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Unverified Commit 7863e436 authored by Yineng Zhang's avatar Yineng Zhang Committed by GitHub
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add configs for block fp8 related kernels (#2628) 


Co-authored-by: default avatarHandH1998 <1335248067@qq.com>
parent 333e3bfd
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Showing with 2726 additions and 34 deletions
benchmark/kernels/fused_moe_triton/tuning_fused_moe_triton.py
View file @ 7863e436
...@@ -39,6 +39,7 @@ def benchmark_config( ...@@ -39,6 +39,7 @@ def benchmark_config(
dtype: torch.dtype, dtype: torch.dtype,
use_fp8_w8a8: bool, use_fp8_w8a8: bool,
use_int8_w8a16: bool, use_int8_w8a16: bool,
block_shape: List[int] = None,
num_iters: int = 100, num_iters: int = 100,
) -> float: ) -> float:
init_dtype = torch.float16 if use_fp8_w8a8 else dtype init_dtype = torch.float16 if use_fp8_w8a8 else dtype
...@@ -83,10 +84,23 @@ def benchmark_config( ...@@ -83,10 +84,23 @@ def benchmark_config(
) )
w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32) w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
if use_fp8_w8a8: if use_fp8_w8a8:
w1_scale = torch.randn(num_experts, dtype=torch.float32) if block_shape is None:
w2_scale = torch.randn(num_experts, dtype=torch.float32) w1_scale = torch.randn(num_experts, dtype=torch.float32)
a1_scale = torch.randn(1, dtype=torch.float32) w2_scale = torch.randn(num_experts, dtype=torch.float32)
a2_scale = torch.randn(1, dtype=torch.float32) a1_scale = torch.randn(1, dtype=torch.float32)
a2_scale = torch.randn(1, dtype=torch.float32)
else:
block_n, block_k = block_shape[0], block_shape[1]
n_tiles_w1 = (shard_intermediate_size + block_n - 1) // block_n
n_tiles_w2 = (hidden_size + block_n - 1) // block_n
k_tiles_w1 = (hidden_size + block_k - 1) // block_k
k_tiles_w2 = (shard_intermediate_size // 2 + block_k - 1) // block_k
w1_scale = torch.rand(
(num_experts, n_tiles_w1, k_tiles_w1), dtype=torch.float32
)
w2_scale = torch.rand(
(num_experts, n_tiles_w2, k_tiles_w2), dtype=torch.float32
)
w1 = w1.to(torch.float8_e4m3fn) w1 = w1.to(torch.float8_e4m3fn)
w2 = w2.to(torch.float8_e4m3fn) w2 = w2.to(torch.float8_e4m3fn)
...@@ -114,6 +128,7 @@ def benchmark_config( ...@@ -114,6 +128,7 @@ def benchmark_config(
w2_scale=w2_scale, w2_scale=w2_scale,
a1_scale=a1_scale, a1_scale=a1_scale,
a2_scale=a2_scale, a2_scale=a2_scale,
block_shape=block_shape,
) )
# JIT compilation & warmup # JIT compilation & warmup
...@@ -192,6 +207,7 @@ class BenchmarkWorker: ...@@ -192,6 +207,7 @@ class BenchmarkWorker:
dtype: torch.dtype, dtype: torch.dtype,
use_fp8_w8a8: bool, use_fp8_w8a8: bool,
use_int8_w8a16: bool, use_int8_w8a16: bool,
block_shape: List[int],
) -> Tuple[Dict[str, int], float]: ) -> Tuple[Dict[str, int], float]:
torch.cuda.manual_seed_all(0) torch.cuda.manual_seed_all(0)
dtype_str = get_config_dtype_str( dtype_str = get_config_dtype_str(
...@@ -199,8 +215,10 @@ class BenchmarkWorker: ...@@ -199,8 +215,10 @@ class BenchmarkWorker:
) )
# NOTE(woosuk): The current naming convention uses w2.shape[2], which # NOTE(woosuk): The current naming convention uses w2.shape[2], which
