From 7caa504a8008b7a7919e76377e2a4c73084385df Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Fri, 23 Jan 2026 01:56:49 +0800 Subject: [PATCH 01/27] Pass the test,may be need more review and check --- src/tensor/tensor.cpp | 102 ++++++++++++++++++++++++++++++++++++++---- 1 file changed, 94 insertions(+), 8 deletions(-) diff --git a/src/tensor/tensor.cpp b/src/tensor/tensor.cpp index 2f594bb65..c1b98a7e5 100644 --- a/src/tensor/tensor.cpp +++ b/src/tensor/tensor.cpp @@ -164,27 +164,113 @@ void Tensor::debug() const { } bool Tensor::isContiguous() const { - TO_BE_IMPLEMENTED(); + size_t Rank=this->ndim(); + const auto&Cur_strides=this->strides(); + const auto&Shapes=this->shape(); + ptrdiff_t accumulate_stride=1; + if(Rank==0) return true; + for(size_t i=Rank;i>0;--i){ + size_t index=i-1; + if(accumulate_stride!=Cur_strides[index]) return false; + accumulate_stride*=Shapes[index]; + } return true; } tensor_t Tensor::permute(const std::vector &order) const { - TO_BE_IMPLEMENTED(); - return std::shared_ptr(new Tensor(_meta, _storage)); + if(order.size()!=this->ndim()){ + throw std::runtime_error("Order Error!"); + } + const auto&old_shape=this->shape(); + const auto&old_strides=this->strides(); + std::vector new_shape(old_shape.size()); + std::vector new_strides(old_strides.size()); + for(size_t i=0;i=this->ndim()){ + throw std::runtime_error("Index Error!"); + } + new_shape[i]=old_shape[order_index]; + new_strides[i]=old_strides[order_index]; + } + TensorMeta _meta{this->dtype(),new_shape,new_strides}; + return std::shared_ptr(new Tensor(_meta, _storage,this->_offset)); } tensor_t Tensor::view(const std::vector &shape) const { - TO_BE_IMPLEMENTED(); - return std::shared_ptr(new Tensor(_meta, _storage)); + auto target_numel=std::accumulate(shape.begin(), shape.end(), size_t(1), std::multiplies()); + if(this->numel()!=target_numel) throw std::runtime_error("size error"); + if (this->isContiguous()) { + std::vector new_strides(shape.size()); + size_t stride = 1; + for (long i = shape.size() - 1; i >= 0; --i) { + new_strides[i] = stride; + stride *= shape[i]; + } + TensorMeta meta{this->dtype(), shape, new_strides}; + return std::shared_ptr(new Tensor(meta, this->_storage, this->_offset)); + } + std::vector new_strides;new_strides.reserve(shape.size()); + const auto&old_strides=this->strides(); + const auto&old_shape=this->shape(); + size_t old_dim_index=0,split_divisor=1; + for(size_t new_dim_index=0;new_dim_index=old_shape.size()){ + throw std::runtime_error("Dim Error!"); + } + size_t available_dim_size=old_shape[old_dim_index]/split_divisor; + size_t original_stride=old_strides[old_dim_index]; + if(target_dim=old_shape.size()){ + throw std::runtime_error("Dim Error!"); + } + if(accumulated_size>1){ + if(old_strides[old_dim_index-1]!=static_cast(old_shape[old_dim_index]*old_strides[old_dim_index])){ + throw std::runtime_error("Transform Error!"); + } + } + accumulated_size*=old_shape[old_dim_index++]; + } + if(accumulated_size!=target_dim){ + throw std::runtime_error("Match Error!"); + } + new_strides.emplace_back(old_strides[old_dim_index-1]); + } + TensorMeta _meta{this->dtype(),shape,new_strides}; + return std::shared_ptr(new Tensor(_meta, _storage,this->_offset)); } tensor_t Tensor::slice(size_t dim, size_t start, size_t end) const { - TO_BE_IMPLEMENTED(); - return std::shared_ptr(new Tensor(_meta, _storage)); + if(dim>=this->ndim()) throw std::runtime_error("Dim Error!"); + if(start>=end) throw std::runtime_error("Index Error!"); + if(end>this->shape()[dim]) throw std::runtime_error("End Error!"); + auto new_shape=this->shape(); + new_shape[dim]=end-start; + auto new_offset=this->_offset+start*this->strides()[dim]*this->elementSize(); + TensorMeta _meta{this->dtype(),new_shape,this->strides()}; + return std::shared_ptr(new Tensor(_meta, _storage,new_offset)); } void Tensor::load(const void *src_) { - TO_BE_IMPLEMENTED(); + std::byte* dis_ptr=this->data(); + size_t Elemsize_in_bytes=this->elementSize()*this->numel(); + llaisysMemcpyKind_t CurKind=this->deviceType()==LLAISYS_DEVICE_CPU?LLAISYS_MEMCPY_H2H:LLAISYS_MEMCPY_H2D; + core::context().runtime().api()->memcpy_sync(dis_ptr,src_,Elemsize_in_bytes,CurKind); } tensor_t Tensor::contiguous() const { From 0ac6892eb11ab9d9838ff89dce9c4c410631dded Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sat, 24 Jan 2026 23:36:29 +0800 Subject: [PATCH 02/27] Finish the taks 2.1 Argmax --- src/ops/argmax/op.cpp | 36 ++++++++++++++++++++++++++++++++++-- 1 file changed, 34 insertions(+), 2 deletions(-) diff --git a/src/ops/argmax/op.cpp b/src/ops/argmax/op.cpp index 6dc37d426..1b448e7ea 100644 --- a/src/ops/argmax/op.cpp +++ b/src/ops/argmax/op.cpp @@ -1,7 +1,39 @@ #include "op.hpp" - +#include "../../utils.hpp" namespace llaisys::ops { +template +void argmax_cpu_kernel(tensor_t max_idx,tensor_t max_val,const tensor_t vals){ + size_t n=vals->numel(); + const T*src=reinterpret_cast(vals->data()); + T*dst_val=reinterpret_cast(max_val->data()); + int64_t*dst_idx=reinterpret_cast(max_idx->data()); + float cur_max_fval=utils::cast(src[0]); + T cur_max_val=src[0]; + size_t cur_max_idx=0; + for(size_t i=1;i(src[i]); + if(cur_fval>cur_max_fval){ + cur_max_fval=cur_fval; + cur_max_idx=i; + cur_max_val=src[i]; + } + } + *dst_val=cur_max_val; + *dst_idx=cur_max_idx; +} void argmax(tensor_t max_idx, tensor_t max_val, tensor_t vals) { - TO_BE_IMPLEMENTED(); + switch (vals->dtype()) { + case LLAISYS_DTYPE_F16: + argmax_cpu_kernel(max_idx, max_val, vals); + break; + case LLAISYS_DTYPE_BF16: + argmax_cpu_kernel(max_idx, max_val, vals); + break; + case LLAISYS_DTYPE_F32: + argmax_cpu_kernel(max_idx, max_val, vals); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops From f5e61c2821cab8947afdbaec5e4e98557a455c6c Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sun, 25 Jan 2026 17:14:47 +0800 Subject: [PATCH 03/27] use the std::move --- src/tensor/tensor.