [moe_b1.half() for moe_b1 in _h4h_b] if moe else _h4h_b.half()
_4hh_b = [moe_b1.half() for moe_b1 in _4hh_b] if moe else _4hh_b.half()
attn_nw = attn_nw if attn_nw is None else attn_nw.half()
attn_nb = attn_nb if attn_nb is None else attn_nb.half()
input_nw = input_nw.half()
input_nb = input_nb.half()
if moe and moe_type == 'residual' and fp16:
_res_h4h_b = _res_h4h_b.half()
_res_4hh_b = _res_4hh_b.half()
_res_h4h_w = _res_h4h_w.half()
_res_4hh_w = _res_4hh_w.half()
_res_coef = _res_coef.half()
mp_replace = ReplaceWithTensorSlicing(mp_group=mp_group)
#expert_mp_replace = ReplaceWithTensorSlicing(mp_group=expert_mp_group)
if inference:
if moe:
ep_world_size = torch.distributed.get_world_size()
local_ep_size = 1 if num_experts < ep_world_size else num_experts // ep_world_size
transformer_config = transformer_inference.DeepSpeedMoEInferenceConfig(
hidden_size=hidden_size,
heads=num_attention_heads,
layer_norm_eps=config.layer_norm_eps if hasattr(
config,
'layer_norm_eps') else 1e-12,
fp16=fp16,
pre_layer_norm=preln,
mp_size=mp_size,
q_int8=quantize,
moe_experts=local_ep_size,
global_experts=num_experts,
mlp_type=moe_type)
else:
transformer_config = transformer_inference.DeepSpeedInferenceConfig(
hidden_size=hidden_size,
heads=num_attention_heads,
layer_norm_eps=config.layer_norm_eps if hasattr(
config,
'layer_norm_eps') else (config.layer_norm_epsilon if hasattr(
config,
'layer_norm_epsilon') else 1e-12),
fp16=fp16,
pre_layer_norm=preln,
mp_size=mp_size,
q_int8=quantize,
return_tuple=(return_tuple or (policy_cls is HFBertLayerPolicy)),
triangular_masking=(policy_cls is not HFBertLayerPolicy),
local_attention=((config.attention_layers[layer_id] == "local")
if hasattr(config,
'attention_layers') else False),
window_size=(config.window_size if hasattr(config,
'window_size') else 1),
rotary_dim=(config.rotary_dim if hasattr(config,
'rotary_dim') else -1),
mlp_after_attn=(policy_cls is not HFGPTJLayerPolicy))
if quantize and quantize_settings is not None:
(quantization_scales,
merge_count,
mlp_extra_grouping,
quantize_groups) = quantize_settings
if moe:
new_module = transformer_inference.DeepSpeedMoEInference(
transformer_config,
mp_group=mp_group,
ep_group=None if ep_group is None else ep_group[num_experts],
expert_mp_group=None
if expert_mp_group is None else expert_mp_group[num_experts],
quantize_scales=quantization_scales[layer_id],
quantize_groups=quantize_groups,
merge_count=merge_count,
mlp_extra_grouping=mlp_extra_grouping,
qkv_merging=(policy_cls is HFBertLayerPolicy))
else:
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
quantize_scales=quantization_scales[layer_id],
quantize_groups=quantize_groups,
merge_count=merge_count,
mlp_extra_grouping=mlp_extra_grouping,
qkv_merging=(policy_cls is HFBertLayerPolicy))
if quantize and qkvw.dtype != torch.int8:
quantize_bits = 8
quantizer = WeightQuantization()
if policy_cls is HFBertLayerPolicy:
data_quantized, _ = quantizer.quantize_data(qkvw.data, quantize_bits, quantize_groups * 3)
else:
data_quantized, _ = quantizer.quantize_data(qkvw.data, quantize_bits, quantize_groups)
qkvw.data.copy_(data_quantized)
qkvw.data = qkvw.data.to(torch.int8)
else:
if moe:
new_module = transformer_inference.DeepSpeedMoEInference(
transformer_config,
mp_group=mp_group,
ep_group=None if ep_group is None else ep_group[num_experts],
expert_mp_group=None
if expert_mp_group is None else expert_mp_group[num_experts],
)
else:
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
)
