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[CPU][FP8] Support FP8 SDPA for CPU backend #2689

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merged 5 commits into from
Sep 12, 2025
Merged

[CPU][FP8] Support FP8 SDPA for CPU backend #2689

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158ead2
[cpu][fp8] support fp8 sdpa for cpu
Valentine233 Aug 5, 2025
ca23d38
fix format
Valentine233 Aug 5, 2025
aa61c56
fix import
Valentine233 Aug 5, 2025
ce443b9
fix import
Valentine233 Aug 5, 2025
5cd1740
remove useless
Valentine233 Aug 25, 2025
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16 changes: 7 additions & 9 deletions ...ototype/inductor/test_int8_sdpa_fusion.py → test/prototype/inductor/test_qsdpa_fusion.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,8 @@
from torch.testing._internal.inductor_utils import HAS_CPU

import torchao
from torchao.prototype.inductor.fx_passes.int8_sdpa_fusion import (
_int8_sdpa_init,
from torchao.prototype.inductor.fx_passes.qsdpa_fusion import (
_qsdpa_init,
custom_pass,
)
from torchao.utils import torch_version_at_least
Expand Down Expand Up @@ -120,7 +120,7 @@ def _check_common(
)
source_code = "\n".join(source_code)
if has_fuse_pattern:
self.assertGreaterEqual(counters["inductor"]["int8_fuse_attention"], 1)
self.assertGreaterEqual(counters["inductor"]["qsdpa_fuse_attention"], 1)
if contains:
self.assertTrue(
any(
Expand Down Expand Up @@ -151,14 +151,14 @@ def _check_common(
@skipIfRocm
@unittest.skipIf(
not torch_version_at_least("2.7.0"),
reason="int8 sdpa requires torch 2.7 or later",
reason="qsdpa requires torch 2.7 or later",
)
@unittest.skipIf(
"CPU" not in torch._C._dispatch_dump("torchao::qscaled_dot_product"),
reason="cpp kernels not built",
)
@config.patch({"freezing": True})
def _test_sdpa_int8_rewriter(self):
def _test_qsdpa_rewriter(self):
import torchao.quantization.pt2e.quantizer.x86_inductor_quantizer as xiq
from torchao.quantization.pt2e.quantize_pt2e import convert_pt2e, prepare_pt2e
from torchao.quantization.pt2e.quantizer.x86_inductor_quantizer import (
Expand Down Expand Up @@ -193,7 +193,7 @@ def _test_sdpa_int8_rewriter(self):
),
config.patch(post_grad_custom_pre_pass=custom_pass),
):
_int8_sdpa_init()
_qsdpa_init()
quantizer = X86InductorQuantizer()
quantizer.set_global(xiq.get_default_x86_inductor_quantization_config())
quantizer.set_function_type_qconfig(
Expand All @@ -213,9 +213,7 @@ def _test_sdpa_int8_rewriter(self):

class SDPAPatternRewriterCpuTests(TestSDPAPatternRewriterTemplate):
device = "cpu"
test_sdpa_int8_rewriter_cpu = (
TestSDPAPatternRewriterTemplate._test_sdpa_int8_rewriter
)
test_qsdpa_rewriter_cpu = TestSDPAPatternRewriterTemplate._test_qsdpa_rewriter


if __name__ == "__main__":
Expand Down
203 changes: 140 additions & 63 deletions test/test_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -154,51 +154,101 @@ def _scaled_dot_product_int8_op_ref(
out = torch.clamp(torch.round(out / o_scale) + o_zp, min=0, max=255)
return out.to(torch.uint8)

def _scaled_dot_product_fp8_op_ref(
self,
q,
k,
v,
attn_mask=None,
dropout_p=0,
is_causal=False,
q_scale=1.0,
k_scale=1.0,
v_scale=1.0,
a_scale=1.0,
o_scale=1.0,
):
q = q.to(torch.float) * q_scale
k = k.to(torch.float) * k_scale
v = v.to(torch.float) * v_scale
scale_factor = 1 / math.sqrt(q.size(-1))
attn = q @ k.transpose(-2, -1)

attn = attn * scale_factor
if attn_mask is not None:
attn = attn + attn_mask.to(torch.float)
attn_max = attn.max(dim=-1, keepdim=True).values
attn = attn - attn_max
attn = torch.exp(attn)
attn_sum = torch.sum(attn, dim=-1, keepdim=True)
attn = attn / attn_sum
attn = torch.clamp(attn / a_scale, min=-448, max=448)
attn = attn.to(torch.float8_e4m3fn).to(torch.float)
attn = attn * a_scale
out = attn @ v
out = torch.clamp(out / o_scale, min=-448, max=448)
return out.to(torch.float8_e4m3fn)