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/tensor/tensor.cpp b/src/tensor/tensor.cpp index c1b98a7e5..aa84c3440 100644 --- a/src/tensor/tensor.cpp +++ b/src/tensor/tensor.cpp @@ -193,7 +193,7 @@ tensor_t Tensor::permute(const std::vector &order) const { new_shape[i]=old_shape[order_index]; new_strides[i]=old_strides[order_index]; } - TensorMeta _meta{this->dtype(),new_shape,new_strides}; + TensorMeta _meta{this->dtype(),std::move(new_shape),std::move(new_strides)}; return std::shared_ptr(new Tensor(_meta, _storage,this->_offset)); } @@ -251,7 +251,7 @@ tensor_t Tensor::view(const std::vector &shape) const { } new_strides.emplace_back(old_strides[old_dim_index-1]); } - TensorMeta _meta{this->dtype(),shape,new_strides}; + TensorMeta _meta{this->dtype(),shape,std::move(new_strides)}; return std::shared_ptr(new Tensor(_meta, _storage,this->_offset)); } @@ -262,7 +262,7 @@ tensor_t Tensor::slice(size_t dim, size_t start, size_t end) const { auto new_shape=this->shape(); new_shape[dim]=end-start; auto new_offset=this->_offset+start*this->strides()[dim]*this->elementSize(); - TensorMeta _meta{this->dtype(),new_shape,this->strides()}; + TensorMeta _meta{this->dtype(),std::move(new_shape),this->strides()}; return std::shared_ptr(new Tensor(_meta, _storage,new_offset)); } From 3a1ca5c0044c70de4b707f998e1712ecc5439e46 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sun, 25 Jan 2026 17:40:39 +0800 Subject: [PATCH 04/27] Finish Task 2.2 Embedding --- src/ops/embedding/op.cpp | 30 ++++++++++++++++++++++++++++-- 1 file changed, 28 insertions(+), 2 deletions(-) diff --git a/src/ops/embedding/op.cpp b/src/ops/embedding/op.cpp index 84b9a5d06..a21dfd50b 100644 --- a/src/ops/embedding/op.cpp +++ b/src/ops/embedding/op.cpp @@ -1,7 +1,33 @@ #include "op.hpp" - +#include namespace llaisys::ops { +template +void embedding_cpu_kernal(tensor_t out, tensor_t index, tensor_t weight){ + const T*weight_val=reinterpret_cast(weight->data()); + T*out_val=reinterpret_cast(out->data()); + const int64_t*index_val=reinterpret_cast(index->data()); + size_t embedding_dim=weight->shape().back(); + size_t n=index->numel(); + for(size_t i=0;idtype()) { + case LLAISYS_DTYPE_F16: + embedding_cpu_kernal(out,index,weight); + break; + case LLAISYS_DTYPE_BF16: + embedding_cpu_kernal(out,index,weight); + break; + case LLAISYS_DTYPE_F32: + embedding_cpu_kernal(out,index,weight); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops From 4ed8b986f3fa1311bd69693949132f16e56ae6cd Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sun, 25 Jan 2026 23:46:55 +0800 Subject: [PATCH 05/27] Pass Task 2-3 linear --- src/ops/linear/op.cpp | 39 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 37 insertions(+), 2 deletions(-) diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 97d1f8655..92d7f0836 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -1,7 +1,42 @@ #include "op.hpp" - +#include "../../utils.hpp" namespace llaisys::ops { +template +void linear_cpu_kernal(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ + const T*in_ptr=reinterpret_cast(in->data()); + const T*weight_ptr=reinterpret_cast(weight->data()); + const T*bias_ptr=nullptr; + T*out_ptr=reinterpret_cast(out->data()); + if(bias&&bias->numel()>0) bias_ptr=reinterpret_cast(bias->data()); + size_t M=in->shape()[0]; + size_t K=in->shape().back(); + size_t N=weight->shape()[0]; + for(size_t i=0;i(in_ptr[index+i*K]); + float y_val=utils::cast(weight_ptr[index+j*K]); + sum+=x_val*y_val; + } + if(bias_ptr) sum+=utils::cast(bias_ptr[j]); + out_ptr[i*N+j]=utils::cast(sum); + } + } +} void linear(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias) { - TO_BE_IMPLEMENTED(); + switch (in->dtype()) { + case LLAISYS_DTYPE_F16: + linear_cpu_kernal(out,in,weight,bias); + break; + case LLAISYS_DTYPE_BF16: + linear_cpu_kernal(out,in,weight,bias); + break; + case LLAISYS_DTYPE_F32: + linear_cpu_kernal(out,in,weight,bias); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops From 75306f76adf78ff0df425be6f9b929ff844b090e Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Mon, 26 Jan 2026 23:58:48 +0800 Subject: [PATCH 06/27] Finish the Task-2.4 RMS --- src/ops/rms_norm/op.cpp | 39 +++++++++++++++++++++++++++++++++++++-- 1 file changed, 37 insertions(+), 2 deletions(-) diff --git a/src/ops/rms_norm/op.cpp b/src/ops/rms_norm/op.cpp index 529553d9d..4d0300f55 100644 --- a/src/ops/rms_norm/op.cpp +++ b/src/ops/rms_norm/op.cpp @@ -1,7 +1,42 @@ #include "op.hpp" - +#include namespace llaisys::ops { +template +void rms_norm_cpu_kernal(tensor_t out, tensor_t in, tensor_t weight, float eps){ + T*out_ptr=reinterpret_cast(out->data()); + const T*in_ptr=reinterpret_cast(in->data()); + const T*weight_ptr=reinterpret_cast(weight->data()); + size_t n=in->shape()[0]; + size_t d=in->shape().back(); + for(size_t i=0;i(in_ptr[i*d+j]); + sum+=cur_num*cur_num; + } + sum/=(float)d; + float std_sum=sqrtf(sum+eps); + for(size_t j=0;j(weight_ptr[j]); + float xi=utils::cast(in_ptr[i*d+j]); + float out_val=wi*xi/std_sum; + out_ptr[i*d+j]=utils::cast(out_val); + } + } +} void rms_norm(tensor_t out, tensor_t in, tensor_t weight, float eps) { - TO_BE_IMPLEMENTED(); + switch (in->dtype()) { + case LLAISYS_DTYPE_F16: + rms_norm_cpu_kernal(out,in,weight,eps); + break; + case LLAISYS_DTYPE_BF16: + rms_norm_cpu_kernal(out,in,weight,eps); + break; + case LLAISYS_DTYPE_F32: + rms_norm_cpu_kernal(out,in,weight,eps); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops From e9a493779409017afde17a54f4b687664852012d Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Tue, 27 Jan 2026 23:09:59 +0800 Subject: [PATCH 07/27] Finish the task 2-5 RoPE and fix the kernel --- src/ops/embedding/op.cpp | 8 ++++---- src/ops/linear/op.cpp | 8 ++++---- src/ops/rearrange/op.cpp | 6 +++++- src/ops/rms_norm/op.cpp | 8 ++++---- src/ops/rope/op.cpp | 34 +++++++++++++++++++++++++++++++++- test/test_tensor.py | 1 - 6 files changed, 50 insertions(+), 15 deletions(-) diff --git a/src/ops/embedding/op.cpp b/src/ops/embedding/op.cpp index a21dfd50b..2561f7bd8 100644 --- a/src/ops/embedding/op.cpp +++ b/src/ops/embedding/op.cpp @@ -2,7 +2,7 @@ #include namespace llaisys::ops { template -void embedding_cpu_kernal(tensor_t out, tensor_t index, tensor_t weight){ +void embedding_cpu_kernel(tensor_t out, tensor_t index, tensor_t weight){ const T*weight_val=reinterpret_cast(weight->data()); T*out_val=reinterpret_cast(out->data()); const int64_t*index_val=reinterpret_cast(index->data()); @@ -18,13 +18,13 @@ void embedding_cpu_kernal(tensor_t out, tensor_t index, tensor_t weight){ void embedding(tensor_t out, tensor_t index, tensor_t weight) { switch (weight->dtype()) { case LLAISYS_DTYPE_F16: - embedding_cpu_kernal(out,index,weight); + embedding_cpu_kernel(out,index,weight); break; case LLAISYS_DTYPE_BF16: - embedding_cpu_kernal(out,index,weight); + embedding_cpu_kernel(out,index,weight); break; case LLAISYS_DTYPE_F32: - embedding_cpu_kernal(out,index,weight); + embedding_cpu_kernel(out,index,weight); break; default: throw std::runtime_error("Not support this dtype!"); diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 92d7f0836..e0423ad77 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -2,7 +2,7 @@ #include "../../utils.hpp" namespace llaisys::ops { template -void linear_cpu_kernal(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ +void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ const T*in_ptr=reinterpret_cast(in->data()); const T*weight_ptr=reinterpret_cast(weight->data()); const T*bias_ptr=nullptr; @@ -27,13 +27,13 @@ void linear_cpu_kernal(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias void linear(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias) { switch (in->dtype()) { case LLAISYS_DTYPE_F16: - linear_cpu_kernal(out,in,weight,bias); + linear_cpu_kernel(out,in,weight,bias); break; case LLAISYS_DTYPE_BF16: - linear_cpu_kernal(out,in,weight,bias); + linear_cpu_kernel(out,in,weight,bias); break; case LLAISYS_DTYPE_F32: - linear_cpu_kernal(out,in,weight,bias); + linear_cpu_kernel(out,in,weight,bias); break; default: throw std::runtime_error("Not support this dtype!"); diff --git a/src/ops/rearrange/op.cpp b/src/ops/rearrange/op.cpp index 017a6ae59..052876bae 100644 --- a/src/ops/rearrange/op.cpp +++ b/src/ops/rearrange/op.cpp @@ -1,7 +1,11 @@ #include "op.hpp" namespace llaisys::ops { +template +void rearrange_cpu_kernal(tensor_t out, tensor_t in){ + +} void rearrange(tensor_t out, tensor_t in) { - TO_BE_IMPLEMENTED(); + } } // namespace llaisys::ops diff --git a/src/ops/rms_norm/op.cpp b/src/ops/rms_norm/op.cpp index 4d0300f55..4a4b67f37 100644 --- a/src/ops/rms_norm/op.cpp +++ b/src/ops/rms_norm/op.cpp @@ -2,7 +2,7 @@ #include namespace llaisys::ops { template -void rms_norm_cpu_kernal(tensor_t out, tensor_t in, tensor_t weight, float eps){ +void rms_norm_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, float eps){ T*out_ptr=reinterpret_cast(out->data()); const T*in_ptr=reinterpret_cast(in->data()); const T*weight_ptr=reinterpret_cast(weight->data()); @@ -27,13 +27,13 @@ void rms_norm_cpu_kernal(tensor_t out, tensor_t in, tensor_t weight, float eps){ void rms_norm(tensor_t out, tensor_t in, tensor_t weight, float eps) { switch (in->dtype()) { case LLAISYS_DTYPE_F16: - rms_norm_cpu_kernal(out,in,weight,eps); + rms_norm_cpu_kernel(out,in,weight,eps); break; case LLAISYS_DTYPE_BF16: - rms_norm_cpu_kernal(out,in,weight,eps); + rms_norm_cpu_kernel(out,in,weight,eps); break; case LLAISYS_DTYPE_F32: - rms_norm_cpu_kernal(out,in,weight,eps); + rms_norm_cpu_kernel(out,in,weight,eps); break; default: throw std::runtime_error("Not support this dtype!"); diff --git a/src/ops/rope/op.cpp b/src/ops/rope/op.cpp index d60dbe64e..01fd98228 100644 --- a/src/ops/rope/op.cpp +++ b/src/ops/rope/op.cpp @@ -1,7 +1,39 @@ #include "op.hpp" +#include namespace llaisys::ops { +template +void rope_cpu_kernel(tensor_t out, tensor_t in, tensor_t pos_ids, float theta){ + T*out_ptr=reinterpret_cast(out->data()); + T*in_ptr=reinterpret_cast(in->data()); + int64_t*pos_ptr=reinterpret_cast(pos_ids->data()); + size_t N=in->shape()[0],M=in->shape()[1],D=in->shape()[2]; + for(size_t i=0;i(in_ptr[i*(M*D)+j*D+k]),b_in=utils::cast(in_ptr[i*(M*D)+j*D+k+D/2]); + float p_i=utils::cast(pos_ptr[i]); + float theta_in=p_i/powf(theta, 2.0f*k/D); + T a_out=utils::cast(a_in*cos(theta_in)-b_in*sin(theta_in)); + T b_out=utils::cast(b_in*cos(theta_in)+a_in*sin(theta_in)); + out_ptr[i*(M*D)+j*D+k]=a_out;out_ptr[i*(M*D)+j*D+k+D/2]=b_out; + } + } + } +} void rope(tensor_t out, tensor_t in, tensor_t pos_ids, float theta) { - TO_BE_IMPLEMENTED(); + switch (in->dtype()) { + case LLAISYS_DTYPE_F16: + rope_cpu_kernel(out,in,pos_ids,theta); + break; + case LLAISYS_DTYPE_BF16: + rope_cpu_kernel(out,in,pos_ids,theta); + break; + case LLAISYS_DTYPE_F32: + rope_cpu_kernel(out,in,pos_ids,theta); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops diff --git a/test/test_tensor.py b/test/test_tensor.py index 9d2e9a075..c701f58cf 100644 --- a/test/test_tensor.py +++ b/test/test_tensor.py @@ -1,5 +1,4 @@ import llaisys - import torch from test_utils import * import argparse From f3220e030dd5402e6468dc9cdc83b34429c61c0f Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Thu, 29 Jan 2026 01:23:27 +0800 Subject: [PATCH 08/27] Finish The Task 2-6 attention is all your need --- src/ops/self_attention/op.cpp | 76 ++++++++++++++++++++++++++++++++++- 1 file changed, 74 insertions(+), 2 deletions(-) diff --git a/src/ops/self_attention/op.cpp b/src/ops/self_attention/op.cpp index 43d620142..bd2553c35 100644 --- a/src/ops/self_attention/op.cpp +++ b/src/ops/self_attention/op.cpp @@ -1,7 +1,79 @@ #include "op.hpp" - +#include +const int INF=0x3f3f3f3f; namespace llaisys::ops { +template +void self_attention_kernel(tensor_t attn_val, tensor_t q, tensor_t k, tensor_t v, float scale){ + T*attn_val_ptr=reinterpret_cast(attn_val->data()); + const T*q_ptr=reinterpret_cast(q->data()); + const T*k_ptr=reinterpret_cast(k->data()); + const T*v_ptr=reinterpret_cast(v->data()); + size_t seqlen=q->shape()[0],nhead=q->shape()[1],d=q->shape()[2]; + size_t total_len=k->shape()[0],nkvhead=k->shape()[1]; + size_t dv=v->shape()[2]; + std::vector A(total_len); + auto get_k_index=[&](size_t n_index)->size_t{ + if(nkvhead==1) return 0; + if(nkvhead==nhead) return n_index; + return n_index/(nhead/nkvhead); + }; + for(size_t n_index=0;n_index(q_ptr[i*(nhead*d)+n_index*d+k]); + float k_val=utils::cast(k_ptr[j*(nkvhead*d)+k_index*d+k]); + sum+=q_val*k_val; + } + sum*=scale; + size_t global_i=total_len-seqlen+i; + if(j>global_i){ + A[j]=utils::cast(-INF); + } + else{ + A[j]=utils::cast(sum); + MAX_num=fmax(MAX_num,sum); + } + } + float softmax_accu=0; + for(size_t j=0;j(A[j]); + softmax_accu+=std::exp(a_val-MAX_num); + } + float inv_accu=1.0f/softmax_accu; + for(size_t j=0;j(A[j]); + float a_sval=std::exp(a_val-MAX_num); + A[j]=utils::cast(a_sval*inv_accu); + } + for(size_t j=0;j(A[k]); + float v_cv=utils::cast(v_ptr[k*nkvhead*dv+k_index*dv+j]); + out_sum+=a_cv*v_cv; + } + attn_val_ptr[i*nhead*dv+n_index*dv+j]=utils::cast(out_sum); + } + } + } +} void self_attention(tensor_t attn_val, tensor_t