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use default for torch.ops (#4835)

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This commit is contained in:
Yineng Zhang
2025-03-27 19:09:58 -07:00
committed by GitHub
parent 10a9ab7b07
commit 31dfff7da7
7 changed files with 51 additions and 47 deletions
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30
sgl-kernel/python/sgl_kernel/allreduce.py
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@@ -12,49 +12,49 @@ if torch.version.hip is not None:
rank: int,
full_nvlink: bool,
) -> int:
return torch.ops.sgl_kernel.init_custom_ar(
return torch.ops.sgl_kernel.init_custom_ar.default(
meta, rank_data, handles, offsets, rank, full_nvlink
)
def all_reduce_reg(fa: int, inp: torch.Tensor, out: torch.Tensor) -> None:
torch.ops.sgl_kernel.all_reduce_reg(fa, inp, out)
torch.ops.sgl_kernel.all_reduce_reg.default(fa, inp, out)
def all_reduce_unreg(
fa: int, inp: torch.Tensor, reg_buffer: torch.Tensor, out: torch.Tensor
) -> None:
torch.ops.sgl_kernel.all_reduce_unreg(fa, inp, reg_buffer, out)
torch.ops.sgl_kernel.all_reduce_unreg.default(fa, inp, reg_buffer, out)
def dispose(fa: int) -> None:
torch.ops.sgl_kernel.dispose(fa)
torch.ops.sgl_kernel.dispose.default(fa)
def meta_size() -> int:
return torch.ops.sgl_kernel.meta_size()
return torch.ops.sgl_kernel.meta_size.default()
def register_buffer(
fa: int, t: torch.Tensor, handles: List[str], offsets: List[int]
) -> None:
return torch.ops.sgl_kernel.register_buffer(fa, t, handles, offsets)
return torch.ops.sgl_kernel.register_buffer.default(fa, t, handles, offsets)
def get_graph_buffer_ipc_meta(fa: int) -> Tuple[torch.Tensor, List[int]]:
return torch.ops.sgl_kernel.get_graph_buffer_ipc_meta(fa)
return torch.ops.sgl_kernel.get_graph_buffer_ipc_meta.default(fa)
def register_graph_buffers(
fa: int, handles: List[str], offsets: List[List[int]]
) -> None:
torch.ops.sgl_kernel.register_graph_buffers(fa, handles, offsets)
torch.ops.sgl_kernel.register_graph_buffers.default(fa, handles, offsets)
def allocate_meta_buffer(size: int) -> torch.Tensor:
return torch.ops.sgl_kernel.allocate_meta_buffer(size)
return torch.ops.sgl_kernel.allocate_meta_buffer.default(size)
def get_meta_buffer_ipc_handle(inp: torch.Tensor) -> torch.Tensor:
return torch.ops.sgl_kernel.get_meta_buffer_ipc_handle(inp)
return torch.ops.sgl_kernel.get_meta_buffer_ipc_handle.default(inp)
else:
# TRTLLM custom allreduce
def init_custom_reduce(
rank_id, num_devices, rank_data, buffers, tmp_buffers, barrier_in, barrier_out
):
return torch.ops.sgl_kernel.init_custom_ar(
return torch.ops.sgl_kernel.init_custom_ar.default(
rank_id,
num_devices,
rank_data,
@@ -65,13 +65,13 @@ else:
)
def custom_dispose(fa):
torch.ops.sgl_kernel.dispose(fa)
torch.ops.sgl_kernel.dispose.default(fa)
def custom_reduce(fa, inp, out):
torch.ops.sgl_kernel.all_reduce(fa, inp, out)
torch.ops.sgl_kernel.all_reduce.default(fa, inp, out)
def get_graph_buffer_ipc_meta(fa):
return torch.ops.sgl_kernel.get_graph_buffer_ipc_meta(fa)
return torch.ops.sgl_kernel.get_graph_buffer_ipc_meta.default(fa)
def register_graph_buffers(fa, handles, offsets):
torch.ops.sgl_kernel.register_graph_buffers(fa, handles, offsets)
torch.ops.sgl_kernel.register_graph_buffers.default(fa, handles, offsets)

