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polish code with new pre-commit rule (#2923)

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Zero Rains
2025-07-19 23:19:27 +08:00
committed by GitHub
parent b8676d71a8
commit 25698d56d1
424 changed files with 14307 additions and 13518 deletions
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65
scripts/tune_cutlass_fp8_gemm.py
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@@ -11,7 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" UT for cutlass_fp8_fp8_half_gemm_fused """
"""UT for cutlass_fp8_fp8_half_gemm_fused"""
import paddle
from fastdeploy.utils import llm_logger as logger
@@ -26,14 +26,14 @@ def tune_cutlass_fp8_fp8_half_gemm_fused(
"""
Tune fp8 gemm.
"""
assert len(ns) == len(
ks), "The length of `ns` must be equal to that of `ks`"
assert len(ns) == len(ks), "The length of `ns` must be equal to that of `ks`"
try:
from fastdeploy.model_executor.ops.gpu import cutlass_fp8_fp8_half_gemm_fused
except ImportError:
logger.warning(
"From fastdeploy.model_executor.ops.gpu import cutlass_fp8_fp8_half_gemm_fused failed, \
fp8 is only support cuda arch 89+.")
fp8 is only support cuda arch 89+."
)
return
paddle.seed(2003)
for m in range(m_min, m_max + 32, 32):
@@ -42,10 +42,8 @@ def tune_cutlass_fp8_fp8_half_gemm_fused(
for idx in range(len(ns)):
n = ns[idx]
k = ks[idx]
A = paddle.rand(shape=[m, k],
dtype="bfloat16").astype("float8_e4m3fn")
B = paddle.rand(shape=[n, k],
dtype="bfloat16").astype("float8_e4m3fn")
A = paddle.rand(shape=[m, k], dtype="bfloat16").astype("float8_e4m3fn")
B = paddle.rand(shape=[n, k], dtype="bfloat16").astype("float8_e4m3fn")
cutlass_fp8_fp8_half_gemm_fused(
A,
B,
@@ -68,14 +66,16 @@ def tune_cutlass_fp8_fp8_fp8_dual_gemm_fused(
"""
Tune fp8 dual-gemm.
"""
assert len(ns) == len(
ks), "The length of `ns` must be equal to that of `ks`"
assert len(ns) == len(ks), "The length of `ns` must be equal to that of `ks`"
try:
from fastdeploy.model_executor.ops.gpu import cutlass_fp8_fp8_fp8_dual_gemm_fused
from fastdeploy.model_executor.ops.gpu import (
cutlass_fp8_fp8_fp8_dual_gemm_fused,
)
except ImportError:
logger.warning(
"From fastdeploy.model_executor.ops.gpu import cutlass_fp8_fp8_fp8_dual_gemm_fused failed, \
fp8 is only support cuda arch 89+.")
fp8 is only support cuda arch 89+."
)
return
paddle.seed(2003)
for m in range(m_min, m_max + 32, 32):
@@ -84,12 +84,9 @@ def tune_cutlass_fp8_fp8_fp8_dual_gemm_fused(
for idx in range(len(ns)):
n = ns[idx]
k = ks[idx]
A = paddle.rand(shape=[m, k],
dtype="bfloat16").astype("float8_e4m3fn")
B0 = paddle.rand(shape=[n, k],
dtype="bfloat16").astype("float8_e4m3fn")
B1 = paddle.rand(shape=[n, k],
dtype="bfloat16").astype("float8_e4m3fn")
A = paddle.rand(shape=[m, k], dtype="bfloat16").astype("float8_e4m3fn")
B0 = paddle.rand(shape=[n, k], dtype="bfloat16").astype("float8_e4m3fn")
B1 = paddle.rand(shape=[n, k], dtype="bfloat16").astype("float8_e4m3fn")
cutlass_fp8_fp8_fp8_dual_gemm_fused(
A,
B0,
@@ -115,14 +112,16 @@ def tune_per_channel_fp8_gemm_fused(
"""
Tune per-channel quant gemm.
"""
assert len(ns) == len(
ks), "The length of `ns` must be equal to that of `ks`"
assert len(ns) == len(ks), "The length of `ns` must be equal to that of `ks`"
try:
from fastdeploy.model_executor.ops.gpu import per_channel_fp8_fp8_half_gemm_fused
from fastdeploy.model_executor.ops.gpu import (
per_channel_fp8_fp8_half_gemm_fused,
)
except ImportError:
logger.warning(
"From fastdeploy.model_executor.ops.gpu import per_channel_fp8_fp8_half_gemm_fused failed, \
fp8 is only support cuda arch 89+.")
fp8 is only support cuda arch 89+."
)
return
paddle.seed(2003)
for m in range(m_min, m_max + 32, 32):
@@ -131,10 +130,8 @@ def tune_per_channel_fp8_gemm_fused(
for idx in range(len(ns)):
n = ns[idx]
k = ks[idx]
A = paddle.rand(shape=[m, k],
dtype="bfloat16").astype("float8_e4m3fn")
B = paddle.rand(shape=[n, k],
dtype="bfloat16").astype("float8_e4m3fn")
A = paddle.rand(shape=[m, k], dtype="bfloat16").astype("float8_e4m3fn")
B = paddle.rand(shape=[n, k], dtype="bfloat16").astype("float8_e4m3fn")
scalar_scale = paddle.full([1], 0.168, dtype="float32")
channel_scale = paddle.rand(shape=[n], dtype="float32")
@@ -160,14 +157,16 @@ def tune_blockwise_fp8_gemm_fused(
"""
Tune per-channel quant gemm.
"""
assert len(ns) == len(
ks), "The length of `ns` must be equal to that of `ks`"
assert len(ns) == len(ks), "The length of `ns` must be equal to that of `ks`"
try:
from fastdeploy.model_executor.ops.gpu import cutlass_fp8_fp8_half_block_gemm_fused
from fastdeploy.model_executor.ops.gpu import (
cutlass_fp8_fp8_half_block_gemm_fused,
)
except ImportError:
logger.warning(
"From fastdeploy.model_executor.ops.gpu import cutlass_fp8_fp8_half_block_gemm_fused failed, \
fp8 is only support cuda arch 90+.")
fp8 is only support cuda arch 90+."
)
return
paddle.seed(2003)
for m in range(m_min, m_max + 32, 32):
@@ -178,10 +177,8 @@ def tune_blockwise_fp8_gemm_fused(
k = ks[idx]
scale_n = (n + 128 - 1) // 128
scale_k = (k + 128 - 1) // 128
A = paddle.rand(shape=[m, k],
dtype="bfloat16").astype("float8_e4m3fn")
B = paddle.rand(shape=[n, k],
dtype="bfloat16").astype("float8_e4m3fn")
A = paddle.rand(shape=[m, k], dtype="bfloat16").astype("float8_e4m3fn")
B = paddle.rand(shape=[n, k], dtype="bfloat16").astype("float8_e4m3fn")
a_scale = paddle.randn([scale_k, m], dtype="float32")
b_scale = paddle.randn([scale_n, scale_k], dtype="float32")