q, tensor_t k, tensor_t v, float scale) { - TO_BE_IMPLEMENTED(); + switch (q->dtype()) { + case LLAISYS_DTYPE_F16: + self_attention_kernel(attn_val,q,k,v,scale); + break; + case LLAISYS_DTYPE_BF16: + self_attention_kernel(attn_val,q,k,v,scale); + break; + case LLAISYS_DTYPE_F32: + self_attention_kernel(attn_val,q,k,v,scale); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops From 29bd22f516efb53eb72cb6ed612c0285a3c6bdc4 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Thu, 29 Jan 2026 22:42:26 +0800 Subject: [PATCH 09/27] Finish all the Task 1 Task 2 --- src/ops/rearrange/op.cpp | 34 +++++++++++++++++++++++++++++++--- src/ops/swiglu/op.cpp | 32 +++++++++++++++++++++++++++++++- 2 files changed, 62 insertions(+), 4 deletions(-) diff --git a/src/ops/rearrange/op.cpp b/src/ops/rearrange/op.cpp index 052876bae..f43fb55b4 100644 --- a/src/ops/rearrange/op.cpp +++ b/src/ops/rearrange/op.cpp @@ -2,10 +2,38 @@ namespace llaisys::ops { template -void rearrange_cpu_kernal(tensor_t out, tensor_t in){ - +void rearrange_kernel(tensor_t out, tensor_t in){ + const T*in_ptr=reinterpret_cast(in->data()); + T*out_ptr=reinterpret_cast(out->data()); + size_t total_num=in->numel(); + size_t dim=in->ndim(); + for(size_t i=0;i=0;j--){ + size_t cur_shape=in->shape()[j]; + size_t cur_index=index_acc%cur_shape; + index_acc/=cur_shape; + in_offset+=cur_index*in->strides()[j]; + out_offset+=cur_index*out->strides()[j]; + } + out_ptr[out_offset]=in_ptr[in_offset]; + } } void rearrange(tensor_t out, tensor_t in) { - + switch (in->dtype()) { + case LLAISYS_DTYPE_F16: + rearrange_kernel(out,in); + break; + case LLAISYS_DTYPE_BF16: + rearrange_kernel(out,in); + break; + case LLAISYS_DTYPE_F32: + rearrange_kernel(out,in); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops diff --git a/src/ops/swiglu/op.cpp b/src/ops/swiglu/op.cpp index 47edbcc97..74005888d 100644 --- a/src/ops/swiglu/op.cpp +++ b/src/ops/swiglu/op.cpp @@ -1,7 +1,37 @@ #include "op.hpp" +#include namespace llaisys::ops { +template +void swiglu_kernel(tensor_t out, tensor_t gate, tensor_t up){ + T*out_ptr=reinterpret_cast(out->data()); + const T*gate_ptr=reinterpret_cast(gate->data()); + const T*up_ptr=reinterpret_cast(up->data()); + size_t seqlen=out->shape()[0]; + size_t intermediate_size=out->shape()[1]; + for(size_t i=0;i(up_ptr[i*intermediate_size+j]); + float gate_val=utils::cast(gate_ptr[i*intermediate_size+j]); + float t_val=gate_val/(1+std::exp(-gate_val)); + float out_val=up_val*t_val; + out_ptr[i*intermediate_size+j]=utils::cast(out_val); + } + } +} void swiglu(tensor_t out, tensor_t gate, tensor_t up) { - TO_BE_IMPLEMENTED(); + switch (out->dtype()) { + case LLAISYS_DTYPE_F16: + swiglu_kernel(out,gate,up); + break; + case LLAISYS_DTYPE_BF16: + swiglu_kernel(out,gate,up); + break; + case LLAISYS_DTYPE_F32: + swiglu_kernel(out,gate,up); + break; + default: + throw std::runtime_error("Not support this dtype!"); + } } } // namespace llaisys::ops From 5f8a982f3481959ceb8e223b0260434e86bdab12 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sun, 1 Feb 2026 01:06:36 +0800 Subject: [PATCH 10/27] Finsh the bind --- python/llaisys/models/qwen2.py | 109 +++++++++++++++++++++++++++++++-- src/models/qwen2.cpp | 42 +++++++++++++ test/debug.py | 41 +++++++++++++ xmake.lua | 1 + 4 files changed, 188 insertions(+), 5 deletions(-) create mode 100644 src/models/qwen2.cpp create mode 100644 test/debug.py diff --git a/python/llaisys/models/qwen2.py b/python/llaisys/models/qwen2.py index 0d07b0b21..50012b8fe 100644 --- a/python/llaisys/models/qwen2.py +++ b/python/llaisys/models/qwen2.py @@ -4,21 +4,120 @@ from pathlib import Path import safetensors +import ctypes +import numpy as np +import struct +import json +import mmap +class LlaisysQwen2Meta(ctypes.Structure): + _fields_ = [ + ("dtype",ctypes.c_int), + ("nlayer",ctypes.c_size_t), + ("hs",ctypes.c_size_t), + ("nh",ctypes.c_size_t), + ("nkvh",ctypes.c_size_t), + ("dh",ctypes.c_size_t), + ("di",ctypes.c_size_t), + ("maxseq",ctypes.c_size_t), + ("voc",ctypes.c_size_t), + ("epsilon",ctypes.c_float), + ("theta",ctypes.c_float), + ("end_token",ctypes.c_int64,) + ] class Qwen2: def __init__(self, model_path, device: DeviceType = DeviceType.CPU): - # TODO: Implement model constructor + print("DEBUG:Loading NEW Qwen code ...") + LIB_LLAISYS.llaisysQwen2ModelCreate.argtypes=[ + ctypes.POINTER(LlaisysQwen2Meta), + ctypes.c_int, + ctypes.POINTER(ctypes.c_int), + ctypes.c_int + ] + LIB_LLAISYS.llaisysQwen2ModelCreate.restype=ctypes.c_void_p + + LIB_LLAISYS.llaisysQwen2LoadWeight.argtypes=[ + ctypes.c_void_p, + ctypes.c_char_p, + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_int), + ctypes.c_int, + ctypes.c_int + ] + LIB_LLAISYS.llaisysQwen2LoadWeight.restype=None + + meta=LlaisysQwen2Meta() + meta.nlayer=28 + meta.hs=1536 + + model_path = Path(model_path) + + print("Python:calling the cpp to create model ...") + self.model=LIB_LLAISYS.llaisysQwen2ModelCreate( + ctypes.byref(meta), + device.value, + None, + 0 + ) + print("Python: Model handle received:",self.model) model_path = Path(model_path) + TYPE_MAP={ + "F32":13, + "F16":11, + "BF16":19 + } + + print("Python: Scanning .safetensors files...") + for file in sorted(model_path.glob("*.safetensors")): - data_ = safetensors.safe_open(file, framework="numpy", device="cpu") - for name_ in data_.keys(): - ## TODO: load the model weights - pass + with open(file,'rb') as f_obj: + header_size=struct.unpack(' +#include + +extern "C"{ + struct LlaisysQwen2Model{ + LlaisysQwen2Meta meta; + LlaisysQwen2Weights weights; + llaisysDeviceType_t device; + std::vector k_cache; + std::vector v_cache; + llaisys::tensor_t hidden_states; +}; +LlaisysQwen2Model *llaisysQwen2ModelCreate(const LlaisysQwen2Meta *meta, llaisysDeviceType_t device, int *device_ids, int ndevice){ + auto model=new LlaisysQwen2Model(); + model->meta=*meta; + model->device=device; + model->k_cache.resize(meta->nlayer); + model->v_cache.resize(meta->nlayer); + printf("Cpp:Qwen2 Model Initialized on device: %d ! Layers: %lu\n",(int)model->device,meta->nlayer); + fflush(stdout); + return model; +} +void llaisysQwen2ModelDestroy(struct LlaisysQwen2Model * model){ + if(model){ + delete model; + } +} +void llaisysQwen2LoadWeight( + LlaisysQwen2Model*model, + const char*name, + void*data, + int*shape, + int dim, + llaisysDataType_t dtype +){ + printf("Cpp Loading: %s | Type Enum: %d | Size : %lu bytes\n",name,(int)dtype,llaisys::utils::dsize(dtype)); + fflush(stdout); +} +} \ No newline at end of file diff --git a/test/debug.py b/test/debug.py new file mode 100644 index 000000000..0dde62296 --- /dev/null +++ b/test/debug.py @@ -0,0 +1,41 @@ +import argparse +from test_utils import * +import llaisys +import sys +import ctypes +from pathlib import Path + +# 不需要 snapshot_download 了，因为你已经下载好了 +# from huggingface_hub import snapshot_download + +def test_binding_only(): + print("--- Start Binding Test ---") + + # 1. 