new_module.config.scale_attention = scale_attention
# we want the weights in [input, output] shape
# linear layer is created with [input, output] shape
# transpose it here to reduce inference cost!
def transpose(data):
data.view(-1).copy_(data.transpose(-1, -2).contiguous().view(-1))
data = data.reshape(data.shape[-1], data.shape[-2])
return data
if attn_linear_layer:
qkvw.data = transpose(qkvw.data)
dense_w.data = transpose(dense_w.data)
if mlp_linear_layer:
_h4h_w = [transpose(moe_w1.data)
for moe_w1 in _h4h_w] if moe else transpose(_h4h_w.data)
_4hh_w = [transpose(moe_w1.data)
for moe_w1 in _4hh_w] if moe else transpose(_4hh_w.data)
if moe and moe_type == 'residual':
_res_h4h_w.data = transpose(_res_h4h_w.data)
_res_4hh_w.data = transpose(_res_4hh_w.data)
_res_coef.data = transpose(_res_coef.data)
attn_block = new_module.attention
attn_block.attn_qkvw = mp_replace.qkv_copy(attn_block.attn_qkvw, qkvw)
attn_block.attn_qkvb = mp_replace.qkv_copy(attn_block.attn_qkvb, qkvb)
attn_block.attn_ow = mp_replace.copy(attn_block.attn_ow, dense_w)
attn_block.attn_ob = mp_replace.copy(attn_block.attn_ob, dense_b)
mpl_block = new_module.mlp
if moe:
gpu_index = torch.distributed.get_rank()
gpu_index = 0
for ep_index in range(local_ep_size):
mpl_block[ep_index].inter_w.data = _h4h_w[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
mpl_block[ep_index].inter_b.data = _h4h_b[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
mpl_block[ep_index].output_w.data = _4hh_w[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
mpl_block[ep_index].output_b.data = _4hh_b[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
new_module.attn_nw.data = attn_nw.to(torch.cuda.current_device())
new_module.attn_nb.data = attn_nb.to(torch.cuda.current_device())
if moe_type == 'residual':
new_module.res_mlp.inter_w.data = _res_h4h_w.to(
torch.cuda.current_device())
new_module.res_mlp.inter_b.data = _res_h4h_b.to(
torch.cuda.current_device())
new_module.res_mlp.output_w.data = _res_4hh_w.to(
torch.cuda.current_device())
new_module.res_mlp.output_b.data = _res_4hh_b.to(
torch.cuda.current_device())
new_module.res_coef.data = _res_coef.to(torch.cuda.current_device())
else:
mpl_block.inter_w.data = mp_replace.copy(mpl_block.inter_w, _h4h_w)
mpl_block.inter_b.data = mp_replace.copy(mpl_block.inter_b, _h4h_b)
mpl_block.output_w.data = mp_replace.copy(mpl_block.output_w, _4hh_w)
mpl_block.output_b.data = mp_replace.copy(mpl_block.output_b, _4hh_b)
if attn_nw is None:
new_module.mlp.attn_nw = attn_nw
else:
new_module.mlp.attn_nw.data = attn_nw.to(torch.cuda.current_device())
if attn_nb is None:
new_module.mlp.attn_nb = attn_nb
else:
new_module.mlp.attn_nb.data = attn_nb.to(torch.cuda.current_device())
new_module.norm_w.data = input_nw.to(torch.cuda.current_device())
new_module.norm_b.data = input_nb.to(torch.cuda.current_device())
else:
transformer_config = deepspeed.DeepSpeedTransformerConfig(
batch_size=micro_batch_size,
hidden_size=config.hidden_size,
heads=config.num_attention_heads,
attn_dropout_ratio=config.attention_probs_dropout_prob,
hidden_dropout_ratio=config.hidden_dropout_prob,
num_hidden_layers=config.num_hidden_layers,
initializer_range=config.initializer_range,
layer_norm_eps=config.layer_norm_eps if hasattr(
config,
'layer_norm_eps') else 1e-12,
seed=seed,
fp16=fp16,
pre_layer_norm=(False if policy_cls is HFBertLayerPolicy else preln),
return_tuple=return_tuple,
local_rank=local_rank,
stochastic_mode=stochastic_mode,
normalize_invertible=True,
training=training)
new_module = deepspeed.DeepSpeedTransformerLayer(transformer_config)
new_module.attn_qkvw.data = qkvw
new_module.attn_qkvb.data = qkvb
new_module.attn_ow.data = dense_w
new_module.attn_ob.data = dense_b
new_module.attn_nw.data = attn_nw
new_module.attn_nb.data = attn_nb
new_module.norm_w.data = input_nw
new_module.norm_b.data = input_nb
new_module.inter_w.data = _h4h_w
new_module.inter_b.data = _h4h_b
new_module.output_w.data = _4hh_w
new_module.output_b.data = _4hh_b
return new_module
def replace_with_policy(child,
policy_cls,
triangular_masking,
inference=False,
layer_id=0):
policy = policy_cls(child, inference=inference)
if inference:
hidden_size, num_attention_heads = policy.get_hidden_heads()
assert num_attention_heads % mp_size == 0,\
"To run the model parallel across the GPUs, the attention_heads require to be divisible by the world_size!" +\
"This is because the attention computation is partitioned evenly among the parallel GPUs."
