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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.
from typing import Optional
import pytest
import torch
from xformers.ops import rope_padded
from xformers.ops.fmha.attn_bias import BlockDiagonalCausalWithOffsetPaddedKeysMask
from .utils import assert_allclose
compute_capability = (0, 0)
if torch.cuda.is_available():
compute_capability = torch.cuda.get_device_capability("cuda")
cuda_sm80_only = pytest.mark.skipif(
compute_capability < (8, 0), reason="requires sm80+"
)
def _slow_rope(
x: torch.Tensor,
*,
seqpos: Optional[torch.Tensor] = None,
theta=10000,
linear_scale=1,
adjacents: bool = True,
):
"""
Simple rope calculation of rope of one tensor
Args:
x: input, shape (B, M, H, K).
seqpos: gives the position of each sequence element in x in its sequence
(shape (M,)).
"""
x_shape = x.shape
dim = x_shape[-1]
seq_dim = 1
M = x_shape[seq_dim]
assert dim % 2 == 0
if seqpos is None:
seqpos = torch.arange(M, device=x.device)
power = torch.arange(0, dim, 2, device=x.device)[: (dim // 2)].float() / dim
freqs = 1.0 / (theta**power)
all_freqs = torch.outer(seqpos / linear_scale, freqs)
freqs_cis = torch.polar(torch.ones_like(all_freqs), all_freqs) # complex64
for _ in range(x.ndim - seq_dim - 2):
freqs_cis = freqs_cis[:, None]
if adjacents:
x_reshaped = x.float().unflatten(-1, (-1, 2))
x_ = torch.view_as_complex(x_reshaped)
x_out = torch.view_as_real(x_ * freqs_cis)
else:
x_reshaped = x.float().unflatten(-1, (2, -1)).transpose(-1, -2).contiguous()
x_ = torch.view_as_complex(x_reshaped)
x_out = torch.view_as_real(x_ * freqs_cis)
x_out = x_out.transpose(-1, -2)
return x_out.flatten(-2).type_as(x)
def _slow_rope2(
x: torch.Tensor,
*,
seqpos: Optional[torch.Tensor] = None,
theta=10000,
linear_scale=1,
adjacents: bool = True,
):
"""
More flexible unused version of _slow_rope
- allows varying dtypes.
"""
internal_dtype = torch.float64
dim = x.shape[-1]
seq_dim = 1
M = x.shape[seq_dim]
assert dim % 2 == 0
if seqpos is None:
seqpos = torch.arange(M, device=x.device)
power = (
torch.arange(0, dim, 2, device=x.device)[: (dim // 2)].to(internal_dtype) / dim
)
# freqs = 1.0 / (theta**power)
freqs = theta**-power
f = torch.outer(seqpos / linear_scale, freqs)
for _ in range(x.ndim - seq_dim - 2):
f = f[:, None]
if adjacents:
x1, x2 = x.to(internal_dtype).unflatten(-1, (-1, 2)).unbind(-1)
y1 = x1 * f.cos() - x2 * f.sin()
y2 = x1 * f.sin() + x2 * f.cos()
x_out = torch.stack([y1, y2], -1)
else:
x1, x2 = x.to(internal_dtype).unflatten(-1, (2, -1)).unbind(-2)
y1 = x1 * f.cos() - x2 * f.sin()
y2 = x1 * f.sin() + x2 * f.cos()
x_out = torch.stack([y1, y2], -2)
return x_out.flatten(-2).type_as(x)
DTYPES = {"bf16": torch.bfloat16, "f32": torch.float32}
ROPE_ATOL_RTOL = {
"bf16": (5e-3, 8e-3),
"f32": (5e-3, 1e-5),
}
@cuda_sm80_only
@pytest.mark.parametrize(
"adjacents", [True, False], ids=lambda x: "adj" if x else "non-adj"
)
@pytest.mark.parametrize("dtype_str", ["bf16", "f32"])
@pytest.mark.parametrize("internal_dtype", ["", "f32", "f64"])
@pytest.mark.parametrize("dim", [100, 4098])
@pytest.mark.parametrize("padding", [87, 18300])
@pytest.mark.parametrize("groups", [1, 3])
@pytest.mark.parametrize("linear_scale", [1.0, 4.0])
def test_consistency(
adjacents: bool,
dim: int,
padding: int,
groups: int,
internal_dtype: str,
dtype_str: str,
linear_scale: float,
):
torch.manual_seed(1)
heads, kvheads = 10, 2
nqueries = [2, 1, 1]
cache_lens = [27, padding - 5, padding // 2]
device = torch.device("cuda")
dtype = DTYPES[dtype_str]
