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[Draft] Enable slicing of fp8 FusedSDPA for APC #2340

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[Draft] Enable slicing of fp8 FusedSDPA for APC #2340

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872782e
call fp8 fsdpa for the context and query twice
yangulei Nov 20, 2025
7323ad8
apply slicing for the fsdpa
yangulei Nov 20, 2025
688ae02
pass less attn_mask to get better perf.
yangulei Nov 21, 2025
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152 changes: 129 additions & 23 deletions neural_compressor/torch/algorithms/fp8_quant/_quant_common/helper_modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.

import os
import torch
import torch.nn as nn
import types
Expand Down Expand Up @@ -1296,6 +1297,10 @@ def __init__(self, mod, parent, mod_extra_config, *args, **kwargs):
self.register_scale("descale_amax", mod_extra_config.scale.inputs[3].type(torch.float32), self.scale_format)
self.register_scale("scale_output", 1 / mod_extra_config.scale.outputs[0].type(torch.float32), self.scale_format)
self.register_scale("scale_amax", 1 / self.descale_amax, self.scale_format)
self.qkv_slice_thld = int(os.getenv("VLLM_FUSEDSPA_QKV_SLICE_SEQ_LEN_THLD", 8192))
if self.qkv_slice_thld > 0:
self.q_chunk_size = int(os.getenv("VLLM_FUSEDSDPA_Q_SLICE_CHUNK_SIZE", self.qkv_slice_thld))
self.kv_chunk_size = int(os.getenv("VLLM_FUSEDSDPA_KV_SLICE_CHUNK_SIZE", self.qkv_slice_thld))

def forward_qdq(
self,
Expand Down Expand Up @@ -1330,6 +1335,41 @@ def forward_qdq(
seq_padding_type,
)
return results

def fp8_fsdpa_fwd(self,
q,
k,
v,
attn_mask,
dropout_p,
scale,
is_causal,
softmax_mode,
):
results = torch.ops.hpu.fp8_sdpa_recomp_fwd(
q,
k,
v,
attn_mask,
dropout_p,
scale,
is_causal,
True, # requires_backward
softmax_mode, # softmax_mode
self.scale_q, # d_scale_q
self.scale_k, # d_scale_k
self.scale_v, # d_scale_v
self.scale_amax, # q_scale_s
self.scale_output, # q_scale_o
self.descale_amax, # d_scale_s
False, # is_amax_s
False, # is_amax_o
None, # valid_seq_len
"right", # seq_padding_type
(-1, -1), # window_size
None, # sink
)
return results

def forward_quant(
self,
Expand All @@ -1345,32 +1385,98 @@ def forward_quant(
valid_seq_len=None,
seq_padding_type="None",
):
sm_mode = softmax_mode if softmax_mode == "fp32" else "None"
sm_mode = softmax_mode if softmax_mode == "fp32" else "none"
qinput = self.quant_q(q).detach()
kinput = self.quant_k(k).detach()
vinput = self.quant_v(v).detach()
results = self.fp8_fused_sdpa(
qinput,
kinput,
vinput,
attn_mask=attn_mask,
dropout_p=dropout_p,
is_causal=is_causal,
scale=scale,
softmax_mode=sm_mode,
d_scale_q=self.scale_q,
d_scale_k=self.scale_k,
d_scale_v=self.scale_v,
q_scale_s=self.scale_amax,
q_scale_o=self.scale_output,
d_scale_s=self.descale_amax,
is_amax_s=False,
valid_seq_len=valid_seq_len,
seq_padding_type=seq_padding_type,
)
output = results[0]
d_out = self.dequant_output(output)
return d_out
q_len = q.shape[-2]
kv_len = kinput.size(-2)

# for prefill with prefix caching
if q_len != 1 and q_len != kv_len \
and kv_len > self.qkv_slice_thld:
assert attn_mask is not None, "Attention mask is required for FSDPA with prefix caching."
ctx_len = kv_len - q_len
from habana_frameworks.torch.hpex.kernels.Fp8FusedSDPA import is_gqa, gqa_input_reshape_fwd, gqa_output_reshape
gqa = is_gqa(qinput, kinput)
if gqa:
qinput, kinput, vinput, attn_mask = gqa_input_reshape_fwd(qinput, kinput, vinput, attn_mask)

num_q_chunks = (q_len + self.q_chunk_size - 1) // self.q_chunk_size
num_kv_chunks = (kv_len + self.kv_chunk_size - 1) // self.kv_chunk_size
chunk_outputs = []
for q_chunk_idx in range(num_q_chunks):
q_start = q_chunk_idx * self.q_chunk_size
q_end = min((q_chunk_idx + 1) * self.q_chunk_size, q_len)
q_chunk = qinput[..., q_start:q_end, :]

last_out = None
last_m = None
last_linv = None
for kv_chunk_idx in range(num_kv_chunks):
kv_start = kv_chunk_idx * self.kv_chunk_size
kv_end = min((kv_chunk_idx + 1) * self.kv_chunk_size, kv_len)
k_chunk = kinput[..., kv_start:kv_end, :]
v_chunk = vinput[..., kv_start:kv_end, :]

# skip the upper triangular part for causal attention
if kv_start > ctx_len + q_end:
continue

is_causal= True if kv_start-ctx_len==0 else False

# current chunk_size should be multiple of 1024 to get right m/linv
if kv_end-ctx_len==0 and ((q_end-q_start)%1024!=0 or (kv_end-kv_start)%1024!=0):
is_causal = False
attn_mask_chunk = attn_mask[..., kv_start:kv_end]
else:
attn_mask_chunk = None

chunk_res = self.fp8_fsdpa_fwd(q_chunk, k_chunk, v_chunk, attn_mask_chunk, dropout_p, scale, is_causal, sm_mode)
chunk_out, chunk_m, chunk_linv = (gqa_output_reshape(x) for x in (chunk_res[:3])) if gqa else chunk_res[:3]

chunk_m = chunk_m.to(torch.float32)
chunk_linv = chunk_linv.to(torch.float32) * 128.0 if softmax_mode != "fp32" else chunk_linv.to(torch.float32)
chunk_out = self.dequant_output(chunk_out).to(torch.float32)

if kv_chunk_idx == 0:
last_out = chunk_out
last_m = chunk_m
last_linv = chunk_linv
else:
new_m = torch.maximum(last_m, chunk_m)
last_linv_rescaled = (1.0 / last_linv) * torch.exp(last_m - new_m)
chunk_linv_rescaled = (1.0 / chunk_linv) * torch.exp(chunk_m - new_m)
last_linv = 1.0 / (last_linv_rescaled + chunk_linv_rescaled)
last_out = (last_linv_rescaled * last_linv) * last_out + (
chunk_linv_rescaled * last_linv) * chunk_out
last_m = new_m
chunk_outputs.append(last_out)
output = torch.cat(chunk_outputs, dim=-2)
return output.to(q.dtype)
else:
results = self.fp8_fused_sdpa(
qinput,
kinput,
vinput,
attn_mask=attn_mask,
dropout_p=dropout_p,
is_causal=is_causal,
scale=scale,
softmax_mode=sm_mode,
d_scale_q=self.scale_q,
d_scale_k=self.scale_k,
d_scale_v=self.scale_v,
q_scale_s=self.scale_amax,
q_scale_o=self.scale_output,
d_scale_s=self.descale_amax,
is_amax_s=False,
valid_seq_len=valid_seq_len,
seq_padding_type=seq_padding_type,
)
output = results[0]
d_out = self.dequant_output(output)
return d_out

def forward_measure(
self,
Expand Down