2
sgl-kernel/python/sgl_kernel/attention.py
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@@ -2,6 +2,6 @@ import torch
def lightning_attention_decode(q, k, v, past_kv, slope, output, new_kv):
torch.ops.sgl_kernel.lightning_attention_decode(
torch.ops.sgl_kernel.lightning_attention_decode.default(
q, k, v, past_kv, slope, output, new_kv
)

18
sgl-kernel/python/sgl_kernel/elementwise.py
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@@ -14,14 +14,14 @@ def rmsnorm(
) -> torch.Tensor:
if out is None:
out = torch.empty_like(input)
torch.ops.sgl_kernel.rmsnorm(out, input, weight, eps, get_cuda_stream())
torch.ops.sgl_kernel.rmsnorm.default(out, input, weight, eps, get_cuda_stream())
return out
def fused_add_rmsnorm(
input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6
) -> None:
torch.ops.sgl_kernel.fused_add_rmsnorm(input, residual, weight, eps)
torch.ops.sgl_kernel.fused_add_rmsnorm.default(input, residual, weight, eps)
def gemma_rmsnorm(
@@ -32,14 +32,16 @@ def gemma_rmsnorm(
) -> torch.Tensor:
if out is None:
out = torch.empty_like(input)
torch.ops.sgl_kernel.gemma_rmsnorm(out, input, weight, eps, get_cuda_stream())
torch.ops.sgl_kernel.gemma_rmsnorm.default(
out, input, weight, eps, get_cuda_stream()
)
return out
def gemma_fused_add_rmsnorm(
input: torch.Tensor, residual: torch.Tensor, weight: torch.Tensor, eps: float = 1e-6
) -> None:
torch.ops.sgl_kernel.gemma_fused_add_rmsnorm(
torch.ops.sgl_kernel.gemma_fused_add_rmsnorm.default(
input, residual, weight, eps, get_cuda_stream()
)
@@ -65,7 +67,7 @@ def silu_and_mul(input: torch.Tensor, out: torch.Tensor = None) -> torch.Tensor:
device=input.device,
dtype=input.dtype,
)
torch.ops.sgl_kernel.silu_and_mul(out, input, get_cuda_stream())
torch.ops.sgl_kernel.silu_and_mul.default(out, input, get_cuda_stream())
return out
@@ -80,7 +82,7 @@ def gelu_tanh_and_mul(input: torch.Tensor, out: torch.Tensor = None) -> torch.Te
device=input.device,
dtype=input.dtype,
)
torch.ops.sgl_kernel.gelu_tanh_and_mul(out, input, get_cuda_stream())
torch.ops.sgl_kernel.gelu_tanh_and_mul.default(out, input, get_cuda_stream())
return out
@@ -95,7 +97,7 @@ def gelu_and_mul(input: torch.Tensor, out: torch.Tensor = None) -> torch.Tensor:
device=input.device,
dtype=input.dtype,
)
torch.ops.sgl_kernel.gelu_and_mul(out, input, get_cuda_stream())
torch.ops.sgl_kernel.gelu_and_mul.default(out, input, get_cuda_stream())
return out
@@ -139,7 +141,7 @@ def apply_rope_with_cos_sin_cache_inplace(
if cos_sin_cache.dtype != torch.float32:
raise ValueError("cos_sin_cache should be float32")
torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache(
torch.ops.sgl_kernel.apply_rope_pos_ids_cos_sin_cache.default(
q=query.view(query.shape[0], -1, head_size),
k=key.view(key.shape[0], -1, head_size),
q_rope=query.view(query.shape[0], -1, head_size),