# is the intermediate size after silu_and_mul. # is the intermediate size after silu_and_mul.
block_n = block_shape[0] if block_shape else 0
block_k = block_shape[1] if block_shape else 0
op_config = get_moe_configs( op_config = get_moe_configs(
num_experts, shard_intermediate_size // 2, dtype_str num_experts, shard_intermediate_size // 2, dtype_str, block_n, block_k
) )
if op_config is None: if op_config is None:
config = get_default_config( config = get_default_config(
...@@ -223,6 +241,7 @@ class BenchmarkWorker: ...@@ -223,6 +241,7 @@ class BenchmarkWorker:
dtype, dtype,
use_fp8_w8a8, use_fp8_w8a8,
use_int8_w8a16, use_int8_w8a16,
block_shape,
) )
return config, kernel_time return config, kernel_time
...@@ -236,6 +255,7 @@ class BenchmarkWorker: ...@@ -236,6 +255,7 @@ class BenchmarkWorker:
dtype: torch.dtype, dtype: torch.dtype,
use_fp8_w8a8: bool, use_fp8_w8a8: bool,
use_int8_w8a16: bool, use_int8_w8a16: bool,
block_shape: List[int],
search_space: List[Dict[str, int]], search_space: List[Dict[str, int]],
) -> Dict[str, int]: ) -> Dict[str, int]:
best_config = None best_config = None
...@@ -252,6 +272,7 @@ class BenchmarkWorker: ...@@ -252,6 +272,7 @@ class BenchmarkWorker:
dtype, dtype,
use_fp8_w8a8, use_fp8_w8a8,
use_int8_w8a16, use_int8_w8a16,
block_shape,
num_iters=10, num_iters=10,
) )
except triton.runtime.autotuner.OutOfResources: except triton.runtime.autotuner.OutOfResources:
...@@ -287,6 +308,7 @@ def save_configs( ...@@ -287,6 +308,7 @@ def save_configs(
dtype: torch.dtype, dtype: torch.dtype,
use_fp8_w8a8: bool, use_fp8_w8a8: bool,
use_int8_w8a16: bool, use_int8_w8a16: bool,
block_shape: List[int],
) -> None: ) -> None:
dtype_str = get_config_dtype_str( dtype_str = get_config_dtype_str(
dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8 dtype, use_int8_w8a16=use_int8_w8a16, use_fp8_w8a8=use_fp8_w8a8
...@@ -295,7 +317,10 @@ def save_configs( ...@@ -295,7 +317,10 @@ def save_configs(
# NOTE(woosuk): The current naming convention uses w2.shape[2], which # NOTE(woosuk): The current naming convention uses w2.shape[2], which
# is the intermediate size after silu_and_mul. # is the intermediate size after silu_and_mul.
filename = get_config_file_name( filename = get_config_file_name(
num_experts, shard_intermediate_size // 2, dtype_str num_experts,
shard_intermediate_size // 2,
dtype_str,
block_shape,
) )
print(f"Writing best config to {filename}...") print(f"Writing best config to {filename}...")