直接指定你刚才下载好的、确定的绝对路径 + # 注意：确保这个文件夹里真的有 .safetensors 文件 + real_model_path = "/home/cpp/ai-models/DeepSeek-R1-Distill-Qwen-1.5B" + + print(f"1. Using local model at: {real_model_path}") + + # 2. 检查一下路径对不对 (防御性编程) + if not Path(real_model_path).exists(): + print(f"!!! Error: Path does not exist: {real_model_path}") + return + + # 3. 尝试加载 C++ 模型 + try: + print("2. Calling C++ Qwen2 Init (Create + LoadWeights)...") + + # 直接传路径字符串！ + model = llaisys.models.Qwen2(real_model_path, llaisys_device("cpu")) + + print("3. Success! C++ Object Created & Weights Loaded.") + print(f" Model Object: {model}") + + except Exception as e: + print(f"!!! Error Occurred: {e}") + import traceback + traceback.print_exc() + +if __name__ == "__main__": + test_binding_only() \ No newline at end of file diff --git a/xmake.lua b/xmake.lua index 1f65f7a95..55db660c2 100644 --- a/xmake.lua +++ b/xmake.lua @@ -106,6 +106,7 @@ target("llaisys") set_languages("cxx17") set_warnings("all", "error") add_files("src/llaisys/*.cc") + add_files("src/models/*.cpp") set_installdir(".") From 840d1346c26b180727c3a23e19e33da49540af6e Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Mon, 2 Feb 2026 01:15:04 +0800 Subject: [PATCH 11/27] Finish forward init --- python/llaisys/libllaisys/__init__.py | 43 +++++++ python/llaisys/libllaisys/llaisys_types.py | 17 +++ python/llaisys/models/qwen2.py | 64 +++-------- src/models/qwen2.cpp | 125 +++++++++++++++++++-- 4 files changed, 191 insertions(+), 58 deletions(-) diff --git a/python/llaisys/libllaisys/__init__.py b/python/llaisys/libllaisys/__init__.py index f536fb527..0d3952c34 100644 --- a/python/llaisys/libllaisys/__init__.py +++ b/python/llaisys/libllaisys/__init__.py @@ -9,6 +9,7 @@ from .llaisys_types import llaisysDataType_t, DataType from .llaisys_types import llaisysMemcpyKind_t, MemcpyKind from .llaisys_types import llaisysStream_t +from .llaisys_types import LlaisysQwen2Meta from .tensor import llaisysTensor_t from .tensor import load_tensor from .ops import load_ops @@ -33,11 +34,50 @@ def load_shared_library(): return ctypes.CDLL(str(lib_path)) +def load_qwen2_api(lib): + try: + if hasattr(lib,'llaisysQwen2ModelCreate'): + lib.llaisysQwen2ModelCreate.argtypes=[ + ctypes.POINTER(LlaisysQwen2Meta), + ctypes.c_int, + ctypes.POINTER(ctypes.c_int), + ctypes.c_int + ] + lib.llaisysQwen2ModelCreate.restype=ctypes.c_void_p + if hasattr(lib,'llaisysQwen2LoadWeight'): + lib.llaisysQwen2LoadWeight.argtypes=[ + ctypes.c_void_p, + ctypes.c_char_p, + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_size_t), + ctypes.c_size_t, + ctypes.c_int + ] + lib.llaisysQwen2LoadWeight.restype=None + except Exception as e: + print(f"Warning: Failed to load Qwen2 API signatures. {e}") +def llaisys_qwen2_create(meta,device_id): + return LIB_LLAISYS.llaisysQwen2ModelCreate( + ctypes.byref(meta), + device_id, + None, + 0 + ) +def llaisys_qwen2_load_weight(model_handle,name,data_ptr,shape,ndim,dtype): + LIB_LLAISYS.llaisysQwen2LoadWeight( + model_handle, + name, + data_ptr, + shape, + ndim, + dtype + ) LIB_LLAISYS = load_shared_library() load_runtime(LIB_LLAISYS) load_tensor(LIB_LLAISYS) load_ops(LIB_LLAISYS) +load_qwen2_api(LIB_LLAISYS) __all__ = [ @@ -52,4 +92,7 @@ def load_shared_library(): "llaisysMemcpyKind_t", "MemcpyKind", "llaisysStream_t", + "LlaisysQwen2Meta", + "llaisys_qwen2_create", + "llaisys_qwen2_load_weight" ] diff --git a/python/llaisys/libllaisys/llaisys_types.py b/python/llaisys/libllaisys/llaisys_types.py index c5a0b4679..a7b20be13 100644 --- a/python/llaisys/libllaisys/llaisys_types.py +++ b/python/llaisys/libllaisys/llaisys_types.py @@ -61,3 +61,20 @@ class MemcpyKind(IntEnum): "MemcpyKind", "llaisysStream_t", ] + + +class LlaisysQwen2Meta(ctypes.Structure): + _fields_ = [ + ("dtype",ctypes.c_int), + ("nlayer",ctypes.c_size_t), + ("hs",ctypes.c_size_t), + ("nh",ctypes.c_size_t), + ("nkvh",ctypes.c_size_t), + ("dh",ctypes.c_size_t), + ("di",ctypes.c_size_t), + ("maxseq",ctypes.c_size_t), + ("voc",ctypes.c_size_t), + ("epsilon",ctypes.c_float), + ("theta",ctypes.c_float), + ("end_token",ctypes.c_int64,) + ] diff --git a/python/llaisys/models/qwen2.py b/python/llaisys/models/qwen2.py index 50012b8fe..d918d3e6c 100644 --- a/python/llaisys/models/qwen2.py +++ b/python/llaisys/models/qwen2.py @@ -10,57 +10,24 @@ import json import mmap -class LlaisysQwen2Meta(ctypes.Structure): - _fields_ = [ - ("dtype",ctypes.c_int), - ("nlayer",ctypes.c_size_t), - ("hs",ctypes.c_size_t), - ("nh",ctypes.c_size_t), - ("nkvh",ctypes.c_size_t), - ("dh",ctypes.c_size_t), - ("di",ctypes.c_size_t), - ("maxseq",ctypes.c_size_t), - ("voc",ctypes.c_size_t), - ("epsilon",ctypes.c_float), - ("theta",ctypes.c_float), - ("end_token",ctypes.c_int64,) - ] +from ..libllaisys import( + DeviceType, + LlaisysQwen2Meta, + llaisys_qwen2_create, + llaisys_qwen2_load_weight +) class Qwen2: def __init__(self, model_path, device: DeviceType = DeviceType.CPU): print("DEBUG:Loading NEW Qwen code ...") - LIB_LLAISYS.llaisysQwen2ModelCreate.argtypes=[ - ctypes.POINTER(LlaisysQwen2Meta), - ctypes.c_int, - ctypes.POINTER(ctypes.c_int), - ctypes.c_int - ] - LIB_LLAISYS.llaisysQwen2ModelCreate.restype=ctypes.c_void_p - - LIB_LLAISYS.llaisysQwen2LoadWeight.argtypes=[ - ctypes.c_void_p, - ctypes.c_char_p, - ctypes.c_void_p, - ctypes.POINTER(ctypes.c_int), - ctypes.c_int, - ctypes.c_int - ] - LIB_LLAISYS.llaisysQwen2LoadWeight.restype=None meta=LlaisysQwen2Meta() meta.nlayer=28 meta.hs=1536 - model_path = Path(model_path) - print("Python:calling the cpp to create model ...") - self.model=LIB_LLAISYS.llaisysQwen2ModelCreate( - ctypes.byref(meta), - device.value, - None, - 0 - ) + self.model=llaisys_qwen2_create(meta,device.value) print("Python: Model handle received:",self.model) model_path = Path(model_path) @@ -102,18 +69,17 @@ def __init__(self, model_path, device: DeviceType = DeviceType.CPU): c_name=name_.encode('utf-8') ndim=len(shape) - ShapeArrayType=ctypes.c_int*ndim + ShapeArrayType=ctypes.c_size_t*ndim c_shape=ShapeArrayType(*shape) c_data_ptr=ctypes.c_void_p(tensor_np.ctypes.data) - - LIB_LLAISYS.llaisysQwen2LoadWeight( - self.model, - c_name, - c_data_ptr, - c_shape, - ndim, - dtype + llaisys_qwen2_load_weight( + self.model, + c_name, + c_data_ptr, + c_shape, + ndim, + dtype ) del tensor_np print("Python: All weights loaded") diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp index 41af75e03..1f748aa50 100644 --- a/src/models/qwen2.cpp +++ b/src/models/qwen2.cpp @@ -1,22 +1,51 @@ #include "llaisys/models/qwen2.h" #include "../llaisys/llaisys_tensor.hpp" +#include "../ops/embedding/op.hpp" #include "llaisys.h" #include #include +#include +#include +using namespace llaisys; -extern "C"{ - struct LlaisysQwen2Model{ +struct Qwen2Layer{ + tensor_t attention_norm; + tensor_t w_q; + tensor_t w_k; + tensor_t w_v; + tensor_t w_o; + + tensor_t b_q; + tensor_t b_k; + tensor_t b_v; + tensor_t b_o; + + tensor_t ffn_norm; + tensor_t w_gate; + tensor_t w_up; + tensor_t w_down; +}; +struct LlaisysQwen2Model{ LlaisysQwen2Meta meta; - LlaisysQwen2Weights weights; llaisysDeviceType_t device; - std::vector k_cache; - std::vector v_cache; - llaisys::tensor_t hidden_states; + + tensor_t tok_embeddings; + tensor_t norm; + tensor_t output; + + std::vector layers; + + std::vector k_cache; + std::vector v_cache; }; + +extern "C"{ + LlaisysQwen2Model *llaisysQwen2ModelCreate(const LlaisysQwen2Meta *meta, llaisysDeviceType_t device, int *device_ids, int ndevice){ auto model=new LlaisysQwen2Model(); model->meta=*meta; model->device=device; + model->layers.resize(meta->nlayer); model->k_cache.resize(meta->nlayer); model->v_cache.resize(meta->nlayer); printf("Cpp:Qwen2 Model Initialized on device: %d ! Layers: %lu\n",(int)model->device,meta->nlayer); @@ -32,11 +61,89 @@ void llaisysQwen2LoadWeight( LlaisysQwen2Model*model, const char*name, void*data, - int*shape, - int dim, + size_t*shape, + size_t ndim, llaisysDataType_t dtype ){ - printf("Cpp Loading: %s | Type Enum: %d | Size : %lu bytes\n",name,(int)dtype,llaisys::utils::dsize(dtype)); + std::string w_name(name); + tensor_t* target=nullptr; + + if(w_name=="model.embed_tokens.weight"){ + target=&model->tok_embeddings; + } + else if(w_name=="model.norm.weight"){ + target=&model->norm; + } + else if(w_name=="lm_head.weight"){ + target=&model->output; + } + + else if(w_name.find("model.layers.")==0){ + size_t first_dot=13; + size_t second_dot=w_name.find(".",first_dot); + std::string layer_id_str=w_name.substr(first_dot,second_dot-first_dot); + size_t layer_id=std::stoull(layer_id_str); + + if(layer_id>=0&&layer_idmeta.nlayer){ + auto&layer=model->layers[layer_id]; + std::string suffix=w_name.substr(second_dot+1); + + if(suffix=="self_attn.q_proj.weight") target=&layer.w_q; + else if(suffix=="self_attn.k_proj.weight") target=&layer.w_k; + else if(suffix=="self_attn.v_proj.weight") target=&layer.w_v; + else if(suffix=="self_attn.o_proj.weight") target=&layer.w_o; + else if(suffix=="self_attn.q_proj.bias") target=&layer.b_q; + else if(suffix=="self_attn.k_proj.bias") target=&layer.b_k; + else if(suffix=="self_attn.v_proj.bias") target=&layer.b_v; + else if(suffix=="self_attn.o_proj.bias") target=&layer.b_o; + else if(suffix=="mlp.gate_proj.weight") target=&layer.w_gate; + else if(suffix=="mlp.up_proj.weight") target=&layer.w_up; + else if(suffix=="mlp.down_proj.weight") target=&layer.w_down; + else if(suffix=="input_layernorm.weight") target=&layer.attention_norm; + else if(suffix=="post_attention_layernorm.weight") target=&layer.ffn_norm; + } + } + if(target){ + std::vector shape_vec(shape,shape+ndim); + + *target=Tensor::create(shape_vec,dtype,model->device,0); + + (*target)->load(data); + + printf("Cpp loaded: %s -> shape [",name); + for(size_t i=0;idevice; + auto dtype=model->tok_embeddings->dtype(); + size_t hs=model->meta.hs; + + std::vector input_shape={1,seq_len}; + + tensor_t input_tensor=Tensor::create(input_shape,LLAISYS_DTYPE_I64,device,0); + + input_tensor->load(input_ids_ptr); + + std::vector hidden_shape={1,seq_len,hs}; + + tensor_t hidden_states=Tensor::create(hidden_shape,dtype,device,0); + + ops::embedding(hidden_states, input_tensor, model->tok_embeddings); + + return hidden_states; + } } \ No newline at end of file From 424234f4bfb5f6d08969daa0abf53ebc8b23c018 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Tue, 3 Feb 2026 01:38:18 +0800 Subject: [PATCH 12/27] kv_cache --- include/llaisys/models/qwen2.h | 2 +- python/llaisys/libllaisys/__init__.py | 1 + python/llaisys/models/qwen2.py | 59 ++++++++++++---- src/models/qwen2.cpp | 96 ++++++++++++++++++++++++++- 4 files changed, 142 insertions(+), 16 deletions(-) diff --git a/include/llaisys/models/qwen2.h b/include/llaisys/models/qwen2.h index 7054626d4..6a51ac426 100644 --- a/include/llaisys/models/qwen2.h +++ b/include/llaisys/models/qwen2.h @@ -31,7 +31,7 @@ __C { struct LlaisysQwen2Model; - __export struct LlaisysQwen2Model *llaisysQwen2ModelCreate(const LlaisysQwen2Meta *meta, llaisysDeviceType_t device, int *device_ids, int ndevice); + __export struct LlaisysQwen2Model *llaisysQwen2ModelCreate(const LlaisysQwen2Meta *meta, llaisysDeviceType_t device, int *device_ids, int ndevice,llaisysDataType_t dtype); __export void llaisysQwen2ModelDestroy(struct LlaisysQwen2Model * model); diff --git a/python/llaisys/libllaisys/__init__.py b/python/llaisys/libllaisys/__init__.py index 0d3952c34..e5c229c78 100644 --- a/python/llaisys/libllaisys/__init__.py +++ b/python/llaisys/libllaisys/__init__.py @@ -41,6 +41,7 @@ def load_qwen2_api(lib): ctypes.POINTER(LlaisysQwen2Meta), ctypes.c_int, ctypes.POINTER(ctypes.c_int), + ctypes.c_int, ctypes.c_int ] lib.llaisysQwen2ModelCreate.restype=ctypes.c_void_p diff --git a/python/llaisys/models/qwen2.py b/python/llaisys/models/qwen2.py index d918d3e6c..a26d3a43a 100644 --- a/python/llaisys/models/qwen2.py +++ b/python/llaisys/models/qwen2.py @@ -9,6 +9,7 @@ import struct import json import mmap +import os from ..libllaisys import( DeviceType, @@ -16,28 +17,60 @@ llaisys_qwen2_create, llaisys_qwen2_load_weight ) - +TYPE_MAP={ + "F32":13, + "F16":11, + "BF16":19 + } class Qwen2: def __init__(self, model_path, device: DeviceType = DeviceType.CPU): - print("DEBUG:Loading NEW Qwen code ...") - + self.lib=LIB_LLAISYS + + config_path=os.path.join(model_path,"config.json") + if not os.path.exists(config_path): + raise FileNotFoundError(f"Config not found at {config_path}") + with open(config_path,"r") as f: + config=json.load(f) + meta=LlaisysQwen2Meta() - meta.nlayer=28 - meta.hs=1536 - + meta.nlayer=config.get("num_hidden_layers",28) + meta.hs=config.get("hidden_size",1536) + meta.nh=config.get("num_attention_heads",12) + meta.nkvh=config.get("num_key_value_heads",2) + meta.vocab_size=config.get("vocab_size",151936) + meta.max_seq_len=config.get("max_position_embeddings",32768) + meta.epsilon=config.get("rms_norm_eps",1e-6) + meta.theta=config.get("rope_theta",10000.0) + + config_dtype_str=config.get("torch_dtype","float16") + + target_key="F16" + if config_dtype_str=="float32": + target_key="F32" + elif config_dtype_str=="bfloat16": + target_key="BF16" + elif config_dtype_str=="float16": + target_key="F16" + + if target_key not in TYPE_MAP: + print(f"Warning: Unknown dtype {config_dtype_str}, using F16") + target_dtype=TYPE_MAP["F16"] + else: + target_dtype=TYPE_MAP[target_key] + print(f"Python: Loading Model config | Layers={meta.nlayer} | HS={meta.hs} | Dtype={target_key}({target_dtype})") print("Python:calling the cpp to create model ...") - self.model=llaisys_qwen2_create(meta,device.value) + self.model=self.lib.llaisysQwen2ModelCreate( + ctypes.byref(meta), + device.value, + None, + 0, + target_dtype + ) print("Python: Model handle received:",self.model) model_path = Path(model_path) - TYPE_MAP={ - "F32":13, - "F16":11, - "BF16":19 - } - print("Python: Scanning .safetensors files...") for file in sorted(model_path.glob("*.safetensors")): diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp index 1f748aa50..2bd89b570 100644 --- a/src/models/qwen2.cpp +++ b/src/models/qwen2.cpp @@ -1,7 +1,14 @@ #include "llaisys/models/qwen2.h" #include "../llaisys/llaisys_tensor.hpp" #include "../ops/embedding/op.hpp" +#include "../ops/rms_norm/op.hpp" +#include "../ops/linear/op.hpp" +#include "../ops/rope/op.hpp" +#include "../ops/self_attention/op.hpp" +#include "../ops/swiglu/op.hpp" +#include "../ops/add/op.hpp" #include "llaisys.h" +#include #include #include #include @@ -41,13 +48,21 @@ struct LlaisysQwen2Model{ extern "C"{ -LlaisysQwen2Model *llaisysQwen2ModelCreate(const LlaisysQwen2Meta *meta, llaisysDeviceType_t device, int *device_ids, int ndevice){ +LlaisysQwen2Model *llaisysQwen2ModelCreate(const LlaisysQwen2Meta *meta, llaisysDeviceType_t device, int *device_ids, int ndevice,llaisysDataType_t dtype){ auto model=new LlaisysQwen2Model(); model->meta=*meta; model->device=device; model->layers.resize(meta->nlayer); model->k_cache.resize(meta->nlayer); model->v_cache.resize(meta->nlayer); + + size_t head_dim=meta->hs/meta->nh; + std::vector cache_shape={1,meta->maxseq,meta->nkvh,head_dim}; + + for(size_t i=0;inlayer;++i){ + model->k_cache[i]=Tensor::create(cache_shape, dtype,device,0); + model->v_cache[i]=Tensor::create(cache_shape, dtype,device,0); + } printf("Cpp:Qwen2 Model Initialized on device: %d ! Layers: %lu\n",(int)model->device,meta->nlayer); fflush(stdout); return model; @@ -130,6 +145,10 @@ tensor_t llaisysQwen2ModelForward( auto device=model->device; auto dtype=model->tok_embeddings->dtype(); size_t hs=model->meta.hs; + size_t head_dim=hs/model->meta.nh; + size_t kv_dim=head_dim*model->meta.nkvh; + std::vector q_shape={1,seq_len,hs}; + std::vector kv_shape={1,seq_len,kv_dim}; std::vector input_shape={1,seq_len}; @@ -143,7 +162,80 @@ tensor_t llaisysQwen2ModelForward( ops::embedding(hidden_states, input_tensor, model->tok_embeddings); - return hidden_states; + std::vector pos_shape={1,seq_len}; + tensor_t pos_ids=Tensor::create(pos_shape, LLAISYS_DTYPE_I64,device,0); + + std::vector pos_vec(seq_len); + for(size_t i=0;iload(pos_vec.data()); + + for(size_t i=0;imeta.nlayer;++i){ + auto&layer=model->layers[i]; + + tensor_t norm_out=Tensor::create(hidden_shape,dtype,device,0); + ops::rms_norm(norm_out, hidden_states, layer.attention_norm,model->meta.epsilon); + + tensor_t q=Tensor::create(q_shape,dtype,device,0); + tensor_t k=Tensor::create(kv_shape,dtype,device,0); + tensor_t v=Tensor::create(kv_shape,dtype,device,0); + + ops::linear(q, norm_out, layer.w_q, layer.b_q); + ops::linear(k, norm_out, layer.w_k, layer.b_k); + ops::linear(v, norm_out, layer.w_v, layer.b_v); + + ops::rope(q, q, pos_ids, model->meta.theta); + ops::rope(k, k, pos_ids, model->meta.theta); + // TODO: Implement KV Cache Append + tensor_t k_slot=model->k_cache[i]->slice(1,start_pos,start_pos+seq_len); + tensor_t v_slot=model->v_cache[i]->slice(1,start_pos,start_pos+seq_len); + + k_slot->load(k->data()); + v_slot->load(v->data()); + + tensor_t full_k=model->k_cache[i]->slice(1,0,start_pos+seq_len); + tensor_t full_v=model->v_cache[i]->slice(1,0,start_pos+seq_len); + + tensor_t attn_out=Tensor::create(hidden_shape,dtype,device,0); + float scale=1.0f/std::sqrt((float)head_dim); + + ops::self_attention(attn_out, q, full_k, full_v, scale); + + tensor_t proj_out=Tensor::create(hidden_shape,dtype,device,0); + ops::linear(proj_out, attn_out, layer.w_o, layer.b_o); + + ops::add(hidden_states, hidden_states, proj_out); + + tensor_t ffn_norm_out=Tensor::create(hidden_shape,dtype,device,0); + ops::rms_norm(ffn_norm_out, hidden_states,layer.ffn_norm,model->meta.epsilon); + + size_t inter_size=layer.w_gate->shape()[0]; + std::vector inter_shape={1,seq_len,inter_size}; + + tensor_t gate=Tensor::create(inter_shape,dtype,device,0); + tensor_t up=Tensor::create(inter_shape,dtype,device,0); + + ops::linear(gate, ffn_norm_out, layer.w_gate, nullptr); + ops::linear(up, ffn_norm_out,layer.w_up,nullptr); + + tensor_t act=Tensor::create(inter_shape,dtype,device,0); + ops::swiglu(act, gate, up); + + tensor_t mlp_out=Tensor::create(hidden_shape, dtype,device,0); + ops::linear(mlp_out, act, layer.w_down, nullptr); + + ops::add(hidden_states, hidden_states, mlp_out); + } + + tensor_t final_norm=Tensor::create(hidden_shape,dtype,device,0); + ops::rms_norm(final_norm, hidden_states, model->norm, model->meta.epsilon); + + size_t vocab_size=model->output->shape()[0]; + std::vector logits_shape={1,seq_len,vocab_size}; + tensor_t