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refine device type #488

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3c715f8
refine device type
xiezipeng-ML Apr 1, 2023
8ad86a6
Merge branch 'main' into refine_device_type
xiezipeng-ML Apr 7, 2023
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2 changes: 2 additions & 0 deletions configs/common/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,6 +128,8 @@
# NOTE: if it is None, LiBai will automatically set pipeline_stage_id
# `auto_pipeline_stage_id` and `actual_pipeline_stage_id` will be saved in `config.yaml`
custom_pipeline_stage_id=None,
# set device type
device_type="cuda",
),

# the device type of input tensors for model, defaults to "cuda".
Expand Down
22 changes: 14 additions & 8 deletions libai/inference/generator/generation_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,9 @@


class Generator:
dist_utils = dist.get_dist_util()
device_type = dist_utils.device_type

def _prepare_model_inputs(
self,
inputs: Optional[flow.Tensor] = None,
Expand Down Expand Up @@ -101,7 +104,7 @@ def _prepare_input_ids_for_generation(
shape,
dtype=flow.long,
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(self.device_type, list(range(dist.get_world_size()))),
)
* -100
)
Expand All @@ -113,7 +116,7 @@ def _prepare_input_ids_for_generation(
(1, 1),
dtype=flow.long,
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(self.device_type, list(range(dist.get_world_size()))),
)
* bos_token_id
)
Expand All @@ -137,7 +140,7 @@ def _prepare_attention_mask_for_generation(
inputs.shape[:2],
dtype=flow.bool,
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(dist.device_type, list(range(dist.get_world_size()))),
)

def _prepare_encoder_decoder_kwargs_for_generation(
Expand Down Expand Up @@ -171,7 +174,7 @@ def _prepare_decoder_input_ids_for_generation(
(batch_size, 1),
dtype=flow.long,
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(dist.device_type, list(range(dist.get_world_size()))),
)
* decoder_start_token_id
)
Expand All @@ -195,14 +198,15 @@ def _expand_inputs_for_generation(
is_encoder_decoder: bool = False,
attention_mask: Optional[flow.Tensor] = None,
encoder_outputs: Optional[flow.Tensor] = None,
device_type="cuda",
**model_kwargs,
):
expanded_return_idx = (
flow.arange(input_ids.shape[0]).view(-1, 1).repeat(1, expand_size).view(-1)
)
expanded_return_idx = expanded_return_idx.to_global(
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(device_type, list(range(dist.get_world_size()))),
)

input_ids = input_ids.index_select(0, expanded_return_idx)
Expand Down Expand Up @@ -589,12 +593,12 @@ def multinomial_sample(
probs = nn.functional.softmax(next_token_scores, dim=-1)
probs = probs.to_global(
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(self.device_type, list(range(dist.get_world_size()))),
).to_local()
next_tokens = flow.multinomial(probs, num_samples=1).squeeze(1)
next_tokens = next_tokens.to_global(
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(self.device_type, list(range(dist.get_world_size()))),
)
unfinished_sequences = unfinished_sequences.to_global(
sbp=next_tokens.sbp, placement=next_tokens.placement
Expand Down Expand Up @@ -687,7 +691,7 @@ def beam_search(
(batch_size, num_beams),
dtype=flow.float,
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=flow.placement("cuda", list(range(dist.get_world_size()))),
placement=flow.placement(self.device_type, list(range(dist.get_world_size()))),
)
beam_scores[:, 1:] = -1e9
beam_scores = beam_scores.view((batch_size * num_beams,))
Expand Down Expand Up @@ -1019,6 +1023,7 @@ def generate(
input_ids,
expand_size=num_return_sequences,
is_encoder_decoder=self.cfg.is_encoder_decoder,
device_type=self.device_type,
**model_kwargs,
)

Expand Down Expand Up @@ -1057,6 +1062,7 @@ def generate(
input_ids,
expand_size=num_beams,
is_encoder_decoder=self.cfg.is_encoder_decoder,
device_type=self.device_type,
**model_kwargs,
)

Expand Down
5 changes: 4 additions & 1 deletion libai/tokenizer/tokenization_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -138,6 +138,8 @@ class PreTrainedTokenizer(object):
pretrained_vocab_files_map = {}
pretrained_init_configuration = {}
max_model_input_sizes = {}
dist_utils = dist.get_dist_util()
device_type = dist_utils.device_type

SPECIAL_TOKENS_ATTRIBUTES = [
"bos_token",
Expand Down Expand Up @@ -783,7 +785,8 @@ def convert_to_tensors(self, token_ids, return_tensors=None, is_global=False, **
elif is_global:
sbp = kwargs.get("sbp", dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]))
placement = kwargs.get(
"placement", flow.placement("cuda", list(range(dist.get_world_size())))
"placement",
flow.placement(self.device_type, list(range(dist.get_world_size()))),
)
return_token_ids = flow.tensor(
token_ids, sbp=sbp, placement=placement, dtype=flow.long
Expand Down
4 changes: 3 additions & 1 deletion libai/utils/distributed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
# limitations under the License.

import logging
import os

import dill
import numpy as np
Expand Down Expand Up @@ -438,7 +439,8 @@ def convert_to_distributed_default_setting(t):
def ttol(tensor, pure_local=False, ranks=None):
"""Global tensor to local tensor."""
if tensor.is_global:
placement = tensor.placement if not ranks else flow.placement("cuda", ranks)
device_type = os.getenv("DEVICE_TYPE", "cuda")
placement = tensor.placement if not ranks else flow.placement(device_type, ranks)
if pure_local:
tensor = tensor.to_global(placement=placement).to_local()
else:
Expand Down
20 changes: 14 additions & 6 deletions projects/MagicPrompt/layers/attention_layer.py
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Expand Up @@ -208,12 +208,20 @@ def forward(
causal_mask = causal_mask.repeat(attention_scores.size(0), 1, 1, 1)
causal_mask = causal_mask.to_global(placement=attention_scores.placement)
fill_value = flow.finfo(attention_scores.dtype).min
mask_value = flow.ones(
causal_mask.size(),
dtype=attention_scores.dtype,
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=attention_scores.placement,
).fill_(fill_value)
if causal_mask.shape[0] == 1:
mask_value = flow.ones(
causal_mask.size(),
dtype=attention_scores.dtype,
sbp=dist.get_nd_sbp([flow.sbp.broadcast, flow.sbp.broadcast]),
placement=attention_scores.placement,
).fill_(fill_value)
else:
mask_value = flow.ones(
causal_mask.size(),
dtype=attention_scores.dtype,
sbp=dist.get_nd_sbp([flow.sbp.split(0), flow.sbp.broadcast]),
placement=attention_scores.placement,
).fill_(fill_value)
attention_scores = flow.where(causal_mask, attention_scores, mask_value)

if attention_mask is not None:
Expand Down