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[W8A8 Block Linear Refactor][2/N] Remove W8A8Fp8BlockLinearOp and adopt Fp8 block linear kernel selections. (#33892)

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Signed-off-by: maral <maralbahari.98@gmail.com>
Signed-off-by: Maral <maralbahari.98@gmail.com>
This commit is contained in:
Maral
2026-04-09 08:50:39 +08:00
committed by GitHub
parent 8332078cfd
commit 2e9034c998
35 changed files with 1710 additions and 904 deletions
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19
benchmarks/kernels/benchmark_block_fp8_gemm.py
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@@ -9,11 +9,12 @@ os.environ["VLLM_USE_DEEP_GEMM"] = "0"
import torch
from vllm.benchmarks.lib.utils import default_vllm_config
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
W8A8BlockFp8LinearOp,
from vllm.model_executor.kernels.linear import (
init_fp8_linear_kernel,
)
from vllm.model_executor.layers.quantization.utils.quant_utils import (
GroupShape,
create_fp8_quant_key,
)
from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
CUTLASS_BLOCK_FP8_SUPPORTED,
@@ -70,11 +71,15 @@ def build_w8a8_block_fp8_runner(M, N, K, block_size, device, use_cutlass):
weight_group_shape = GroupShape(block_n, block_k)
act_quant_group_shape = GroupShape(1, block_k) # Per-token, per-group quantization
linear_op = W8A8BlockFp8LinearOp(
weight_group_shape=weight_group_shape,
act_quant_group_shape=act_quant_group_shape,
cutlass_block_fp8_supported=use_cutlass,
use_aiter_and_is_supported=False,
linear_op = init_fp8_linear_kernel(
weight_quant_key=create_fp8_quant_key(
static=True, group_shape=weight_group_shape
),
activation_quant_key=create_fp8_quant_key(
static=False, group_shape=act_quant_group_shape
),
out_dtype=torch.get_default_dtype(),
module_name="build_w8a8_block_fp8_runner",
)
def run():