from deepspeed.moe.layer import MoE
moe = False
if hasattr(child, 'mlp') and isinstance(child.mlp, MoE):
num_experts = child.mlp.num_experts
moe = True
attn_linear_layer, qkvw, qkvb, dense_w, dense_b, scale_attention, megatron_v2 = policy.attention()
if not moe or moe_type == 'standard':
mlp_linear_layer, _h4h_w, _h4h_b, _4hh_w, _4hh_b = policy.mlp()
else:
mlp_linear_layer, _h4h_w, _h4h_b, _4hh_w, _4hh_b, \
_res_h4h_w, _res_h4h_b, _res_4hh_w, _res_4hh_b, _res_coef = policy.mlp(moe_type)
attn_nw, attn_nb, input_nw, input_nb = policy.layerNorm()
if quantize:
if policy_cls is not HFBertLayerPolicy:
qkvw = qkvw.to(torch.int8)
dense_w = dense_w.to(torch.int8)
_h4h_w = [moe_w1.to(torch.int8)
for moe_w1 in _h4h_w] if moe else _h4h_w.to(torch.int8)
_4hh_w = [moe_w1.to(torch.int8)
for moe_w1 in _4hh_w] if moe else _4hh_w.to(torch.int8)
elif fp16:
qkvw = qkvw.half()
dense_w = dense_w.half()
_h4h_w = [moe_w1.half() for moe_w1 in _h4h_w] if moe else _h4h_w.half()
_4hh_w = [moe_w1.half() for moe_w1 in _4hh_w] if moe else _4hh_w.half()
if quantize or fp16:
qkvb = qkvb if qkvb is None else qkvb.half()
dense_b = dense_b if dense_b is None else dense_b.half()
_h4h_b = [moe_b1.half() for moe_b1 in _h4h_b] if moe else _h4h_b.half()
_4hh_b = [moe_b1.half() for moe_b1 in _4hh_b] if moe else _4hh_b.half()
attn_nw = attn_nw if attn_nw is None else attn_nw.half()
attn_nb = attn_nb if attn_nb is None else attn_nb.half()
input_nw = input_nw.half()
input_nb = input_nb.half()
if moe and moe_type == 'residual' and fp16:
_res_h4h_b = _res_h4h_b.half()
_res_4hh_b = _res_4hh_b.half()
_res_h4h_w = _res_h4h_w.half()
_res_4hh_w = _res_4hh_w.half()
_res_coef = _res_coef.half()
#expert_mp_replace = ReplaceWithTensorSlicing(mp_group=expert_mp_group)
if inference:
if moe:
ep_world_size = dist.get_world_size()
local_ep_size = 1 if num_experts < ep_world_size else num_experts // ep_world_size
transformer_config = transformer_inference.DeepSpeedMoEInferenceConfig(
hidden_size=hidden_size,
heads=num_attention_heads,
layer_norm_eps=config.layer_norm_eps if hasattr(
config,
'layer_norm_eps') else 1e-12,
fp16=fp16,
pre_layer_norm=policy.pre_attn_norm,
mp_size=mp_size,
q_int8=quantize,
moe_experts=local_ep_size,
global_experts=num_experts,
mlp_type=moe_type)
else:
rotary_dim = config.rotary_dim if hasattr(config, 'rotary_dim') else child.attention.rotary_ndims \
if hasattr(child, 'attention') and hasattr(child.attention,'rotary_ndims') else -1
bigscience_bloom = policy_cls is BLOOMLayerPolicy
transformer_config = transformer_inference.DeepSpeedInferenceConfig(
hidden_size=hidden_size,
heads=num_attention_heads,
layer_norm_eps=config.layer_norm_eps if hasattr(
config,
'layer_norm_eps') else
(config.layer_norm_epsilon
if hasattr(config,
'layer_norm_epsilon') else config.layernorm_epsilon
if hasattr(config,
'layernorm_epsilon') else 1.0e-12),
fp16=fp16,
pre_layer_norm=policy.pre_attn_norm,
mp_size=mp_size,
q_int8=quantize,
return_tuple=(return_tuple or (policy_cls is HFBertLayerPolicy)),
triangular_masking=(policy_cls is not HFBertLayerPolicy),
local_attention=((config.attention_layers[layer_id] == "local")
if hasattr(config,
'attention_layers') else False),
window_size=(config.window_size if hasattr(config,
'window_size') else 1),
rotary_dim=rotary_dim,
mlp_after_attn=(rotary_dim is None or rotary_dim < 0),
mlp_act_func_type=policy.mlp_act_func_type,
training_mp_size=training_mp_size,
bigscience_bloom=bigscience_bloom)
