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[MetaSchedule] Enable BertTuning with MetaScheduler #11

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Jan 16, 2022
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[MetaSchedule] Enable BertTuning with MetaScheduler #11

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ccd146b
Test bert using gluon model.
zxybazh Jan 12, 2022
742ca0f
Minor fix.
zxybazh Jan 13, 2022
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52 changes: 52 additions & 0 deletions python/tvm/meta_schedule/testing/relay_workload.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@ class MODEL_TYPE(Enum): # pylint: disable=invalid-name
VIDEO_CLASSIFICATION = (2,)
SEGMENTATION = (3,)
OBJECT_DETECTION = (4,)
TEXT_CLASSIFICATION = (5,)


# Specify the type of each model
Expand Down Expand Up @@ -95,6 +96,11 @@ class MODEL_TYPE(Enum): # pylint: disable=invalid-name
"r3d_18": MODEL_TYPE.VIDEO_CLASSIFICATION,
"mc3_18": MODEL_TYPE.VIDEO_CLASSIFICATION,
"r2plus1d_18": MODEL_TYPE.VIDEO_CLASSIFICATION,
# Text classification
"bert_tiny": MODEL_TYPE.TEXT_CLASSIFICATION,
"bert_base": MODEL_TYPE.TEXT_CLASSIFICATION,
"bert_medium": MODEL_TYPE.TEXT_CLASSIFICATION,
"bert_large": MODEL_TYPE.TEXT_CLASSIFICATION,
}


Expand All @@ -121,6 +127,8 @@ def get_torch_model(

import torch # type: ignore # pylint: disable=import-error,import-outside-toplevel
from torchvision import models # type: ignore # pylint: disable=import-error,import-outside-toplevel
import transformers # type: ignore # pylint: disable=import-error,import-outside-toplevel
import os # type: ignore # pylint: disable=import-error,import-outside-toplevel

def do_trace(model, inp):
model_trace = torch.jit.trace(model, inp)
Expand All @@ -136,6 +144,50 @@ def do_trace(model, inp):
model = getattr(models.detection, model_name)()
elif MODEL_TYPES[model_name] == MODEL_TYPE.VIDEO_CLASSIFICATION:
model = getattr(models.video, model_name)()
elif MODEL_TYPES[model_name] == MODEL_TYPE.TEXT_CLASSIFICATION:
os.environ["TOKENIZERS_PARALLELISM"] = "false"
config_dict = {
"bert_tiny": transformers.BertConfig(
num_hidden_layers=6,
hidden_size=512,
intermediate_size=2048,
num_attention_heads=8,
return_dict=False,
),
"bert_base": transformers.BertConfig(
num_hidden_layers=12,
hidden_size=768,
intermediate_size=3072,
num_attention_heads=12,
return_dict=False,
),
"bert_medium": transformers.BertConfig(
num_hidden_layers=12,
hidden_size=1024,
intermediate_size=4096,
num_attention_heads=16,
return_dict=False,
),
"bert_large": transformers.BertConfig(
num_hidden_layers=24,
hidden_size=1024,
intermediate_size=4096,
num_attention_heads=16,
return_dict=False,
),
}
configuration = config_dict[model_name]
model = transformers.BertModel(configuration)
input_name = "input_ids"
A = torch.randint(10000, input_shape)

model.eval()
scripted_model = torch.jit.trace(model, [A], strict=False)

input_name = "input_ids"
shape_list = [(input_name, input_shape)]
mod, params = relay.frontend.from_pytorch(scripted_model, shape_list)
return mod, params
else:
raise ValueError("Unsupported model in Torch model zoo.")

