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from .event import Event | ||
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from . import cublas | ||
from . import cudnn |
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# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
from .ffi import cudnnDataType | ||
from .kernels import conv2d, conv2d_gemm |
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import numpy as np | ||
import torch | ||
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import hidet | ||
from hidet.cuda.cudnn import cudnnDataType | ||
from hidet.utils.benchmark import do_bench | ||
from hidet import ops | ||
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def benchmark_cudnn_conv2d(dtype, compute_type, n, c, h, w, k, p, q, r, s, padding, stride, dilations): | ||
# Uses ordinary cudnn.conv2d implemented with Graph-API | ||
tx = tw = ty = dtype | ||
pad_dim1, pad_dim2 = padding | ||
str_dim1, str_dim2 = stride | ||
dil_dim1, dil_dim2 = dilations | ||
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tensor_x = hidet.randn((n, c, h, w), device='cuda', dtype=tx) | ||
tensor_w = hidet.randn((k, c, r, s), device='cuda', dtype=tw) | ||
tensor_y = hidet.empty((n, k, p, q), device='cuda', dtype=ty) | ||
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latencies = do_bench( | ||
lambda: hidet.cuda.cudnn.conv2d( | ||
n, | ||
c, | ||
h, | ||
w, | ||
k, | ||
r, | ||
s, | ||
p, | ||
q, | ||
tensor_x, | ||
tensor_w, | ||
tensor_y, | ||
tx, | ||
tw, | ||
ty, | ||
compute_type, | ||
pad_dim1, | ||
pad_dim2, | ||
str_dim1, | ||
str_dim2, | ||
dil_dim1, | ||
dil_dim2, | ||
), | ||
warmup=10, | ||
rep=1, | ||
) | ||
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print( | ||
f"CuDNN Results for Configuration: dtype = {dtype}, input shape = {[n,c,h,w]}, " | ||
f"weight shape = {[k,c,r,s]}, padding = {padding}, stride = {stride}, dilations = {dilations}:" | ||
) | ||
print("Median Latency Is: " + str(latencies[1]) + " milliseconds") | ||
print("-------------------------------------------------") | ||
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def benchmark_cudnn_conv2d_gemm(dtype, compute_type, n, c, h, w, k, p, q, r, s, padding, stride, dilations): | ||
# Uses cudnn.conv2d_gemm implemented with Legacy-API | ||
tx = tw = ty = dtype | ||
pad_dim1, pad_dim2 = padding | ||
str_dim1, str_dim2 = stride | ||
dil_dim1, dil_dim2 = dilations | ||
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tensor_x = hidet.randn((n, c, h, w), device='cuda', dtype=tx) | ||
tensor_w = hidet.randn((k, c, r, s), device='cuda', dtype=tw) | ||
tensor_y = hidet.empty((n, k, p, q), device='cuda', dtype=ty) | ||
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latencies = do_bench( | ||
lambda: hidet.cuda.cudnn.conv2d_gemm( | ||
n, | ||
c, | ||
h, | ||
w, | ||
k, | ||
r, | ||
s, | ||
tensor_x, | ||
tensor_w, | ||
tensor_y, | ||
tx, | ||
tw, | ||
ty, | ||
compute_type, | ||
pad_dim1, | ||
pad_dim2, | ||
str_dim1, | ||
str_dim2, | ||
dil_dim1, | ||
dil_dim2, | ||
), | ||
warmup=10, | ||
rep=100, | ||
) | ||
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print( | ||
f"cudnn_gemm Results for Configuration: dtype = {dtype}, input shape = {[n,c,h,w]}, " | ||
f"weight shape = {[k,c,r,s]}, padding = {padding}, stride = {stride}, dilations = {dilations}:" | ||
) | ||
print("Median Latency Is: " + str(latencies[1]) + " milliseconds") | ||
print("-------------------------------------------------") | ||
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def benchmark_torch_conv2d(dtype, compute_type, n, c, h, w, k, p, q, r, s, padding, stride, dilations): | ||
# Native PyTorch Eager-mode Execution | ||
data = np.array(np.random.randn(n, c, h, w)).astype(dtype) | ||
weight = np.array(np.random.randn(k, c, r, s)).astype(dtype) | ||
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data_torch, weight_torch = torch.from_numpy(data), torch.from_numpy(weight) | ||
data_torch = data_torch.cuda() | ||
weight_torch = weight_torch.cuda() | ||
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latencies = do_bench( | ||
lambda: torch.nn.functional.conv2d( | ||
data_torch, weight_torch, bias=None, stride=stride, padding=padding, dilation=dilations, groups=1 | ||
), | ||
warmup=10, | ||
rep=100, | ||
) | ||
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print( | ||
f"PyTorch Results for Configuration: dtype = {dtype}, input shape = {[n,c,h,w]}, " | ||
f"weight shape = {[k,c,r,s]}, padding = {padding}, stride = {stride}, dilations = {dilations}:" | ||
) | ||
print("Median Latency Is: " + str(latencies[1]) + " milliseconds") | ||
print("-------------------------------------------------") | ||
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def benchmark_hidet_conv2d(dtype, compute_type, n, c, h, w, k, p, q, r, s, padding, stride, dilations): | ||
# Uses optimized Hidet Graph implementation | ||
tx = tw = dtype | ||
