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import torch
from functorch.compile import memory_efficient_fusion
import benchmark_helper
device = "cuda"
dtype = torch.float16
# LightSeq pattern 1
class DropoutResBias:
@staticmethod
def fn(input, bias, residual):
a = torch.add(input, bias)
b = torch.nn.functional.dropout(a, p=0.7, training=True)
c = b + residual
return c
@staticmethod
def args():
batch_size, seq_len, hidden_size = 32, 196, 1024
input = torch.randn(
batch_size,
seq_len,
hidden_size,
requires_grad=True,
device=device,
dtype=dtype,
)
bias = torch.randn(hidden_size, requires_grad=True, device=device, dtype=dtype)
residual = torch.randn(
batch_size,
seq_len,
hidden_size,
requires_grad=False,
device=device,
dtype=dtype,
)
args = (input, bias, residual)
return args
class DropoutResBiasScalar:
@staticmethod
def fn(input, bias, residual, p: float):
a = torch.add(input, bias)
b = torch.nn.functional.dropout(a, p, training=True)
c = b + residual
return c
@staticmethod
def args():
batch_size, seq_len, hidden_size = 32, 196, 1024
input = torch.randn(
batch_size,
seq_len,
hidden_size,
requires_grad=True,
device=device,
dtype=dtype,
)
bias = torch.randn(hidden_size, requires_grad=True, device=device, dtype=dtype)
residual = torch.randn(
batch_size,
seq_len,
hidden_size,
requires_grad=False,
device=device,
dtype=dtype,
)
args = (input, bias, residual, 0.7)
return args
# LightSeq pattern 2
class BiasReluDropout:
@staticmethod
def fn(input, bias):
a = torch.add(input, bias)
b = torch.nn.functional.relu(a)
c = torch.nn.functional.dropout(b, p=0.6, training=True)
return c
@staticmethod
def args():
batch_size = 32
seq_len = 196
intermediate_size = 4096
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
)
args = (input, bias)
return args
class BiasDropoutResLayerNorm:
@staticmethod
def fn(input, bias, residual):
hidden_size = 1024
a = torch.add(input, bias)
b = torch.nn.functional.dropout(a, p=0.7, training=True)
c = b + residual
d = torch.nn.functional.layer_norm(c, normalized_shape=(hidden_size,))
return d
@staticmethod
def args():
batch_size = 32
seq_len = 196
hidden_size = 1024
input = torch.randn(
batch_size,
seq_len,
hidden_size,
requires_grad=True,
device=device,
dtype=dtype,
)
bias = torch.randn(hidden_size, requires_grad=True, device=device, dtype=dtype)
residual = torch.randn(
batch_size,
seq_len,
hidden_size,
requires_grad=False,
device=device,
dtype=dtype,
)
args = (input, bias, residual)
return args
class LayerNormSigmoid:
@staticmethod
def fn(inp):
hidden_size = 512
a = torch.nn.functional.layer_norm(inp, normalized_shape=(hidden_size,))
b = torch.sigmoid(a)
return b
@staticmethod
def args():
batch_size = 8192
hidden_size = 512
inp = torch.randn(
batch_size, hidden_size, requires_grad=True, device=device, dtype=dtype
)
args = (inp,)
return args
for cl in [DropoutResBias, BiasReluDropout, DropoutResBiasScalar, BiasDropoutResLayerNorm, LayerNormSigmoid]:
# Clear the compile cache
# Get the function and inputs
obj = cl()
fn = obj.fn
args = obj.args()
# Find the static args
static_argnums = []
for idx, arg in enumerate(args):
if not isinstance(arg, torch.Tensor):
static_argnums.append(idx)
# Get the optimized function
opt_fn = memory_efficient_fusion(fn, static_argnums)
# Profile cuda kernels
benchmark_helper.profile_cuda_kernels(fn, args, "Eager")
with torch.jit.fuser("fuser2"):
benchmark_helper.profile_cuda_kernels(opt_fn, args, "AOTAutograd")
# Time it with Torch Timer
benchmark_helper.time_with_torch_timer(fn, args, "Eager")
with torch.jit.fuser("fuser2"):
benchmark_helper.time_with_torch_timer(opt_fn, args, "AOTAutograd")
# Time it with manual Timer
benchmark_helper.time_with_manual_timer(fn, args, "Eager")
with torch.jit.fuser("fuser2"):
benchmark_helper.time_with_manual_timer(opt_fn, args, "AOTAutograd")
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