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[OP] support deepgeem for sm103 #7073

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[OP] support deepgeem for sm103 #7073

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d8fdf9d
support deepgeem for sm103
BingooYang Mar 30, 2026
703c828
add assert
BingooYang Mar 30, 2026
5a2b811
modify code style
BingooYang Mar 30, 2026
a188b9a
add assert
BingooYang Mar 31, 2026
5d32f41
modify sm version condition
BingooYang Mar 31, 2026
786e08a
remove assert
BingooYang Apr 1, 2026
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5 changes: 3 additions & 2 deletions fastdeploy/model_executor/layers/quantization/block_wise_fp8.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,7 +67,7 @@ def __init__(self, weight_block_size: list = [-1, -1], is_checkpoint_bf16: bool
self.quant_round_type = 1
self.use_deep_gemm = bool(envs.FD_USE_DEEP_GEMM)
self.is_checkpoint_bf16 = is_checkpoint_bf16
self.deepgemm_scale_ue8m0 = True if get_sm_version() == 100 else False
self.deepgemm_scale_ue8m0 = True if get_sm_version() >= 100 else False

def name(self) -> str:
return "block_wise_fp8"
Expand Down Expand Up @@ -125,7 +125,8 @@ def deep_gemm_fp8_gemm_nt(
layer_output_size: int,
bias: paddle.Tensor = None,
):
if get_sm_version() == 100 and current_platform.is_cuda():
sm_version = get_sm_version()
if sm_version >= 100 and current_platform.is_cuda():
# disable_ue8m0_cast is default False for SM100
fp8_gemm_nt(
(x, x_scale_tensor),
Expand Down
4 changes: 2 additions & 2 deletions fastdeploy/model_executor/layers/quantization/fp8_utils.py
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Expand Up @@ -65,7 +65,7 @@ def load_deep_gemm():
"""

if current_platform.is_cuda():
if get_sm_version() == 100:
if get_sm_version() >= 100:
# SM100 should use PFCC DeepGemm
paddle.compat.enable_torch_proxy(scope={"deep_gemm"})
try:
Expand Down Expand Up @@ -245,7 +245,7 @@ def fused_stack_transpose_quant(expert_weight_list, use_ue8m0=False):
# Blackwell (SM100) GPUs require pow2_scale quantization.
# Guard with is_cuda() so non-CUDA environments do not call into
# paddle.device.cuda.* and cause a crash.
use_pow2_scale = current_platform.is_cuda() and get_sm_version() == 100
use_pow2_scale = current_platform.is_cuda() and get_sm_version() >= 100

w, scale = paddlefleet_ops.fuse_stack_transpose_fp8_quant(
expert_weight_list,
Expand Down