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import torch
from functorch.compile import memory_efficient_pointwise_fusion
import benchmark_helper
# ALL comments regarding the patetrns
def bias_gelu_dropout(input, bias):
a = torch.add(input, bias)
b = torch.nn.functional.gelu(a)
c = torch.nn.functional.dropout(b, p=0.6, training=True)
return c
def aot_fn(input, bias):
a = torch.add(input, bias)
b = a * 0.5 * (1.0 + torch.tanh(0.79788456 * a * (1 + 0.044715 * a * a)))
c = torch.nn.functional.dropout(b, p=0.6, training=True)
return c
fn = bias_gelu_dropout
# Set inputs
device = "cuda"
dtype = torch.float16
batch_size = 32
seq_len = 196
intermediate_size = 4096
# batch_size = 2
# seq_len = 4
# intermediate_size = 3
input = torch.randn(
batch_size,
seq_len,
intermediate_size,
requires_grad=True,
device=device,
dtype=dtype,
)
bias = torch.randn(intermediate_size, requires_grad=True, device=device, dtype=dtype)
# Get the optimized function
opt_fn = memory_efficient_pointwise_fusion(
aot_fn, compiler_name="torchscript_nvfuser"
)
# Profile cuda kernels
benchmark_helper.profile_cuda_kernels(fn, (input, bias), "Eager")
with torch.jit.fuser("fuser2"):
benchmark_helper.profile_cuda_kernels(opt_fn, (input, bias), "AOTAutograd")
# Time it with Torch Timer
benchmark_helper.time_with_torch_timer(fn, (input, bias), "Eager")
with torch.jit.fuser("fuser2"):
benchmark_helper.time_with_torch_timer(opt_fn, (input, bias), "AOTAutograd")
# Time it with manual Timer
benchmark_helper.time_with_manual_timer(fn, (input, bias), "Eager")
with torch.jit.fuser("fuser2"):
benchmark_helper.time_with_manual_timer(opt_fn, (input, bias), "AOTAutograd")
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