# Can we make the internals of attn_bias be on the gpu.
attn_bias = BlockDiagonalCausalWithOffsetPaddedKeysMask.from_seqlens(
q_seqlen=nqueries, kv_padding=padding, kv_seqlen=cache_lens
)
total_cache_length = len(cache_lens) * padding
total_nqueries = sum(nqueries)
if groups == 1:
cache_k = torch.rand(
1, total_cache_length, kvheads, dim, device=device, dtype=dtype
)
cache_v = torch.rand(
1, total_cache_length, kvheads, dim, device=device, dtype=dtype
)
xq = torch.rand(1, total_nqueries, heads, dim, device=device, dtype=dtype)
xk = torch.rand(1, total_nqueries, kvheads, dim, device=device, dtype=dtype)
xv = torch.rand(1, total_nqueries, kvheads, dim, device=device, dtype=dtype)
else:
cache_k = torch.rand(
1, total_cache_length, groups, kvheads, dim, device=device, dtype=dtype
)
cache_v = torch.rand(
1, total_cache_length, groups, kvheads, dim, device=device, dtype=dtype
)
xq = torch.rand(
1, total_nqueries, groups, heads, dim, device=device, dtype=dtype
)
xk = torch.rand(
1, total_nqueries, groups, kvheads, dim, device=device, dtype=dtype
)
xv = torch.rand(
1, total_nqueries, groups, kvheads, dim, device=device, dtype=dtype
)
cache_k_orig = cache_k.clone()
cache_v_orig = cache_v.clone()
out = rope_padded(
xq,
xk,
xv,
cache_k,
cache_v,
attn_bias,
linear_scale=linear_scale,
adjacents=adjacents,
internal_dtype=internal_dtype,
)
seqpos = torch.tensor(
[cache_lens[0] - 2, cache_lens[0] - 1, cache_lens[1] - 1, cache_lens[2] - 1],
device=device,
)
cache_locs = [seqpos[0], seqpos[1], padding + seqpos[2], 2 * padding + seqpos[3]]
baseline = _slow_rope if dtype_str == "f32" else _slow_rope2
expected_out = baseline(
xq, linear_scale=linear_scale, seqpos=seqpos, adjacents=adjacents
)
atol, rtol = ROPE_ATOL_RTOL[dtype_str]
assert_allclose(out, expected_out, atol=atol, rtol=rtol)
assert_allclose(cache_v[:, cache_locs], xv, atol=atol, rtol=rtol)
cache_v[:, cache_locs] = cache_v_orig[:, cache_locs]
assert torch.allclose(cache_v, cache_v_orig)
slow_roped_xk = _slow_rope(
xk, linear_scale=linear_scale, seqpos=seqpos, adjacents=adjacents
)
assert_allclose(
cache_k[:, cache_locs],
slow_roped_xk,
atol=atol,
rtol=rtol,
)
cache_k[:, cache_locs] = cache_k_orig[:, cache_locs]
assert torch.allclose(cache_k, cache_k_orig)
@cuda_sm80_only
@pytest.mark.parametrize("seqlen", [512, 2**16])
def test_rope_prefill(seqlen) -> None:
heads, kvheads = 2, 1
dim = 32
device = "cuda"
adjacents = True
dtype = torch.bfloat16
attn_bias = BlockDiagonalCausalWithOffsetPaddedKeysMask.from_seqlens(
q_seqlen=[seqlen], kv_padding=seqlen + 1, kv_seqlen=[seqlen]
)
cache_k = torch.rand(1, seqlen + 1, kvheads, dim, device=device, dtype=dtype)
cache_v = torch.randn_like(cache_k)
xq = torch.rand(1, seqlen, heads, dim, device=device, dtype=dtype)
xk = torch.rand(1, seqlen, kvheads, dim, device=device, dtype=dtype)
xv = torch.rand(1, seqlen, kvheads, dim, device=device, dtype=dtype)
out = rope_padded(
xq,
xk,
xv,
cache_k,
cache_v,
attn_bias,
adjacents=adjacents,
)
seqpos = torch.arange(start=0, end=seqlen, device=device)
expected_out = _slow_rope2(xq, seqpos=seqpos, adjacents=adjacents)
atol, rtol = ROPE_ATOL_RTOL["bf16"]
assert_allclose(out, expected_out, atol=atol, rtol=rtol)
@cuda_sm80_only
def test_rope_seqpos() -> None:
heads, kvheads = 2, 1
dim = 32
device = "cuda"
adjacents = True
dtype = torch.bfloat16
seqlen = 723
attn_bias = BlockDiagonalCausalWithOffsetPaddedKeysMask.from_seqlens(
q_seqlen=[seqlen], kv_padding=seqlen + 1, kv_seqlen=[seqlen]
)
cache_k = torch.rand(1, seqlen + 1, kvheads, dim, device=device, dtype=dtype)
cache_v = torch.randn_like(cache_k)
xq = torch.rand(1, seqlen, heads, dim, device=device, dtype=dtype)
xk = torch.rand(1, seqlen, kvheads, dim, device=device, dtype=dtype)
xv = torch.rand(1, seqlen, kvheads, dim, device=device, dtype=dtype)
def inner(seqpos, *, first_seqpos_input=None, seqpos_input=None):
out = rope_padded(
xq,
xk,
xv,
cache_k,
cache_v,
attn_bias,
adjacents=adjacents,
first_seqpos=first_seqpos_input,
seqpos=seqpos_input,
)
expected_out = _slow_rope2(xq, seqpos=seqpos, adjacents=adjacents)
atol, rtol = ROPE_ATOL_RTOL["bf16"]
assert_allclose(out, expected_out, atol=atol, rtol=rtol)
inner(torch.arange(start=0, end=seqlen, device=device))
inner(
torch.arange(start=4, end=seqlen + 4, device=device),
first_seqpos_input=torch.tensor([4], device=device),
)
custom_seqpos = torch.arange(start=0, end=seqlen, device=device)
custom_seqpos[231] = 934
custom_seqpos[423] = 134
inner(custom_seqpos, seqpos_input=custom_seqpos)
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