26
sgl-kernel/python/sgl_kernel/gemm.py
View File

@@ -7,11 +7,11 @@ from sgl_kernel.utils import _get_cache_buf, get_cuda_stream
def awq_dequantize(
qweight: torch.Tensor, scales: torch.Tensor, qzeros: torch.Tensor
) -> torch.ByteTensor:
return torch.ops.sgl_kernel.awq_dequantize(qweight, scales, qzeros)
return torch.ops.sgl_kernel.awq_dequantize.default(qweight, scales, qzeros)
def int8_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype, bias=None):
return torch.ops.sgl_kernel.int8_scaled_mm(
return torch.ops.sgl_kernel.int8_scaled_mm.default(
mat_a,
mat_b,
scales_a,
@@ -22,7 +22,7 @@ def int8_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype, bias=None):
def fp8_blockwise_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype):
return torch.ops.sgl_kernel.fp8_blockwise_scaled_mm(
return torch.ops.sgl_kernel.fp8_blockwise_scaled_mm.default(
mat_a,
mat_b,
scales_a,
@@ -32,7 +32,7 @@ def fp8_blockwise_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype):
def fp8_scaled_mm(mat_a, mat_b, scales_a, scales_b, out_dtype, bias=None):
return torch.ops.sgl_kernel.fp8_scaled_mm(
return torch.ops.sgl_kernel.fp8_scaled_mm.default(
mat_a,
mat_b,
scales_a,
@@ -51,7 +51,7 @@ def _bmm_fp8_internal(
B_scale: torch.Tensor,
) -> None:
cublas_handle = torch.cuda.current_blas_handle()
torch.ops.sgl_kernel.bmm_fp8(
torch.ops.sgl_kernel.bmm_fp8.default(
A,
B,
D,
@@ -91,7 +91,7 @@ def sgl_per_token_group_quant_fp8(
fp8_min: float,
fp8_max: float,
) -> None:
torch.ops.sgl_kernel.sgl_per_token_group_quant_fp8(
torch.ops.sgl_kernel.sgl_per_token_group_quant_fp8.default(
input, output_q, output_s, group_size, eps, fp8_min, fp8_max
)
@@ -105,7 +105,7 @@ def sgl_per_token_group_quant_int8(
int8_min: float,
int8_max: float,
) -> None:
torch.ops.sgl_kernel.sgl_per_token_group_quant_int8(
torch.ops.sgl_kernel.sgl_per_token_group_quant_int8.default(
input, output_q, output_s, group_size, eps, int8_min, int8_max
)
@@ -116,7 +116,9 @@ def sgl_per_tensor_quant_fp8(
output_s: torch.Tensor,
is_static: bool,
) -> None:
torch.ops.sgl_kernel.sgl_per_tensor_quant_fp8(input, output_q, output_s, is_static)
torch.ops.sgl_kernel.sgl_per_tensor_quant_fp8.default(
input, output_q, output_s, is_static
)
def cublas_grouped_gemm(
@@ -129,7 +131,7 @@ def cublas_grouped_gemm(
len(inputs) > 0 and len(weights) > 0 and len(outputs) > 0
), "Inputs/weights/outputs should not be empty!"
cublas_handle = torch.cuda.current_blas_handle()
torch.ops.sgl_kernel.cublas_grouped_gemm(
torch.ops.sgl_kernel.cublas_grouped_gemm.default(
inputs,
weights,
outputs,
@@ -144,7 +146,7 @@ def sgl_per_token_quant_fp8(
output_q: torch.Tensor,
output_s: torch.Tensor,
) -> None:
torch.ops.sgl_kernel.sgl_per_token_quant_fp8(input, output_q, output_s)
torch.ops.sgl_kernel.sgl_per_token_quant_fp8.default(input, output_q, output_s)
def cutlass_scaled_fp4_mm(
@@ -158,7 +160,7 @@ def cutlass_scaled_fp4_mm(
assert a.ndim == 2 and b.ndim == 2
m, n = a.shape[0], b.shape[0]
out = torch.empty((m, n), dtype=out_dtype, device=a.device)
torch.ops.sgl_kernels.cutlass_scaled_fp4_mm(
torch.ops.sgl_kernel.cutlass_scaled_fp4_mm.default(
out, a, b, block_scale_a, block_scale_b, alpha
)
return out
@@ -210,7 +212,7 @@ def scaled_fp4_quant(
(rounded_m, rounded_n // 4), device=device, dtype=torch.int32
)
torch.ops.sgl_kernels.scaled_fp4_quant(
torch.ops.sgl_kernel.scaled_fp4_quant.default(
output, input, output_scale, input_global_scale
)
output_scale = output_scale.view(torch.float8_e4m3fn)