...@@ -323,10 +348,10 @@ def main(args: argparse.Namespace): ...@@ -323,10 +348,10 @@ def main(args: argparse.Namespace):
topk = config.num_experts_per_tok topk = config.num_experts_per_tok
intermediate_size = config.moe_intermediate_size intermediate_size = config.moe_intermediate_size
shard_intermediate_size = 2 * intermediate_size // args.tp_size shard_intermediate_size = 2 * intermediate_size // args.tp_size
elif config.architectures[0] == "DeepseekV2ForCausalLM": elif config.architectures[0] in ["DeepseekV2ForCausalLM", "DeepseekV3ForCausalLM"]:
E = config.n_routed_experts E = config.n_routed_experts
topk = config.num_experts_per_tok topk = config.num_experts_per_tok
intermediate_size = config.intermediate_size intermediate_size = config.moe_intermediate_size
shard_intermediate_size = 2 * intermediate_size // args.tp_size shard_intermediate_size = 2 * intermediate_size // args.tp_size
else: else:
# Default: Mixtral # Default: Mixtral
...@@ -339,6 +364,13 @@ def main(args: argparse.Namespace): ...@@ -339,6 +364,13 @@ def main(args: argparse.Namespace):
dtype = config.torch_dtype dtype = config.torch_dtype
use_fp8_w8a8 = args.dtype == "fp8_w8a8" use_fp8_w8a8 = args.dtype == "fp8_w8a8"
use_int8_w8a16 = args.dtype == "int8_w8a16" use_int8_w8a16 = args.dtype == "int8_w8a16"
block_shape = None
if (
hasattr(config, "quantization_config")
and "weight_block_size" in config.quantization_config
):
block_shape = config.quantization_config["weight_block_size"]
assert len(block_shape) == 2
if args.batch_size is None: if args.batch_size is None:
batch_sizes = [ batch_sizes = [
...@@ -381,6 +413,14 @@ def main(args: argparse.Namespace): ...@@ -381,6 +413,14 @@ def main(args: argparse.Namespace):
if args.tune: if args.tune:
search_space = get_configs_compute_bound() search_space = get_configs_compute_bound()
if block_shape is not None:
block_n, block_k = block_shape[0], block_shape[1]
search_space = [
config
for config in search_space
if block_n % config["BLOCK_SIZE_N"] == 0
and block_k % config["BLOCK_SIZE_K"] == 0
]
print(f"Start tuning over {len(search_space)} configurations...") print(f"Start tuning over {len(search_space)} configurations...")
start = time.time() start = time.time()
...@@ -396,6 +436,7 @@ def main(args: argparse.Namespace): ...@@ -396,6 +436,7 @@ def main(args: argparse.Namespace):
dtype, dtype,
use_fp8_w8a8, use_fp8_w8a8,
use_int8_w8a16, use_int8_w8a16,
block_shape,
search_space, search_space,
) )
for batch_size in batch_sizes for batch_size in batch_sizes
...@@ -413,6 +454,7 @@ def main(args: argparse.Namespace): ...@@ -413,6 +454,7 @@ def main(args: argparse.Namespace):
dtype, dtype,
use_fp8_w8a8, use_fp8_w8a8,
use_int8_w8a16, use_int8_w8a16,
block_shape,
) )
end = time.time() end = time.time()
print(f"Tuning took {end - start:.2f} seconds") print(f"Tuning took {end - start:.2f} seconds")
...@@ -429,6 +471,7 @@ def main(args: argparse.Namespace): ...@@ -429,6 +471,7 @@ def main(args: argparse.Namespace):
dtype, dtype,
use_fp8_w8a8, use_fp8_w8a8,
use_int8_w8a16, use_int8_w8a16,
block_shape,
) )
for batch_size in batch_sizes for batch_size in batch_sizes
], ],
......
python/sglang/srt/layers/moe/fused_moe_triton/configs/E=256,N=128,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"2": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
}
}
python/sglang/srt/layers/moe/fused_moe_triton/configs/E=256,N=256,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 3
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
}
}
python/sglang/srt/layers/moe/fused_moe_triton/fused_moe.py
View file @ 7863e436
...@@ -392,14 +392,25 @@ def invoke_fused_moe_kernel( ...@@ -392,14 +392,25 @@ def invoke_fused_moe_kernel(
) )
def get_config_file_name(E: int, N: int, dtype: Optional[str]) -> str: def get_config_file_name(
E: int, N: int, dtype: Optional[str], block_shape: Optional[int] = None
) -> str:
device_name = get_device_name().replace(" ", "_") device_name = get_device_name().replace(" ", "_")
dtype_selector = "" if not dtype else f",dtype={dtype}" dtype_selector = "" if not dtype else f",dtype={dtype}"
return f"E={E},N={N},device_name={device_name}{dtype_selector}.json" block_shape_selector = (
"" if not block_shape or not all(block_shape) else f",block_shape={block_shape}"
)
return f"E={E},N={N},device_name={device_name}{dtype_selector}{block_shape_selector}.json"
@functools.lru_cache @functools.lru_cache
def get_moe_configs(E: int, N: int, dtype: Optional[str]) -> Optional[Dict[int, Any]]: def get_moe_configs(
E: int,
N: int,
dtype: Optional[str],
block_n: Optional[int] = 0,
block_k: Optional[int] = 0,
) -> Optional[Dict[int, Any]]:
""" """
Return optimized configurations for the fused MoE kernel. Return optimized configurations for the fused MoE kernel.
