feat(CRITICAL): 从 GitHub 扫描搬运 ixformer SDK + xllm 完整 GDN/MoE 代码
来源:
1. Chranos/ixformer (GitHub) → ixformer_sdk/ (230 files, 70K lines)
- inference/functions/vllm.py: vllm_moe_topk_softmax 完整实现 (2033 lines)
- inference/functions/moe.py: MoE ops 完整实现 (1380 lines)
- contrib/vllm_flash_attn/: FA2 Python 接口 (1018 lines)
- contrib/tgi/fused_moe.py: TGI fused MoE (429 lines)
- csrc/include/ixformer/: C++ kernel headers + cmake
2. Deep-Spark/xllm (GitHub) → upstream_ref/xllm_latest/ (+15 files)
- npu_torch/qwen3_5_decoder_layer_impl.cpp/.h
- npu_torch/qwen3_5_gated_delta_net.cpp/.h
- npu_torch/qwen3_next_*.cpp/.h (6 files)
- npu_torch/attention.cpp/.h + fused_moe.cpp/.h + CMakeLists.txt
- models/llm/qwen3_5.h + qwen3_5_mtp.h + qwen3_next.h
- models/vlm/qwen3_5.h
调用链完整性:
ixformer_sdk/inference/functions/vllm.py
→ ops.infer.moe_topk_softmax() (C++ 层)
→ 这就是 base 镜像 libixformer.so 里的实现
upstream_ref/xllm_latest/core/layers/ilu/fused_moe.cpp
→ ixformer::infer::topk_softmax() (直接 C++ 调用)
→ ixformer::infer::group_gemm() → 完整 7-step MoE pipeline
This commit is contained in:
project6-dev
199
ixformer_sdk/inference/functions/bert.py
Normal file
199
ixformer_sdk/inference/functions/bert.py
Normal file
@@ -0,0 +1,199 @@
|
||||
import ixformer._C as ops
|
||||
import torch
|
||||
|
||||
__all__ = [
|
||||
"ref_bert_embedding",
|
||||
"bert_embedding",
|
||||
"ref_bert_add_norm",
|
||||
"bert_add_norm",
|
||||
"ref_bert_unpack_start_end_logits",
|
||||
"bert_unpack_start_end_logits",
|
||||
"ref_bert_linear_residual",
|
||||
"bert_linear_residual",
|
||||
]
|
||||
|
||||
|
||||
def ref_bert_embedding(
|
||||
token_weight: torch.Tensor,
|
||||
pos_weight: torch.Tensor,
|
||||
type_weight: torch.Tensor,
|
||||
ln_weight: torch.Tensor,
|
||||
ln_bias: torch.Tensor,
|
||||
token_ids: torch.Tensor,
|
||||
pos_ids: torch.Tensor,
|
||||
type_ids: torch.Tensor,
|
||||
epsilon: float = 1e-5,
|
||||
out: torch.Tensor = None,
|
||||
):
|
||||
assert out is None
|
||||
emd1 = torch.nn.functional.embedding(token_ids, token_weight)
|
||||
emd2 = torch.nn.functional.embedding(pos_ids, pos_weight)
|
||||
emd3 = torch.nn.functional.embedding(type_ids, type_weight)
|
||||
|
||||
out = emd1 + emd2 + emd3
|
||||
out = torch.nn.functional.layer_norm(out, [out.shape[-1]], ln_weight, ln_bias)
|
||||
return out
|
||||
|
||||
|
||||
def bert_embedding(
|
||||
token_weight: torch.Tensor,
|
||||
pos_weight: torch.Tensor,
|
||||
type_weight: torch.Tensor,
|
||||
ln_weight: torch.Tensor,
|
||||
ln_bias: torch.Tensor,
|
||||
token_ids: torch.Tensor,
|
||||
pos_ids: torch.Tensor,
|
||||
type_ids: torch.Tensor,
|
||||
epsilon: float = 1e-5,
|
||||
out: torch.Tensor = None,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
token_weight: (vocab_size, hidden_size) torch.float16, torch.bfloat16
|
||||
pos_weight: (pos_size, hidden_size) same as token_weight
|
||||
type_weight: (type_size, hidden_size) same as token_weight
|
||||
ln_weight: (hidden_size) same as token_weight
|
||||
ln_bias: (hidden_size) same as token_weight
|
||||
token_ids: (num_tokens) torch.int32, torch.int64
|
||||
pos_ids: (num_tokens) same as token_ids
|
||||
type_ids: (num_tokens) same as token_ids
|
||||
epsilon: float
|
||||
out: (num_tokens, hidden_size) same as token_weight
|
||||
Returns:
|
||||
out: (num_tokens, hidden_size) same as token_weight
|
||||
"""
|
||||
if out is None:
|
||||
out_shape = list(token_ids.shape)
|
||||
hidden_size = token_weight.shape[-1]
|
||||
out_shape.append(hidden_size)
|
||||
out = token_weight.new_empty(out_shape)
|
||||
|
||||
ops.infer.bert_embedding(
|
||||
token_weight,
|
||||
pos_weight,
|
||||
type_weight,
|
||||
ln_weight,
|
||||
ln_bias,
|
||||
token_ids,
|
||||
pos_ids,
|
||||
type_ids,
|
||||
out,
|
||||
epsilon,
|
||||
)
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def ref_bert_add_norm(
|
||||
input: torch.Tensor,