4
sgl-kernel/python/sgl_kernel/moe.py
View File

@@ -11,7 +11,7 @@ def moe_align_block_size(
token_cnts_buffer,
cumsum_buffer,
):
torch.ops.sgl_kernel.moe_align_block_size(
torch.ops.sgl_kernel.moe_align_block_size.default(
topk_ids,
num_experts,
block_size,
@@ -29,6 +29,6 @@ def topk_softmax(
token_expert_indices: torch.Tensor,
gating_output: float,
) -> None:
torch.ops.sgl_kernel.topk_softmax(
torch.ops.sgl_kernel.topk_softmax.default(
topk_weights, topk_ids, token_expert_indices, gating_output
)

10
sgl-kernel/python/sgl_kernel/sampling.py
View File

@@ -12,7 +12,7 @@ def _top_k_renorm_probs_internal(
probs = probs.float()
maybe_top_k_arr = maybe_top_k_arr.int() if maybe_top_k_arr is not None else None
renorm_probs = torch.empty_like(probs)
torch.ops.sgl_kernel.top_k_renorm_probs(
torch.ops.sgl_kernel.top_k_renorm_probs.default(
probs,
renorm_probs,
maybe_top_k_arr,
@@ -40,7 +40,7 @@ def _top_p_renorm_probs_internal(
probs = probs.float()
maybe_top_p_arr = maybe_top_p_arr.float() if maybe_top_p_arr is not None else None
renorm_probs = torch.empty_like(probs)
torch.ops.sgl_kernel.top_p_renorm_probs(
torch.ops.sgl_kernel.top_p_renorm_probs.default(
probs,
renorm_probs,
maybe_top_p_arr,
@@ -75,7 +75,7 @@ def _top_p_sampling_from_probs_internal(
)
samples = torch.empty(probs.size(0), dtype=torch.int32, device=device)
success = torch.empty(probs.size(0), dtype=torch.bool, device=device)
torch.ops.sgl_kernel.top_p_sampling_from_probs(
torch.ops.sgl_kernel.top_p_sampling_from_probs.default(
probs,
uniform_samples,
samples,
@@ -121,7 +121,7 @@ def _top_k_top_p_sampling_from_probs_internal(
)
samples = torch.empty(probs.size(0), dtype=torch.int32, device=device)
success = torch.empty(probs.size(0), dtype=torch.bool, device=device)
torch.ops.sgl_kernel.top_k_top_p_sampling_from_probs(
torch.ops.sgl_kernel.top_k_top_p_sampling_from_probs.default(
probs,
uniform_samples,
samples,
@@ -179,7 +179,7 @@ def _min_p_sampling_from_probs_internal(
maybe_min_p_arr.float() if maybe_min_p_arr is not None else None
)
samples = torch.empty(probs.size(0), dtype=torch.int32, device=device)
torch.ops.sgl_kernel.min_p_sampling_from_probs(
torch.ops.sgl_kernel.min_p_sampling_from_probs.default(
probs,
uniform_samples,
samples,

8
sgl-kernel/python/sgl_kernel/speculative.py
View File

@@ -17,7 +17,7 @@ def tree_speculative_sampling_target_only(
threshold_acc: float = 1.0,
deterministic: bool = True,
) -> None:
torch.ops.sgl_kernel.tree_speculative_sampling_target_only(
torch.ops.sgl_kernel.tree_speculative_sampling_target_only.default(
predicts,
accept_index,
accept_token_num,
@@ -45,7 +45,7 @@ def verify_tree_greedy(
retrive_next_sibling: torch.Tensor,
target_predict: torch.Tensor,
) -> None:
torch.ops.sgl_kernel.verify_tree_greedy(
torch.ops.sgl_kernel.verify_tree_greedy.default(
predicts,
accept_index,
accept_token_num,
@@ -71,7 +71,7 @@ def build_tree_kernel_efficient(
depth: int,
draft_token_num: int,
) -> None:
torch.ops.sgl_kernel.build_tree_kernel_efficient(
torch.ops.sgl_kernel.build_tree_kernel_efficient.default(
parent_list,
selected_index,
verified_seq_len,
@@ -92,7 +92,7 @@ def segment_packbits(
output_indptr: torch.Tensor,
y: torch.Tensor,
) -> None:
torch.ops.sgl_kernel.segment_packbits(
torch.ops.sgl_kernel.segment_packbits.default(
x,
input_indptr,
output_indptr,