...@@ -411,7 +422,7 @@ def get_moe_configs(E: int, N: int, dtype: Optional[str]) -> Optional[Dict[int, ...@@ -411,7 +422,7 @@ def get_moe_configs(E: int, N: int, dtype: Optional[str]) -> Optional[Dict[int,
# First look up if an optimized configuration is available in the configs # First look up if an optimized configuration is available in the configs
# directory # directory
json_file_name = get_config_file_name(E, N, dtype) json_file_name = get_config_file_name(E, N, dtype, [block_n, block_k])
config_file_path = os.path.join( config_file_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name os.path.dirname(os.path.realpath(__file__)), "configs", json_file_name
...@@ -442,25 +453,38 @@ def get_default_config( ...@@ -442,25 +453,38 @@ def get_default_config(
topk: int, topk: int,
dtype: Optional[str], dtype: Optional[str],
is_marlin: bool, is_marlin: bool,
block_shape: Optional[List[int]] = None,
) -> Dict[str, int]: ) -> Dict[str, int]:
if dtype == "fp8_w8a8": if dtype == "fp8_w8a8":
config = { if block_shape is None:
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4,
}
if M <= E:
config = { config = {
"BLOCK_SIZE_M": 64, "BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128, "BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128, "BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1, "GROUP_SIZE_M": 32,
"num_warps": 4, "num_warps": 8,
"num_stages": 4, "num_stages": 4,
} }
if M <= E:
config = {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4,
}
else:
# Block-wise quant: BLOCK_SIZE_N must be divisable by block_shape[0]
# BLOCK_SIZE_K must be divisable by block_shape[1]
config = {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": block_shape[0],
"BLOCK_SIZE_K": block_shape[1],
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 3,
}
else: else:
config = { config = {
"BLOCK_SIZE_M": 64, "BLOCK_SIZE_M": 64,
...@@ -496,7 +520,9 @@ def try_get_optimal_moe_config( ...@@ -496,7 +520,9 @@ def try_get_optimal_moe_config(
else: else:
# First try to load optimal config from the file # First try to load optimal config from the file
E, _, N = w2_shape E, _, N = w2_shape
configs = get_moe_configs(E, N, dtype) block_n = block_shape[0] if block_shape else 0
block_k = block_shape[1] if block_shape else 0
configs = get_moe_configs(E, N, dtype, block_n, block_k)
if configs: if configs:
# If an optimal configuration map has been found, look up the # If an optimal configuration map has been found, look up the
...@@ -504,14 +530,9 @@ def try_get_optimal_moe_config( ...@@ -504,14 +530,9 @@ def try_get_optimal_moe_config(
config = configs[min(configs.keys(), key=lambda x: abs(x - M))] config = configs[min(configs.keys(), key=lambda x: abs(x - M))]
else: else:
# Else use the default config # Else use the default config
config = get_default_config(M, E, N, w1_shape[2], top_k, dtype, is_marlin) config = get_default_config(
# TODO(HandH1998): Optimize the configs of block-wise quant. M, E, N, w1_shape[2], top_k, dtype, is_marlin, block_shape
# NOTE(HandH1998): For block-wise quant, )
# BLOCK_K must be divisable by block_shape[1]
# BLOCK_N and BLOCK_M has no requirements
if block_shape is not None:
config["BLOCK_SIZE_N"] = block_shape[0]
config["BLOCK_SIZE_K"] = block_shape[1]
return config return config
......
