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gpu benchmrak add fp32

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pull/8905/head
VectorSL 4 years ago
parent 0b3aa904c0
commit ff0cdd0bd3
8 changed files with 56 additions and 23 deletions
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1
mindspore/ops/_op_impl/akg/gpu/equal.py
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@ -23,6 +23,7 @@ equal_op_info = AkgGpuRegOp("Equal") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \

1
mindspore/ops/_op_impl/akg/gpu/greater_equal.py
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@ -23,6 +23,7 @@ greater_equal_op_info = AkgGpuRegOp("GreaterEqual") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \

1
mindspore/ops/_op_impl/akg/gpu/lessequal.py
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@ -23,6 +23,7 @@ lessequal_op_info = AkgGpuRegOp("LessEqual") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F64_Default, DataType.F64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \

1
mindspore/ops/_op_impl/akg/gpu/notequal.py
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@ -22,6 +22,7 @@ notequal_op_info = AkgGpuRegOp("NotEqual") \
.output(0, "output") \
.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \

2
model_zoo/official/cv/resnet/README.md
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@ -277,7 +277,7 @@ sh run_standalone_train_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATA
sh run_eval_gpu.sh [resnet50|resnet101] [cifar10|imagenet2012] [DATASET_PATH] [CHECKPOINT_PATH]
# gpu benchmark example
sh run_gpu_resnet_benchmark.sh [IMAGENET_DATASET_PATH] [BATCH_SIZE](optional) [DEVICE_NUM](optional)
sh run_gpu_resnet_benchmark.sh [IMAGENET_DATASET_PATH] [BATCH_SIZE](optional) [DTYPE](optional) [DEVICE_NUM](optional)
```
#### Running parameter server mode training

22
model_zoo/official/cv/resnet/gpu_resnet_benchmark.py
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@ -39,6 +39,8 @@ parser.add_argument('--epoch_size', type=str, default="2", help='Epoch_size: def
parser.add_argument('--print_per_steps', type=str, default="20", help='Print loss and time per steps: default 20')
parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
parser.add_argument('--dataset_path', type=str, default=None, help='Imagenet dataset path')
parser.add_argument('--dtype', type=str, choices=["fp32", "fp16", "FP16", "FP32"], default="fp16",\
help='Compute data type fp32 or fp16: default fp16')
args_opt = parser.parse_args()
set_seed(1)
@ -60,7 +62,7 @@ def pad(image):
output = np.concatenate((image, zeros), axis=2)
return output
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="GPU"):
def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="GPU", dtype="fp16"):
ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=4, shuffle=True)
image_size = 224
@ -81,9 +83,11 @@ def create_dataset(dataset_path, do_train, repeat_num=1, batch_size=32, target="
C.CenterCrop(image_size),
C.Normalize(mean=mean, std=std),
]
if dtype == "fp32":
trans.append(C.HWC2CHW())
ds = ds.map(operations=trans, input_columns="image", num_parallel_workers=4)
ds = ds.map(operations=pad, input_columns="image", num_parallel_workers=4)
if dtype == "fp16":
ds = ds.map(operations=pad, input_columns="image", num_parallel_workers=4)
# apply batch operations
ds = ds.batch(batch_size, drop_remainder=True)
# apply dataset repeat operation
@ -112,6 +116,7 @@ if __name__ == '__main__':
epoch_size = int(args_opt.epoch_size)
total_batch = int(args_opt.batch_size)
print_per_steps = int(args_opt.print_per_steps)
compute_type = str(args_opt.dtype).lower()
# init context
context.set_context(mode=context.GRAPH_MODE, device_target=dev, save_graphs=False)
@ -122,14 +127,14 @@ if __name__ == '__main__':
# create dataset
dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
batch_size=total_batch, target=dev)
batch_size=total_batch, target=dev, dtype=compute_type)
step_size = dataset.get_dataset_size()
if (print_per_steps > step_size or print_per_steps < 1):
print("Arg: print_per_steps should lessequal to dataset_size ", step_size)
print("Change to default: 20")
print_per_steps = 20
# define net
net = resnet(class_num=1001)
net = resnet(class_num=1001, dtype=compute_type)
# init weight
for _, cell in net.cells_and_names():
@ -163,10 +168,11 @@ if __name__ == '__main__':
loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, 0.9, 1e-4, 1024)
loss_scale = FixedLossScaleManager(1024, drop_overflow_update=False)
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})
# Mixed precision
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False)
if compute_type == "fp16":
model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
amp_level="O2", keep_batchnorm_fp32=False)
# define callbacks
time_cb = MyTimeMonitor(total_batch, print_per_steps)
loss_cb = LossMonitor()