Expand Down
8 changes: 4 additions & 4 deletions src/meta_schedule/space_generator/post_order_apply.cc
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Expand Up @@ -95,10 +95,10 @@ class PostOrderApplyNode : public SpaceGeneratorNode {

Array<tir::Schedule> GenerateDesignSpace(const IRModule& mod_) final {
using ScheduleAndUnvisitedBlocks = std::pair<tir::Schedule, Array<tir::BlockRV>>;
tir::Schedule sch = tir::Schedule::Traced( //
/*mod=*/mod_, //
/*rand_state=*/ForkSeed(&this->rand_state_), //
/*debug_mode=*/tir::kVerifySRefTree | tir::kVerifyCachedFlags, //
tir::Schedule sch = tir::Schedule::Traced( //
/*mod=*/mod_, //
/*rand_state=*/ForkSeed(&this->rand_state_), //
/*debug_mode=*/tir::kVerifySRefTree | tir::kVerifyCachedFlags,
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/*error_render_level=*/tir::ScheduleErrorRenderLevel::kDetail);

std::vector<ScheduleAndUnvisitedBlocks> stack;
Expand Down
2 changes: 1 addition & 1 deletion src/meta_schedule/task_scheduler/task_scheduler.cc
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Expand Up @@ -124,7 +124,7 @@ void TaskSchedulerNode::Tune() {

int running_tasks = tasks.size();
for (int task_id; (task_id = NextTaskId()) != -1;) {
LOG(INFO) << "Scheduler picks Task #" << task_id << ": " << tasks[task_id]->task_name;
LOG(INFO) << "Scheduler picks Task #" << task_id + 1 << ": " << tasks[task_id]->task_name;
TuneContext task = tasks[task_id];
ICHECK(!task->is_stopped);
ICHECK(!task->runner_futures.defined());
Expand Down
12 changes: 8 additions & 4 deletions tests/python/unittest/test_meta_schedule_tune_relay.py
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Expand Up @@ -31,7 +31,7 @@


@pytest.mark.skip("Integration test")
@pytest.mark.parametrize("model_name", ["resnet18"])
@pytest.mark.parametrize("model_name", ["resnet18", "mobilenet_v2", "bert_base"])
@pytest.mark.parametrize("batch_size", [1])
@pytest.mark.parametrize("target", ["llvm --num-cores=16", "nvidia/geforce-rtx-3070"])
def test_meta_schedule_tune_relay(model_name: str, batch_size: int, target: str):
Expand All @@ -47,6 +47,9 @@ def test_meta_schedule_tune_relay(model_name: str, batch_size: int, target: str)
input_shape = (1, 3, 300, 300)
elif MODEL_TYPES[model_name] == MODEL_TYPE.VIDEO_CLASSIFICATION:
input_shape = (batch_size, 3, 3, 299, 299)
elif MODEL_TYPES[model_name] == MODEL_TYPE.TEXT_CLASSIFICATION:
seq_length = 128
input_shape = (batch_size, seq_length)
else:
raise ValueError("Unsupported model: " + model_name)
output_shape: Tuple[int, int] = (batch_size, 1000)
Expand All @@ -71,7 +74,7 @@ def test_meta_schedule_tune_relay(model_name: str, batch_size: int, target: str)
work_dir=work_dir,
)
for i, sch in enumerate(schs):
print("-" * 10 + f" Part {i}/{len(schs)} " + "-" * 10)
print("-" * 10 + f" Part {i+1}/{len(schs)} " + "-" * 10)
if sch is None:
print("No valid schedule found!")
else:
Expand All @@ -80,5 +83,6 @@ def test_meta_schedule_tune_relay(model_name: str, batch_size: int, target: str)


if __name__ == """__main__""":
test_meta_schedule_tune_relay("resnet18", 1, "llvm --num-cores=16")
test_meta_schedule_tune_relay("resnet18", 1, "nvidia/geforce-rtx-3070")
# test_meta_schedule_tune_relay("resnet18", 1, "llvm --num-cores=16")
# test_meta_schedule_tune_relay("mobilenet_v2", 1, "nvidia/geforce-rtx-3070")
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test_meta_schedule_tune_relay("bert_base", 1, "llvm --num-cores=16")