python/sglang/srt/layers/quantization/configs/N=1536,K=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
}
}
python/sglang/srt/layers/quantization/configs/N=1536,K=7168,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
}
}
python/sglang/srt/layers/quantization/configs/N=2048,K=512,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
}
}
python/sglang/srt/layers/quantization/configs/N=2048,K=512,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
}
}
python/sglang/srt/layers/quantization/configs/N=2304,K=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
}
}
python/sglang/srt/layers/quantization/configs/N=2304,K=7168,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
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},
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},
"3072": {
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}
}
python/sglang/srt/layers/quantization/configs/N=24576,K=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
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},
"4": {
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"8": {
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},
"16": {
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"24": {
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},
"32": {
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"48": {
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"64": {
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"96": {
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"128": {
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"256": {
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},
"512": {
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},
"3072": {
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},
"4096": {
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}
}
python/sglang/srt/layers/quantization/configs/N=24576,K=7168,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"2": {
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"4": {
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"8": {
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},
"16": {
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"24": {
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},
"32": {
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"48": {
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},
"64": {
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"96": {
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"256": {
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"512": {
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},
"3072": {
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}
python/sglang/srt/layers/quantization/configs/N=3072,K=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"2": {
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},
"4": {
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"GROUP_SIZE_M": 1,
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"8": {
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"GROUP_SIZE_M": 16,
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},
"16": {
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"24": {
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},
"32": {
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"48": {
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"64": {
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"96": {
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"128": {
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"256": {
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}
}
python/sglang/srt/layers/quantization/configs/N=3072,K=7168,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"2": {
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},
"4": {
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},
"8": {
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"GROUP_SIZE_M": 64,
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},
"16": {
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"24": {
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},
"32": {
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"48": {
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"64": {
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"96": {
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"128": {
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"256": {
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"512": {
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"1024": {
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},
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}
}
python/sglang/srt/layers/quantization/configs/N=32768,K=512,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"2": {
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},
"4": {
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"8": {
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"16": {
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},
"32": {
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"48": {
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"64": {
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"96": {
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"128": {
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"256": {
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"3072": {
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"4096": {
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}
python/sglang/srt/layers/quantization/configs/N=32768,K=512,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
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"8": {
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"16": {
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"48": {
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}
python/sglang/srt/layers/quantization/configs/N=36864,K=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
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"4": {
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"8": {
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"16": {
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"24": {
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},
"32": {
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"48": {
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"64": {
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"96": {
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"4096": {
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}
}
python/sglang/srt/layers/quantization/configs/N=36864,K=7168,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
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"8": {
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},
"16": {
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"24": {
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"GROUP_SIZE_M": 32,
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},
"32": {
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"48": {
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"64": {
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},
"96": {
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"128": {
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"GROUP_SIZE_M": 32,
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"256": {
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"512": {
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},
"1024": {
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},
"1536": {
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"2048": {
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"3072": {
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"4096": {
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}
python/sglang/srt/layers/quantization/configs/N=4096,K=512,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"2": {
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"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
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},
"4": {
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"GROUP_SIZE_M": 16,
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"8": {
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"GROUP_SIZE_M": 1,
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"16": {
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"24": {
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"32": {
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"48": {
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"64": {
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"96": {
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"128": {
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"256": {
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},
"512": {
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},
"1024": {
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},
"1536": {
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},
"2048": {
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"num_stages": 2
},
"3072": {
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"BLOCK_SIZE_N": 128,
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"GROUP_SIZE_M": 16,
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},
"4096": {
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"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
}
}
python/sglang/srt/layers/quantization/configs/N=4096,K=512,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128, 128].json 0 → 100644
View file @ 7863e436
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"2": {
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"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 64,
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"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
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"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
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"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
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"GROUP_SIZE_M": 64,
"num_warps": 4,
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},
"32": {
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"48": {
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"GROUP_SIZE_M": 1,
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},
"64": {
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"96": {
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},
"128": {
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"256": {
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"512": {
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},
"1024": {
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"num_stages": 2
},
"1536": {
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},
"2048": {
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},
"3072": {
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},
"4096": {
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"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
}
}
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