pad_dim1, pad_dim2 = padding | ||
str_dim1, str_dim2 = stride | ||
dil_dim1, dil_dim2 = dilations | ||
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hidet.option.search_space(2) | ||
tensor_x = hidet.symbol((n, c, h, w), device='cuda', dtype=tx) | ||
tensor_w = hidet.randn((k, c, r, s), device='cuda', dtype=tw) | ||
output = ops.conv2d( | ||
tensor_x, tensor_w, stride=(str_dim1, str_dim2), dilations=(dil_dim1, dil_dim2), padding=(pad_dim1, pad_dim2) | ||
) | ||
graph = hidet.trace_from(output, inputs=[tensor_x, tensor_w]) | ||
graph = hidet.graph.optimize(graph) | ||
graph = graph.cuda_graph() | ||
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latencies = do_bench(lambda: graph.run_async(), warmup=10, rep=100) | ||
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print( | ||
f"Optimized Hidet Results for Configuration: dtype = {dtype}, input shape = {[n,c,h,w]}, " | ||
f"weight shape = {[k,c,r,s]}, padding = {padding}, stride = {stride}, dilations = {dilations}:" | ||
) | ||
print("Median Latency Is: " + str(latencies[1]) + " milliseconds") | ||
print("-------------------------------------------------") | ||
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if __name__ == '__main__': | ||
sizes = [ | ||
# Group 1 | ||
[1, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]], | ||
[2, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]], | ||
[4, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]], | ||
[8, 3, 224, 224, 64, 112, 112, 7, 7, [3, 3], [2, 2], [1, 1]], | ||
# Group 2 | ||
[1, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]], | ||
[2, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]], | ||
[4, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]], | ||
[8, 64, 56, 56, 128, 56, 56, 1, 1, [0, 0], [1, 1], [1, 1]], | ||
] | ||
dtypes = [['float32', cudnnDataType.CUDNN_DATA_FLOAT], ['float16', cudnnDataType.CUDNN_DATA_HALF]] | ||
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for data_type in dtypes: | ||
for size in sizes: | ||
benchmark_cudnn_conv2d_gemm(*(data_type + size)) | ||
benchmark_torch_conv2d(*(data_type + size)) | ||
benchmark_hidet_conv2d(*(data_type + size)) |
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# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
import os | ||
import sys | ||
import glob | ||
from enum import IntEnum | ||
from ctypes import c_int32, c_void_p, c_char_p | ||
from hidet.ffi.ffi import get_func | ||
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from hidet.utils.py import initialize | ||
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class cudnnDataType(IntEnum): | ||
""" | ||
defined in cudnn_ops_infer_v8.h | ||
""" | ||
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CUDNN_DATA_FLOAT = 0 | ||
CUDNN_DATA_DOUBLE = 1 | ||
CUDNN_DATA_HALF = 2 | ||
CUDNN_DATA_INT8 = 3 | ||
CUDNN_DATA_INT32 = 4 | ||
CUDNN_DATA_INT8x4 = 5 | ||
CUDNN_DATA_UINT8 = 6 | ||
CUDNN_DATA_UINT8x4 = 7 | ||
CUDNN_DATA_INT8x32 = 8 | ||
CUDNN_DATA_BFLOAT16 = 9 | ||
CUDNN_DATA_INT64 = 10 | ||
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set_library_path = get_func(func_name='hidet_cudnn_set_library_path', arg_types=[c_char_p], restype=None) | ||
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conv2d = get_func( | ||
func_name='hidet_cudnn_conv2d', | ||
arg_types=[ | ||
c_int32, # n | ||
c_int32, # c | ||
c_int32, # h | ||
c_int32, # w | ||
c_int32, # k | ||
c_int32, # r | ||
c_int32, # s | ||
c_int32, # p | ||
c_int32, # q | ||
c_void_p, # ptr_x | ||
c_void_p, # ptr_w | ||
c_void_p, # ptr_y | ||
c_int32, # tx | ||
c_int32, # tw | ||
c_int32, # ty | ||
c_int32, # compute_type | ||
c_int32, # pad_dim1 | ||
c_int32, # pad_dim2 | ||
c_int32, # str_dim1 | ||
c_int32, # str_dim2 | ||
c_int32, # dil_dim1 | ||
c_int32, # dil_dim2 | ||
], | ||
restype=None, | ||
) | ||
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conv2d_gemm = get_func( | ||
func_name='hidet_cudnn_conv2d_gemm', | ||
arg_types=[ | ||
c_int32, # n | ||
c_int32, # c | ||
c_int32, # h | ||
c_int32, # w | ||
c_int32, # k | ||
c_int32, # r | ||
c_int32, # s | ||
c_void_p, # ptr_x | ||
c_void_p, # ptr_w | ||
c_void_p, # ptr_y | ||
c_int32, # tx | ||
c_int32, # tw | ||
c_int32, # ty | ||
c_int32, # compute_type | ||
c_int32, # pad_dim1 | ||
c_int32, # pad_dim2 | ||
c_int32, # str_dim1 | ||
c_int32, # str_dim2 | ||
c_int32, # dil_dim1 | ||
c_int32, # dil_dim2 | ||
], | ||
restype=None, | ||
) | ||
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@initialize() | ||
def set_cudnn_library_path(): | ||
# use nvidia-cuda-cudnn | ||
for path in sys.path: | ||
nvidia_path = os.path.join(path, 'nvidia') | ||
if not os.path.exists(nvidia_path): | ||
continue | ||
cudnn_path = glob.glob(os.path.join(nvidia_path, 'cudnn', 'lib', 'libcudnn.so.[0-9]*')) | ||
if cudnn_path: | ||
set_library_path(cudnn_path[0].encode('utf-8')) | ||
return |
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