@pytest.mark.skipif(
not torch_version_at_least("2.7.0"),
reason="int8 sdpa requires torch 2.7 or later",
reason="quantized sdpa requires torch 2.7 or later",
)
@pytest.mark.skipif(not IS_LINUX, reason="only support on linux")
@pytest.mark.skipif(
"CPU" not in torch._C._dispatch_dump("torchao::qscaled_dot_product"),
reason="cpp kernels not built",
)
@parametrize("input_dtype", [torch.uint8, torch.float8_e4m3fn])
@parametrize("batch_size", [56, 120])
@parametrize("n_head", [2, 16])
@parametrize("q_seq_len", [18, 89])
@parametrize("kv_seq_len", [100, 253])
@parametrize("head_dim", [32, 64])
@parametrize("mask_dtype", [None, torch.float32, torch.bfloat16])
def test_scaled_dot_product_int8_op(
self, batch_size, n_head, q_seq_len, kv_seq_len, head_dim, mask_dtype
def test_quantized_scaled_dot_product_op(
self,
input_dtype,
batch_size,
n_head,
q_seq_len,
kv_seq_len,
head_dim,
mask_dtype,
):
torch.manual_seed(1234)
device = "cpu"
q_scale = float(1.7907238006591797)
q_zp = int(127)
k_scale = float(1.8039721250534058)
k_zp = int(125)
v_scale = float(1.839004635810852)
v_zp = int(127)
a_scale = float(0.003919653594493866)
a_zp = int(120)
o_scale = float(1.8191684484481812)
o_zp = int(128)
if input_dtype == torch.uint8:
q_scale = float(1.7907238006591797)
k_scale = float(1.8039721250534058)
v_scale = float(1.839004635810852)
a_scale = float(0.003919653594493866)
o_scale = float(1.8191684484481812)
q_zp = int(127)
k_zp = int(125)
v_zp = int(127)
a_zp = int(120)
o_zp = int(128)
atol, rtol = 1.0, 5e-6
else:
q_scale = float(5.96875)
k_scale = float(5.78125)
v_scale = float(0.98046875)
a_scale = float(4.84375)
o_scale = float(3.171875)
atol, rtol = 0.125, 5e-6
q_shape = [batch_size, q_seq_len, n_head, head_dim]
kv_shape = [batch_size, kv_seq_len, n_head, head_dim]
mask_shape = [batch_size, 1, 1, kv_seq_len]
q = torch.randn(q_shape, dtype=torch.float, device=device).transpose(1, 2) * 100
k = (
torch.randn(kv_shape, dtype=torch.float, device=device).transpose(1, 2)
* 100
)
v = (
torch.randn(kv_shape, dtype=torch.float, device=device).transpose(1, 2)
* 100
)
q = q.to(torch.uint8)
k = k.to(torch.uint8)
v = v.to(torch.uint8)
q = torch.randn(q_shape, dtype=torch.float, device=device).transpose(1, 2)
k = torch.randn(kv_shape, dtype=torch.float, device=device).transpose(1, 2)
v = torch.randn(kv_shape, dtype=torch.float, device=device).transpose(1, 2)
if input_dtype == torch.uint8:
q *= 100
k *= 100
v *= 100
q = q.to(input_dtype)
k = k.to(input_dtype)
v = v.to(input_dtype)
attn_mask = (
torch.randn(mask_shape, dtype=mask_dtype, device=device)
if mask_dtype is not None
Expand All @@ -211,44 +261,71 @@ def test_scaled_dot_product_int8_op(
attn_mask.clone() if mask_dtype is not None else None,
)

math_ref = self._scaled_dot_product_int8_op_ref(
q2,
k2,
v2,
attn_mask=attn_mask,
dropout_p=0.0,
is_causal=False,
q_scale=q_scale,
q_zp=q_zp,
k_scale=k_scale,
k_zp=k_zp,
v_scale=v_scale,
v_zp=v_zp,
a_scale=a_scale,
a_zp=a_zp,
o_scale=o_scale,
o_zp=o_zp,
)
actual = torch.ops.torchao.qscaled_dot_product(
q,
k,
v,
attn_mask=attn_mask_2,
dropout_p=0.0,
is_causal=False,
q_scale=q_scale,
q_zp=q_zp,
k_scale=k_scale,
k_zp=k_zp,
v_scale=v_scale,
v_zp=v_zp,
a_scale=a_scale,
a_zp=a_zp,
o_scale=o_scale,
o_zp=o_zp,
)

self.assertEqual(actual, math_ref, atol=1.0, rtol=5e-6)
if input_dtype == torch.uint8:
math_ref = self._scaled_dot_product_int8_op_ref(
q2,
k2,
v2,
attn_mask=attn_mask,
dropout_p=0.0,
is_causal=False,
q_scale=q_scale,
q_zp=q_zp,
k_scale=k_scale,
k_zp=k_zp,
v_scale=v_scale,
v_zp=v_zp,
a_scale=a_scale,
a_zp=a_zp,
o_scale=o_scale,
o_zp=o_zp,
)
actual = torch.ops.torchao.qscaled_dot_product(
q,
k,
v,
attn_mask=attn_mask_2,
dropout_p=0.0,
is_causal=False,
q_scale=q_scale,
q_zp=q_zp,
k_scale=k_scale,
k_zp=k_zp,
v_scale=v_scale,
v_zp=v_zp,
a_scale=a_scale,
a_zp=a_zp,
o_scale=o_scale,
o_zp=o_zp,
)
else:
math_ref = self._scaled_dot_product_fp8_op_ref(
q2,
k2,
v2,
attn_mask=attn_mask,
dropout_p=0.0,
is_causal=False,
q_scale=q_scale,
k_scale=k_scale,
v_scale=v_scale,
a_scale=a_scale,
o_scale=o_scale,
)
actual = torch.ops.torchao.qscaled_dot_product(
q,
k,
v,
attn_mask=attn_mask_2,
dropout_p=0.0,
is_causal=False,
q_scale=q_scale,
k_scale=k_scale,
v_scale=v_scale,
a_scale=a_scale,
o_scale=o_scale,
)
self.assertEqual(actual.float(), math_ref.float(), atol=atol, rtol=rtol)


instantiate_parametrized_tests(TestOps)
Expand Down
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