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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import os
import random
from typing import Tuple
import pytest
import torch
from xformers import _is_triton_available
from xformers.ops import fused_allgather_and_linear, fused_linear_and_reducescatter
from .multiprocessing_utils import launch_subprocesses
compute_capability = (0, 0)
if torch.cuda.is_available():
compute_capability = torch.cuda.get_device_capability("cuda")
cuda_sm70_only = pytest.mark.skipif(
compute_capability < (7, 0), reason="requires sm70+"
)
at_least_2_gpus = pytest.mark.skipif(
torch.cuda.device_count() < 2, reason="needs at least 2 GPUs"
)
# We care about correctness, not performance, hence let's "disable" the
# expensive autotuning by removing all configs except one (the first one).
if _is_triton_available():
from xformers.ops._triton.sequence_parallel_fused_kernels import (
_xformers_seqpar_matmul_kernel,
)
while len(_xformers_seqpar_matmul_kernel.configs) > 1:
_xformers_seqpar_matmul_kernel.configs.pop()
def compare_fused_and_non_fused_ops(
my_rank: int,
world_size: int,
subgroup: torch.distributed.ProcessGroup,
step: str,
dims: Tuple[int, ...],
dtype: torch.dtype,
triton: bool,
):
batch_dims = dims[:-2]
subbatch_dims = (batch_dims[0] // world_size,) + batch_dims[1:]
outer_dim = dims[-2]
inner_dim = dims[-1]
# To check for correctness we want to compare the outputs but the accuracy
# of matmuls, apparently, is not that great. We thus try to produce inputs
# for which no rounding at all will occur. We do this by using zero or one
# inputs, so their product will also be zero or one, and keep the reduction
# dimension small enough so that they fit in the mantissa without overflow.
max_exact_value = 2 * (1 / torch.finfo(dtype).eps)
# 0.25 is the ratio of expected ones and we aim at 2/3 of the safe range
assert outer_dim * 0.25 <= max_exact_value * 0.66
assert inner_dim * world_size * 0.25 <= max_exact_value * 0.66
if step == "all-gather":
inputs = torch.testing.make_tensor(
(world_size,) + subbatch_dims + (outer_dim,),
dtype=dtype,
device="cuda",
low=0,
high=1,
).round()
weight = torch.testing.make_tensor(
(inner_dim, outer_dim), dtype=dtype, device="cuda", low=0, high=1
).round()
# Non-fused reference code
output_reference = torch.matmul(inputs, weight.t()).flatten(0, 1)
# Faster fused mode
output_fused = fused_allgather_and_linear(
inputs[my_rank], weight, group=subgroup, _triton=triton
)
elif step == "reduce-scatter":
inputs = torch.testing.make_tensor(
(world_size,) + batch_dims + (inner_dim,),
dtype=dtype,
device="cuda",
low=0,
high=1,
).round()
weights = torch.testing.make_tensor(
(world_size, outer_dim, inner_dim),
dtype=dtype,
device="cuda",
low=0,
high=1,
).round()
# Non-fused reference code
staging = torch.empty(
(world_size,) + subbatch_dims + (outer_dim,), dtype=dtype, device="cuda"
)
for rank in range(world_size):
torch.matmul(
inputs[rank].tensor_split(world_size, dim=0)[my_rank],
weights[rank].t(),
out=staging[rank],
)
output_reference = torch.sum(staging, dim=0, dtype=dtype)
# Faster fused mode
output_fused = fused_linear_and_reducescatter(
inputs[my_rank], weights[my_rank], group=subgroup, _triton=triton
)
torch.testing.assert_close(output_reference, output_fused, atol=0, rtol=0)
def inner_sequence_parallel_fused(
seed: int,
kind: str,
step: str,
dims: Tuple[int, ...],
dtype: torch.dtype,
):
my_rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
subgroup = torch.distributed.new_group()
triton = True
if kind == "fallback":
os.environ["DISABLE_FUSED_SEQUENCE_PARALLEL"] = "1"
elif kind == "pytorch":
triton = False
torch.random.manual_seed(seed)
compare_fused_and_non_fused_ops(
my_rank=my_rank,
world_size=world_size,
subgroup=subgroup,
step=step,
dims=dims,
dtype=dtype,
triton=triton,
)
@cuda_sm70_only
@pytest.mark.parametrize(
"kind",
["singleton", pytest.param("fallback", marks=at_least_2_gpus), "pytorch", "triton"],
)
@pytest.mark.parametrize("step", ["all-gather", "reduce-scatter"])
@pytest.mark.parametrize(
"dims",
[
pytest.param((2, 2, 512, 512, 256), id="nice-shapes"),
pytest.param((2, 1023, 511, 257), id="ugly-shapes"),
],
)
@pytest.mark.parametrize(
"dtype",
[
pytest.param(torch.bfloat16, id="bf16"),
pytest.param(torch.float16, id="fp16"),
pytest.param(torch.float32, id="fp32"),
],
)
def test_sequence_parallel_fused(
kind: str,
step: str,
dims: Tuple[int, ...],
dtype: torch.dtype,
):
world_size = 1 if kind == "singleton" else 2
seed = random.getrandbits(32)
launch_subprocesses(
world_size,
inner_sequence_parallel_fused,
seed=seed,
kind=kind,
step=step,
dims=dims,
dtype=dtype,
)
def inner_sequence_parallel_fused_triton_handle_all_dtypes(
seed: int,
step: str,
dims: Tuple[int, ...],
):
my_rank = torch.distributed.get_rank()
world_size = torch.distributed.get_world_size()
subgroup = torch.distributed.new_group()
torch.random.manual_seed(seed)
for dtype in [torch.bfloat16, torch.float16, torch.float32]:
compare_fused_and_non_fused_ops(
my_rank=my_rank,
world_size=world_size,
subgroup=subgroup,
step=step,
dims=dims,
dtype=dtype,
triton=True,
)
@cuda_sm70_only
@pytest.mark.parametrize("step", ["all-gather", "reduce-scatter"])
@pytest.mark.parametrize(
"dims",
[
pytest.param((2, 2, 512, 512, 256), id="nice-shapes"),
pytest.param((2, 1023, 511, 257), id="ugly-shapes"),
],
)
def test_sequence_parallel_fused_triton_handle_all_dtypes(
step: str,
dims: Tuple[int, ...],
):
world_size = 2
seed = random.getrandbits(32)
launch_subprocesses(
world_size,
inner_sequence_parallel_fused_triton_handle_all_dtypes,
seed=seed,
step=step,
dims=dims,
)
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