if quantize and quantize_settings is not None:
(quantization_scales,
merge_count,
mlp_extra_grouping,
quantize_groups) = quantize_settings
if moe:
new_module = transformer_inference.DeepSpeedMoEInference(
transformer_config,
mp_group=mp_group,
ep_group=None if ep_group is None else ep_group[num_experts],
expert_mp_group=None
if expert_mp_group is None else expert_mp_group[num_experts],
quantize_scales=quantization_scales[layer_id],
quantize_groups=quantize_groups,
merge_count=merge_count,
mlp_extra_grouping=mlp_extra_grouping,
qkv_merging=(policy_cls is HFBertLayerPolicy))
else:
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
quantize_scales=quantization_scales[layer_id],
quantize_groups=quantize_groups,
merge_count=merge_count,
mlp_extra_grouping=mlp_extra_grouping,
qkv_merging=(policy_cls is HFBertLayerPolicy))
if quantize and qkvw.dtype != torch.int8:
quantize_bits = 8
quantizer = WeightQuantization()
if policy_cls is HFBertLayerPolicy:
data_quantized, _ = quantizer.quantize_data(qkvw.data, quantize_bits, quantize_groups * 3)
else:
data_quantized, _ = quantizer.quantize_data(qkvw.data, quantize_bits, quantize_groups)
qkvw.data.copy_(data_quantized)
qkvw.data = qkvw.data.to(torch.int8)
else:
if moe:
new_module = transformer_inference.DeepSpeedMoEInference(
transformer_config,
mp_group=mp_group,
ep_group=None if ep_group is None else ep_group[num_experts],
expert_mp_group=None
if expert_mp_group is None else expert_mp_group[num_experts],
)
else:
new_module = transformer_inference.DeepSpeedTransformerInference(
transformer_config,
mp_group=mp_group,
)
new_module.config.scale_attention = scale_attention
# we want the weights in [input, output] shape
# linear layer is created with [input, output] shape
# transpose it here to reduce inference cost!
def transpose(data):
# temp move to cpu to avoid requiring extra GPU memory during the reshape
data = data.to('cpu')
data.reshape(-1).copy_(data.transpose(-1, -2).contiguous().reshape(-1))
data = data.reshape(data.shape[-1], data.shape[-2])
data.to(torch.cuda.current_device())
return data
attn_block = new_module.attention
mpl_block = new_module.mlp
if attn_linear_layer:
if qkvw.numel() == 0 or qkvw.is_meta:
if qkvw.is_meta or qkvw.ds_tensor.numel(
) < attn_block.attn_qkvw.numel():
pass
else:
with GatheredParameters([qkvw,
dense_w,
qkvb,
dense_b],
modifier_rank=0):
qkvw = transpose(qkvw.data)
dense_w = transpose(dense_w.data)
qkvb = qkvb.data
dense_b = dense_b.data
else:
qkvw.data = transpose(qkvw.data)
dense_w.data = transpose(dense_w.data)
def _transpose(x):
num_attention_heads_per_partition = transformer_config.heads // transformer_config.mp_size
attention_head_size = x.shape[-1] // num_attention_heads_per_partition
new_x_shape = x.size()[:-1] + (num_attention_heads_per_partition,
attention_head_size)
x_1 = x.view(*new_x_shape)
(q, k, v) = torch.split(x_1, (x_1.shape[-1] // 3), dim=(x_1.dim() - 1))
if len(q.shape) > 2:
return torch.cat((q.reshape(q.shape[0],
-1),
k.reshape(q.shape[0],
-1),
v.reshape(q.shape[0],
-1)),
dim=-1).reshape(x.shape)
else:
return torch.cat((q.reshape(-1),
k.reshape(-1),
v.reshape(-1)),
dim=-1).reshape(x.shape)
if megatron_v2:
new_module.config.rotate_half = True
new_module.config.rotate_every_two = False
# Note: this part needs to be added for BLOOM architecture
qkvw = torch.nn.parameter.Parameter(_transpose(qkvw).contiguous())
qkvb = torch.nn.parameter.Parameter(_transpose(qkvb).contiguous())
