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[format] applied code formatting on changed files in PR 5124 #5125

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Merged
github-actions merged 1 commit into from
Nov 29, 2023
Merged

[format] applied code formatting on changed files in PR 5124 #5125

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14 changes: 10 additions & 4 deletions colossalai/booster/plugin/hybrid_parallel_plugin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,8 +28,8 @@
from colossalai.shardformer.layer.utils import SeqParallelUtils
from colossalai.shardformer.policies.base_policy import Policy
from colossalai.tensor.d_tensor.api import is_distributed_tensor
from colossalai.zero.low_level import LowLevelZeroOptimizer
from colossalai.utils.device import get_current_device
from colossalai.zero.low_level import LowLevelZeroOptimizer

from .pp_plugin_base import PipelinePluginBase

Expand Down Expand Up @@ -385,7 +385,9 @@ def _compute_grad_norm(self, param_gradient_pairs: List[Tuple[Tensor]], norm_typ

total_norm_exponentiated += grad_norm_exponentiated

total_norm_exponentiated_cuda = torch.tensor([float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32)
total_norm_exponentiated_cuda = torch.tensor(
[float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32
)
if self.tp_size > 1:
# compute norm in tp process group
dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.tp_pg)
Expand Down Expand Up @@ -586,7 +588,9 @@ def _compute_grad_norm(self, param_gradient_pairs: List[Tuple[Tensor]], norm_typ

total_norm_exponentiated += grad_norm_exponentiated

total_norm_exponentiated_cuda = torch.tensor([float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32)
total_norm_exponentiated_cuda = torch.tensor(
[float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32
)
if self.tp_size > 1:
# compute norm in tp process group
dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.tp_pg)
Expand Down Expand Up @@ -837,7 +841,9 @@ def _compute_grad_norm(self, gradients: List[Tensor], norm_type: int = 2) -> flo

total_norm_exponentiated += grad_norm_exponentiated

total_norm_exponentiated_cuda = torch.tensor([float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32)
total_norm_exponentiated_cuda = torch.tensor(
[float(total_norm_exponentiated)], device=get_current_device(), dtype=torch.float32
)
if dp_size > 1:
# compute norm in dp process group
dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.dp_pg)
Expand Down
19 changes: 13 additions & 6 deletions tests/test_booster/test_plugin/test_gemini_plugin.py
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Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
from contextlib import nullcontext
from typing import Optional
import pytest

import pytest
import torch
import torch.distributed as dist

Expand All @@ -11,8 +11,6 @@
from colossalai.fx import is_compatible_with_meta
from colossalai.lazy.lazy_init import LazyInitContext
from colossalai.nn.optimizer import HybridAdam
from colossalai.tensor.d_tensor.api import clear_layout_converter
from colossalai.shardformer.layer.utils import Randomizer
from colossalai.tensor.colo_parameter import ColoParameter
from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
from tests.kit.model_zoo import model_zoo
Expand All @@ -26,7 +24,13 @@ def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn, zero_size, t
ctx = nullcontext()
extra_dp_size = dist.get_world_size() // (zero_size * tp_size)
enable_all_optimization = True if tp_size > 1 else False
plugin = GeminiPlugin(max_norm=1.0, initial_scale=2**5, tp_size=tp_size, extra_dp_size=extra_dp_size, enable_all_optimization=enable_all_optimization)
plugin = GeminiPlugin(
max_norm=1.0,
initial_scale=2**5,
tp_size=tp_size,
extra_dp_size=extra_dp_size,
enable_all_optimization=enable_all_optimization,
)
booster = Booster(plugin=plugin)
with ctx:
model = model_fn()
Expand Down Expand Up @@ -66,7 +70,9 @@ def run_fn(init_method, model_fn, data_gen_fn, output_transform_fn, zero_size, t
@parameterize("init_method", ["none"])
@parameterize("zero_size", [2])
@parameterize("tp_size", [2])
def check_gemini_plugin(subset: str, init_method: str = "none", early_stop: bool = True, zero_size: int = 1, tp_size: int = 1):
def check_gemini_plugin(
subset: str, init_method: str = "none", early_stop: bool = True, zero_size: int = 1, tp_size: int = 1
):
"""check gemini plugin over model zoo

Args:
Expand Down Expand Up @@ -156,11 +162,12 @@ def run_dist(rank, world_size, port, early_stop: bool = True):
def test_gemini_plugin(early_stop: bool = True):
spawn(run_dist, 4, early_stop=early_stop)


@pytest.mark.largedist
@rerun_if_address_is_in_use()
def test_gemini_plugin_3d(early_stop: bool = True):
spawn(run_dist, 8, early_stop=early_stop)


if __name__ == "__main__":
test_gemini_plugin(early_stop=False)
test_gemini_plugin(early_stop=False)