17
model_zoo/official/cv/resnet/scripts/run_gpu_resnet_benchmark.sh
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@ -14,10 +14,11 @@
# limitations under the License.
# ============================================================================
if [ $# != 1 ] && [ $# != 2 ] && [ $# != 3 ]
if [ $# != 1 ] && [ $# != 2 ] && [ $# != 3 ] && [ $# != 4 ]
then
echo "Usage: sh run_gpu_resnet_benchmark.sh [DATASET_PATH] [BATCH_SIZE](optional) [DEVICE_NUM](optional)"
echo "Example: sh run_gpu_resnet_benchmark.sh /path/imagenet/train 256 8"
echo "Usage: sh run_gpu_resnet_benchmark.sh [DATASET_PATH] [BATCH_SIZE](optional) [DTYPE](optional)\
[DEVICE_NUM](optional)"
echo "Example: sh run_gpu_resnet_benchmark.sh /path/imagenet/train 256 FP16 8"
exit 1
fi
@ -44,6 +45,12 @@ fi
if [ $# == 3 ]
then
mpirun --allow-run-as-root -n $3 python ${self_path}/../gpu_resnet_benchmark.py --run_distribute=True \
--dataset_path=$DATAPATH --batch_size=$2
python ${self_path}/../gpu_resnet_benchmark.py --run_distribute=True --dtype=$3 \
--dataset_path=$DATAPATH --batch_size=$2
fi
if [ $# == 4 ]
then
mpirun --allow-run-as-root -n $4 python ${self_path}/../gpu_resnet_benchmark.py --run_distribute=True \
--dataset_path=$DATAPATH --batch_size=$2 --dtype=$3
fi

34
model_zoo/official/cv/resnet/src/resnet_gpu_benchmark.py
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@ -20,6 +20,13 @@ from mindspore.ops import operations as P
from mindspore.common.tensor import Tensor
from scipy.stats import truncnorm
format_ = "NHWC"
# tranpose shape to NCHW, default init is NHWC.
def _trans_shape(shape, shape_format):
if shape_format == "NCHW":
return (shape[0], shape[3], shape[1], shape[2])
return shape
def _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size):
fan_in = in_channel * kernel_size * kernel_size
scale = 1.0
@ -37,30 +44,33 @@ def _weight_variable(shape, factor=0.01):
def _conv3x3(in_channel, out_channel, stride=1):
weight_shape = (out_channel, 3, 3, in_channel)
weight_shape = _trans_shape(weight_shape, format_)
weight = _weight_variable(weight_shape)
return nn.Conv2d(in_channel, out_channel, kernel_size=3, stride=stride,
padding=1, pad_mode='pad', weight_init=weight, data_format="NHWC")
padding=1, pad_mode='pad', weight_init=weight, data_format=format_)
def _conv1x1(in_channel, out_channel, stride=1):
weight_shape = (out_channel, 1, 1, in_channel)
weight_shape = _trans_shape(weight_shape, format_)
weight = _weight_variable(weight_shape)
return nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=stride,
padding=0, pad_mode='pad', weight_init=weight, data_format="NHWC")
padding=0, pad_mode='pad', weight_init=weight, data_format=format_)
def _conv7x7(in_channel, out_channel, stride=1):
weight_shape = (out_channel, 7, 7, in_channel)
weight_shape = _trans_shape(weight_shape, format_)
weight = _weight_variable(weight_shape)
return nn.Conv2d(in_channel, out_channel, kernel_size=7, stride=stride,
padding=3, pad_mode='pad', weight_init=weight, data_format="NHWC")
padding=3, pad_mode='pad', weight_init=weight, data_format=format_)
def _bn(channel):
return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9, gamma_init=1, beta_init=0,
moving_mean_init=0, moving_var_init=1, data_format="NHWC")
moving_mean_init=0, moving_var_init=1, data_format=format_)
def _bn_last(channel):
return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9, gamma_init=0, beta_init=0,
moving_mean_init=0, moving_var_init=1, data_format="NHWC")
moving_mean_init=0, moving_var_init=1, data_format=format_)
def _fc(in_channel, out_channel):
weight_shape = (out_channel, in_channel)
@ -165,10 +175,13 @@ class ResNet(nn.Cell):
if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!")
self.conv1 = _conv7x7(4, 64, stride=2)
input_data_channel = 4
if format_ == "NCHW":
input_data_channel = 3
self.conv1 = _conv7x7(input_data_channel, 64, stride=2)
self.bn1 = _bn(64)
self.relu = P.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same", data_format="NHWC")
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same", data_format=format_)
self.layer1 = self._make_layer(block,
layer_nums[0],
in_channel=in_channels[0],
@ -190,7 +203,7 @@ class ResNet(nn.Cell):
out_channel=out_channels[3],
stride=strides[3])
self.avg_pool = P.AvgPool(7, 1, data_format="NHWC")
self.avg_pool = P.AvgPool(7, 1, data_format=format_)
self.flatten = nn.Flatten()
self.end_point = _fc(out_channels[3], num_classes)
@ -237,7 +250,7 @@ class ResNet(nn.Cell):
return out
def resnet50(class_num=1001):
def resnet50(class_num=1001, dtype="fp16"):
"""
Get ResNet50 neural network.
@ -250,6 +263,9 @@ def resnet50(class_num=1001):
Examples:
>>> net = resnet50(1001)
"""
global format_
if dtype == "fp32":
format_ = "NCHW"
return ResNet(ResidualBlock,
[3, 4, 6, 3],
[64, 256, 512, 1024],

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