|
||||
residual: torch.Tensor,
|
||||
ln_weight: torch.Tensor,
|
||||
ln_bias: torch.Tensor,
|
||||
epsilon: float = 1e-5,
|
||||
out: torch.Tensor = None,
|
||||
):
|
||||
assert out is None
|
||||
input = input + residual
|
||||
return torch.nn.functional.layer_norm(
|
||||
input, [input.shape[-1]], ln_weight, ln_bias, epsilon
|
||||
)
|
||||
|
||||
|
||||
def bert_add_norm(
|
||||
input: torch.Tensor,
|
||||
residual: torch.Tensor,
|
||||
ln_weight: torch.Tensor,
|
||||
ln_bias: torch.Tensor,
|
||||
epsilon: float = 1e-5,
|
||||
out: torch.Tensor = None,
|
||||
):
|
||||
"""
|
||||
out = input + residual
|
||||
out = add_norm(out, ln_weight, ln_bias, epsilon)
|
||||
Args:
|
||||
input: (num_tokens, hidden_size) torch.float16, torch.bfloat16
|
||||
residual: (num_tokens, hidden_size) same as input
|
||||
ln_weight: (hidden_size) same as input
|
||||
ln_bias: (hidden_size) same as input
|
||||
epsilon: float
|
||||
out: (num_tokens, hidden_size) same as input
|
||||
Returns:
|
||||
out: (num_tokens, hidden_size) same as input
|
||||
"""
|
||||
if out is None:
|
||||
out = torch.empty_like(input)
|
||||
ops.infer.bert_add_norm(input, residual, ln_weight, ln_bias, out, epsilon)
|
||||
return out
|
||||
|
||||
|
||||
def ref_bert_unpack_start_end_logits(
|
||||
logits: torch.Tensor,
|
||||
cu_seq_lens: torch.Tensor,
|
||||
max_seq_len: int,
|
||||
start_logits: torch.Tensor = None,
|
||||
end_logits: torch.Tensor = None,
|
||||
):
|
||||
batch_size = cu_seq_lens.shape[0] - 1
|
||||
if start_logits is None:
|
||||
start_logits = logits.new_empty([batch_size, max_seq_len])
|
||||
if end_logits is None:
|
||||
end_logits = logits.new_empty([batch_size, max_seq_len])
|
||||
cu_seq_len_cpu = cu_seq_lens.detach().cpu()
|
||||
for i in range(batch_size):
|
||||
start_idx = cu_seq_len_cpu[i]
|
||||
end_idx = cu_seq_len_cpu[i + 1]
|
||||
cur_len = end_idx - start_idx
|
||||
start_logits[i, :cur_len] = logits[start_idx:end_idx, 0]
|
||||
end_logits[i, :cur_len] = logits[start_idx:end_idx, 1]
|
||||
return start_logits, end_logits
|
||||
|
||||
|
||||
def bert_unpack_start_end_logits(
|
||||
logits: torch.Tensor,
|
||||
cu_seq_lens: torch.Tensor,
|
||||
max_seq_len: int,
|
||||
start_logits: torch.Tensor = None,
|
||||
end_logits: torch.Tensor = None,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
logits: (num_tokens, 2) torch.float16, torch.bfloat16
|
||||
cu_seq_lens: (batch_size+1) torch.int32, torch.int64
|
||||
max_seq_len: int
|
||||
start_logits: (batch_size, max_seq_len) same as logits
|
||||
end_logits: (batch_size, max_seq_len) same as logits
|
||||
Returns:
|
||||
start_logits: (batch_size, max_seq_len) same as logits
|
||||
end_logits: (batch_size, max_seq_len) same as logits
|
||||
"""
|
||||
batch_size = cu_seq_lens.shape[0] - 1
|
||||
if start_logits is None:
|
||||
start_logits = logits.new_empty([batch_size, max_seq_len])
|
||||
if end_logits is None:
|
||||
end_logits = logits.new_empty([batch_size, max_seq_len])
|
||||
ops.infer.bert_unpack_start_end_logits(
|
||||
logits, cu_seq_lens, start_logits, end_logits
|
||||
)
|
||||
return start_logits, end_logits
|
||||
|
||||
|
||||
def ref_bert_linear_residual(
|
||||
input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, out: torch.Tensor
|
||||
):
|
||||
return torch.nn.functional.linear(input, weight, bias) + out
|
||||
|
||||
|
||||
def bert_linear_residual(
|
||||
input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, out: torch.Tensor
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
input: (m, k) torch.float16, torch.bfloat16
|
||||
weight: (n, k) same as input
|
||||
bias: (n) same as input
|
||||
out: (m, n) same as input
|
||||
Returns:
|
||||
out: (m, n) same as input
|
||||
"""
|
||||
ops.infer.bert_linear_residual(input, weight, bias, out)
|
||||
return out
|
||||
Reference in New Issue
Block a user