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# Copyright (c) 2023, Tri Dao.
""" Useful functions for writing test code. """
import torch
import torch.utils.benchmark as benchmark
def benchmark_forward(
fn, *inputs, repeats=10, desc="", verbose=True, amp=False, amp_dtype=torch.float16, **kwinputs
):
"""Use Pytorch Benchmark on the forward pass of an arbitrary function."""
if verbose:
print(desc, "- Forward pass")
def amp_wrapper(*inputs, **kwinputs):
with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp):
fn(*inputs, **kwinputs)
t = benchmark.Timer(
stmt="fn_amp(*inputs, **kwinputs)",
globals={"fn_amp": amp_wrapper, "inputs": inputs, "kwinputs": kwinputs},
num_threads=torch.get_num_threads(),
)
m = t.timeit(repeats)
if verbose:
print(m)
return t, m
def benchmark_backward(
fn,
*inputs,
grad=None,
repeats=10,
desc="",
verbose=True,
amp=False,
amp_dtype=torch.float16,
**kwinputs,
):
"""Use Pytorch Benchmark on the backward pass of an arbitrary function."""
if verbose:
print(desc, "- Backward pass")
with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp):
y = fn(*inputs, **kwinputs)
if type(y) is tuple:
y = y[0]
if grad is None:
grad = torch.randn_like(y)
else:
if grad.shape != y.shape:
raise RuntimeError("Grad shape does not match output shape")
def f(*inputs, y, grad):
# Set .grad to None to avoid extra operation of gradient accumulation
for x in inputs:
if isinstance(x, torch.Tensor):
x.grad = None
y.backward(grad, retain_graph=True)
t = benchmark.Timer(
stmt="f(*inputs, y=y, grad=grad)",
globals={"f": f, "inputs": inputs, "y": y, "grad": grad},
num_threads=torch.get_num_threads(),
)
m = t.timeit(repeats)
if verbose:
print(m)
return t, m
def benchmark_combined(
fn,
*inputs,
grad=None,
repeats=10,
desc="",
verbose=True,
amp=False,
amp_dtype=torch.float16,
**kwinputs,
):
"""Use Pytorch Benchmark on the forward+backward pass of an arbitrary function."""
if verbose:
print(desc, "- Forward + Backward pass")
with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp):
y = fn(*inputs, **kwinputs)
if type(y) is tuple:
y = y[0]
if grad is None:
grad = torch.randn_like(y)
else:
if grad.shape != y.shape:
raise RuntimeError("Grad shape does not match output shape")
def f(grad, *inputs, **kwinputs):
for x in inputs:
if isinstance(x, torch.Tensor):
x.grad = None
with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp):
y = fn(*inputs, **kwinputs)
if type(y) is tuple:
y = y[0]
y.backward(grad, retain_graph=True)
t = benchmark.Timer(
stmt="f(grad, *inputs, **kwinputs)",
globals={"f": f, "fn": fn, "inputs": inputs, "grad": grad, "kwinputs": kwinputs},
num_threads=torch.get_num_threads(),
)
m = t.timeit(repeats)
if verbose:
print(m)
return t, m
def benchmark_fwd_bwd(
fn,
*inputs,
grad=None,
repeats=10,
desc="",
verbose=True,
amp=False,
amp_dtype=torch.float16,
**kwinputs,
):
"""Use Pytorch Benchmark on the forward+backward pass of an arbitrary function."""
return (
benchmark_forward(
fn,
*inputs,
repeats=repeats,
desc=desc,
verbose=verbose,
amp=amp,
amp_dtype=amp_dtype,
**kwinputs,
),
benchmark_backward(
fn,
*inputs,
grad=grad,
repeats=repeats,
desc=desc,
verbose=verbose,
amp=amp,
amp_dtype=amp_dtype,
**kwinputs,
),
)
def benchmark_all(
fn,
*inputs,
grad=None,
repeats=10,
desc="",
verbose=True,
amp=False,
amp_dtype=torch.float16,
**kwinputs,
):
"""Use Pytorch Benchmark on the forward+backward pass of an arbitrary function."""
return (
benchmark_forward(
fn,
*inputs,
repeats=repeats,
desc=desc,
verbose=verbose,
amp=amp,
amp_dtype=amp_dtype,
**kwinputs,
),
benchmark_backward(
fn,
*inputs,
grad=grad,
repeats=repeats,
desc=desc,
verbose=verbose,
amp=amp,
amp_dtype=amp_dtype,
**kwinputs,
),
benchmark_combined(
fn,
*inputs,
grad=grad,
repeats=repeats,
desc=desc,
verbose=verbose,
amp=amp,
amp_dtype=amp_dtype,
**kwinputs,
),
)
def pytorch_profiler(
fn,
*inputs,
trace_filename=None,
backward=False,
amp=False,
amp_dtype=torch.float16,
cpu=False,
verbose=True,
**kwinputs,
):
"""Wrap benchmark functions in Pytorch profiler to see CUDA information."""
if backward:
with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp):
out = fn(*inputs, **kwinputs)
if type(out) is tuple:
out = out[0]
g = torch.randn_like(out)
for _ in range(30): # Warm up
if backward:
for x in inputs:
if isinstance(x, torch.Tensor):
x.grad = None
with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp):
out = fn(*inputs, **kwinputs)
if type(out) is tuple:
out = out[0]
# Backward should be done outside autocast
if backward:
out.backward(g, retain_graph=True)
activities = ([torch.profiler.ProfilerActivity.CPU] if cpu else []) + [
torch.profiler.ProfilerActivity.CUDA
]
with torch.profiler.profile(
activities=activities,
record_shapes=True,
# profile_memory=True,
with_stack=True,
) as prof:
if backward:
for x in inputs:
if isinstance(x, torch.Tensor):
x.grad = None
with torch.autocast(device_type="cuda", dtype=amp_dtype, enabled=amp):
out = fn(*inputs, **kwinputs)
if type(out) is tuple:
out = out[0]
if backward:
out.backward(g, retain_graph=True)
if verbose:
# print(prof.key_averages().table(sort_by="self_cuda_time_total", row_limit=50))
print(prof.key_averages().table(row_limit=50))
if trace_filename is not None:
prof.export_chrome_trace(trace_filename)
def benchmark_memory(fn, *inputs, desc="", verbose=True, **kwinputs):
torch.cuda.empty_cache()
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
fn(*inputs, **kwinputs)
torch.cuda.synchronize()
mem = torch.cuda.max_memory_allocated() / ((2**20) * 1000)
if verbose:
print(f"{desc} max memory: {mem}GB")
torch.cuda.empty_cache()
return mem
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