# NOTE: This part caused instability in the multi-GPU inference!
# TODO: This needs to be incorporated in the kernels.
#dense_b = dense_b if dense_b is None else dense_b * (
# transformer_config.training_mp_size / transformer_config.mp_size)
#_4hh_b = _4hh_b * (transformer_config.training_mp_size /
# transformer_config.mp_size)
if mlp_linear_layer:
if not moe and (_4hh_w.numel() == 0 or _4hh_w.is_meta):
if _4hh_w.is_meta or _4hh_w.ds_tensor.numel(
) < mpl_block.inter_w.numel():
pass
else:
with GatheredParameters([_h4h_w,
_4hh_w,
_4hh_b,
_h4h_b],
modifier_rank=0):
_h4h_w = transpose(_h4h_w.data)
_4hh_w = transpose(_4hh_w.data)
_h4h_b = _h4h_b.data
_4hh_b = _4hh_b.data
else:
_h4h_w = [transpose(moe_w1.data)
for moe_w1 in _h4h_w] if moe else transpose(_h4h_w.data)
_4hh_w = [transpose(moe_w1.data)
for moe_w1 in _4hh_w] if moe else transpose(_4hh_w.data)
if moe and moe_type == 'residual':
_res_h4h_w.data = transpose(_res_h4h_w.data)
_res_4hh_w.data = transpose(_res_4hh_w.data)
_res_coef.data = transpose(_res_coef.data)
if qkvw.is_meta or qkvw.numel() == 0 or qkvw.is_meta:
if qkvw.is_meta or qkvw.ds_tensor.numel() < attn_block.attn_qkvw.numel():
pass
else:
with GatheredParameters([
attn_block.attn_qkvw,
attn_block.attn_qkvb,
attn_block.attn_ow,
attn_block.attn_ob
],
modifier_rank=0):
attn_block.attn_qkvw = mp_replace.copy(
attn_block.attn_qkvw,
qkvw)
attn_block.attn_qkvb = mp_replace.copy(
attn_block.attn_qkvb,
qkvb)
attn_block.attn_ow = mp_replace.copy(attn_block.attn_ow, dense_w)
attn_block.attn_ob = mp_replace.copy(attn_block.attn_ob, dense_b)
else:
if bigscience_bloom:
attn_block.attn_qkvw = mp_replace.copy(attn_block.attn_qkvw, qkvw)
attn_block.attn_qkvb = mp_replace.copy(attn_block.attn_qkvb, qkvb)
else:
attn_block.attn_qkvw = mp_replace.qkv_copy(
attn_block.attn_qkvw,
qkvw)
attn_block.attn_qkvb = mp_replace.qkv_copy(
attn_block.attn_qkvb,
qkvb)
attn_block.attn_ow = mp_replace.copy(attn_block.attn_ow, dense_w)
attn_block.attn_ob = mp_replace.copy(attn_block.attn_ob, dense_b)
if moe:
gpu_index = dist.get_rank()
gpu_index = 0
for ep_index in range(local_ep_size):
mpl_block[ep_index].inter_w.data = _h4h_w[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
mpl_block[ep_index].inter_b.data = _h4h_b[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
mpl_block[ep_index].output_w.data = _4hh_w[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
mpl_block[ep_index].output_b.data = _4hh_b[
gpu_index * local_ep_size + ep_index].to(
torch.cuda.current_device())
new_module.attn_nw.data = attn_nw.to(torch.cuda.current_device())
new_module.attn_nb.data = attn_nb.to(torch.cuda.current_device())
if moe_type == 'residual':
new_module.res_mlp.inter_w.data = _res_h4h_w.to(
torch.cuda.current_device())
new_module.res_mlp.inter_b.data = _res_h4h_b.to(
torch.cuda.current_device())
new_module.res_mlp.output_w.data = _res_4hh_w.to(
torch.cuda.current_device())
new_module.res_mlp.output_b.data = _res_4hh_b.to(
torch.cuda.current_device())
new_module.res_coef.data = _res_coef.to(torch.cuda.current_device())
else:
if _4hh_w.numel() == 0 or _4hh_w.is_meta:
if _4hh_w.is_meta or _4hh_w.ds_tensor.numel(
) < mpl_block.inter_w.numel():
pass
else:
with GatheredParameters([_h4h_w,
_4hh_w,
_4hh_w,
_4hh_b],
modifier_rank=0):
mpl_block.inter_w = mp_replace.copy(
mpl_block.inter_w,
_h4h_w)
mpl_block.inter_b = mp_replace.copy(
mpl_block.inter_b,
_h4h_b)
mpl_block.output_w = mp_replace.copy(
mpl_block.output_w,
_4hh_w)
mpl_block.output_b = mp_replace.copy(
mpl_block.output_b,
_4hh_b)
else:
mpl_block.inter_w = mp_replace.copy(mpl_block.inter_w, _h4h_w)
mpl_block.inter_b = mp_replace.copy(mpl_block.inter_b, _h4h_b)
mpl_block.output_w = mp_replace.copy(mpl_block.output_w, _4hh_w)
mpl_block.output_b = mp_replace.copy(mpl_block.output_b, _4hh_b)
if attn_nw is None:
new_module.mlp.attn_nw = attn_nw
new_module.mlp.attn_nb = attn_nb
else:
if attn_nw.is_meta or attn_nw.numel() == 0:
if attn_nw.is_meta or attn_nw.ds_tensor.numel(
) < new_module.mlp.attn_nw.numel():
pass
else:
with GatheredParameters([attn_nw, attn_nb], modifier_rank=0):
new_module.mlp.attn_nw.data.copy_(
attn_nw.to(torch.cuda.current_device()))
new_module.mlp.attn_nb.data.copy_(
attn_nb.to(torch.cuda.current_device()))
else:
new_module.mlp.attn_nw.data.copy_(
attn_nw.to(torch.cuda.current_device()))
new_module.mlp.attn_nb.data.copy_(
attn_nb.to(torch.cuda.current_device()))
if input_nw.is_meta or input_nw.numel() == 0:
if input_nw.is_meta or input_nw.ds_tensor.numel(
) < new_module.norm_w.numel():
pass
else:
with GatheredParameters([input_nw, input_nb], modifier_rank=0):
new_module.norm_w.data.copy_(
input_nw.to(torch.cuda.current_device()))
new_module.norm_b.data.copy_(
input_nb.to(torch.cuda.current_device()))
else:
new_module.norm_w.data.copy_(input_nw.to(torch.cuda.current_device()))
new_module.norm_b.data.copy_(input_nb.to(torch.cuda.current_device()))
else:
transformer_config = deepspeed.DeepSpeedTransformerConfig(
batch_size=micro_batch_size if micro_batch_size > 0 else 1,
hidden_size=config.hidden_size,
heads=config.num_attention_heads,
attn_dropout_ratio=config.attention_probs_dropout_prob,
hidden_dropout_ratio=config.hidden_dropout_prob,
num_hidden_layers=config.num_hidden_layers,
initializer_range=config.initializer_range,
layer_norm_eps=config.layer_norm_eps if hasattr(
config,
'layer_norm_eps') else 1e-12,
seed=seed,
fp16=fp16,
pre_layer_norm=policy.pre_attn_norm,
return_tuple=return_tuple,
local_rank=local_rank,
stochastic_mode=stochastic_mode,
normalize_invertible=True,
training=training)
new_module = deepspeed.DeepSpeedTransformerLayer(transformer_config)
new_module.attn_qkvw.data = qkvw
new_module.attn_qkvb.data = qkvb
new_module.attn_ow.data = dense_w
new_module.attn_ob.data = dense_b
new_module.attn_nw.data = attn_nw
new_module.attn_nb.data = attn_nb
new_module.norm_w.data = input_nw
new_module.norm_b.data = input_nb
new_module.inter_w.data = _h4h_w
new_module.inter_b.data = _h4h_b
new_module.output_w.data = _4hh_w
new_module.output_b.data = _4hh_b
return new_module
def replace_fn(child):
new_module = deepspeed.DeepSpeedTransformerLayer(transformer_config)
_copy_child_transformer_state(new_module, child,
transformer_config.pre_layer_norm)
return new_module