logits=Tensor::create(logits_shape, dtype,device,0); + + ops::linear(logits, final_norm, model->output, nullptr); + + return logits; } } \ No newline at end of file From 123f48f99437233b48406e6f5adc2590f5532f9e Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Wed, 4 Feb 2026 19:43:49 +0800 Subject: [PATCH 13/27] Fix the bug --- python/llaisys/models/qwen2.py | 68 +++++++- src/core/core.hpp:Zone.Identifier | 3 + src/models/qwen2.cpp | 83 ++++++++-- src/ops/argmax/op.cpp | 46 +++--- src/ops/linear/op.cpp | 60 +++++-- src/ops/rms_norm/op.cpp | 9 +- src/ops/rope/op.cpp | 156 ++++++++++++++++-- src/ops/self_attention/op.cpp | 220 +++++++++++++++++++------ src/ops/swiglu/op.cpp | 31 ++-- src/tensor/tensor.cpp | 27 ++- src/utils/{types.cpp => types.cpp.old} | 1 - src/utils/types.hpp | 85 +++++++++- 12 files changed, 639 insertions(+), 150 deletions(-) create mode 100644 src/core/core.hpp:Zone.Identifier rename src/utils/{types.cpp => types.cpp.old} (99%) diff --git a/python/llaisys/models/qwen2.py b/python/llaisys/models/qwen2.py index a26d3a43a..48d7d8236 100644 --- a/python/llaisys/models/qwen2.py +++ b/python/llaisys/models/qwen2.py @@ -18,8 +18,10 @@ llaisys_qwen2_load_weight ) TYPE_MAP={ + # 必须与 C++ enum llaisysDataType_t 完全一致 + # 参见 include/llaisys.h 和 python/llaisys/libllaisys/llaisys_types.py "F32":13, - "F16":11, + "F16":12, # 修复: 之前错误地映射为 11(F8) "BF16":19 } class Qwen2: @@ -39,7 +41,7 @@ def __init__(self, model_path, device: DeviceType = DeviceType.CPU): meta.nh=config.get("num_attention_heads",12) meta.nkvh=config.get("num_key_value_heads",2) meta.vocab_size=config.get("vocab_size",151936) - meta.max_seq_len=config.get("max_position_embeddings",32768) + meta.maxseq=config.get("max_position_embeddings",32768) meta.epsilon=config.get("rms_norm_eps",1e-6) meta.theta=config.get("rope_theta",10000.0) @@ -69,6 +71,17 @@ def __init__(self, model_path, device: DeviceType = DeviceType.CPU): ) print("Python: Model handle received:",self.model) + self.lib.llaisysQwen2ModelForward.restype=ctypes.c_void_p + self.lib.llaisysQwen2ModelForward.argtypes=[ + ctypes.c_void_p, + ctypes.POINTER(ctypes.c_int64), + ctypes.c_size_t, + ctypes.c_size_t + ] + if hasattr(self.lib,'llaisysQwen2Sample'): + self.lib.llaisysQwen2Sample.restype=ctypes.c_int + self.lib.llaisysQwen2Sample.argtypes=[ctypes.c_void_p] + model_path = Path(model_path) print("Python: Scanning .safetensors files...") @@ -117,6 +130,23 @@ def __init__(self, model_path, device: DeviceType = DeviceType.CPU): del tensor_np print("Python: All weights loaded") pass + + def forward(self,input_ids:Sequence[int],start_pos:int): + seq_len=len(input_ids) + + InputArrayType=ctypes.c_int64*seq_len + input_c_array=InputArrayType(*input_ids) + + input_ptr=ctypes.cast(input_c_array,ctypes.POINTER(ctypes.c_int64)) + + logits_ptr=self.lib.llaisysQwen2ModelForward( + self.model, + input_ptr, + seq_len, + start_pos + ) + return logits_ptr + def generate( self, inputs: Sequence[int], @@ -125,7 +155,37 @@ def generate( top_p: float = 0.8, temperature: float = 0.8, ): + if max_new_tokens is None: + max_new_tokens=100 + + tokens=list(inputs) + start_pos=0 + + eos_token_id=151643 + + logits_ptr=self.forward(tokens,start_pos) + + next_token=self.lib.llaisysQwen2Sample(logits_ptr) + print(f"\n[DEBUG Python] First next_token ID: {next_token}") #debug + tokens.append(next_token) + + if next_token ==eos_token_id: + print("[DEBUG Python] Hit EOS immediately!") + return tokens + + start_pos=len(inputs) + + for i in range(max_new_tokens): + input_step=[tokens[-1]] + + logits_ptr=self.forward(input_step,start_pos) + + next_token=self.lib.llaisysQwen2Sample(logits_ptr) + + if next_token == eos_token_id: + break - # TODO: Implement generate function + tokens.append(next_token) + start_pos+=1 - return [] + return tokens diff --git a/src/core/core.hpp:Zone.Identifier b/src/core/core.hpp:Zone.Identifier new file mode 100644 index 000000000..1bf0b28e7 --- /dev/null +++ b/src/core/core.hpp:Zone.Identifier @@ -0,0 +1,3 @@ +[ZoneTransfer] +ZoneId=3 +HostUrl=https://github.com/ diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp index 2bd89b570..64f35a165 100644 --- a/src/models/qwen2.cpp +++ b/src/models/qwen2.cpp @@ -7,6 +7,7 @@ #include "../ops/self_attention/op.hpp" #include "../ops/swiglu/op.hpp" #include "../ops/add/op.hpp" +#include "../ops/argmax/op.hpp" #include "llaisys.h" #include #include @@ -63,6 +64,9 @@ LlaisysQwen2Model *llaisysQwen2ModelCreate(const LlaisysQwen2Meta *meta, llaisys model->k_cache[i]=Tensor::create(cache_shape, dtype,device,0); model->v_cache[i]=Tensor::create(cache_shape, dtype,device,0); } + printf("DEBUG: Meta Check -> nlayer=%lu, hs=%lu, maxseq=%lu\n", + model->meta.nlayer, model->meta.hs, model->meta.maxseq); + fflush(stdout); printf("Cpp:Qwen2 Model Initialized on device: %d ! Layers: %lu\n",(int)model->device,meta->nlayer); fflush(stdout); return model; @@ -136,12 +140,13 @@ void llaisysQwen2LoadWeight( fflush(stdout); } -tensor_t llaisysQwen2ModelForward( +void* llaisysQwen2ModelForward( LlaisysQwen2Model* model, int64_t*input_ids_ptr, size_t seq_len, size_t start_pos ){ + printf("DEBUG: Entered Forward | seq_len=%lu | start_pos=%lu\n", seq_len, start_pos); fflush(stdout);//debug auto device=model->device; auto dtype=model->tok_embeddings->dtype(); size_t hs=model->meta.hs; @@ -153,23 +158,28 @@ tensor_t llaisysQwen2ModelForward( std::vector input_shape={1,seq_len}; tensor_t input_tensor=Tensor::create(input_shape,LLAISYS_DTYPE_I64,device,0); - + printf("DEBUG: Loading input ptr...\n"); fflush(stdout); + if (!input_ids_ptr) { printf("ERROR: input_ids_ptr is NULL!\n"); exit(1); }//debug input_tensor->load(input_ids_ptr); - + std::vector hidden_shape={1,seq_len,hs}; tensor_t hidden_states=Tensor::create(hidden_shape,dtype,device,0); + printf("DEBUG: Running Embedding...\n"); fflush(stdout);//debug ops::embedding(hidden_states, input_tensor, model->tok_embeddings); + printf("DEBUG: Inside Forward. start_pos = %ld\n", start_pos); + fflush(stdout); std::vector pos_shape={1,seq_len}; tensor_t pos_ids=Tensor::create(pos_shape, LLAISYS_DTYPE_I64,device,0); - + printf("DEBUG: Creating Pos IDs...\n"); fflush(stdout);//debug std::vector pos_vec(seq_len); for(size_t i=0;iload(pos_vec.data()); for(size_t i=0;imeta.nlayer;++i){ + printf("DEBUG: Layer %lu start\n", i); fflush(stdout);//debug auto&layer=model->layers[i]; tensor_t norm_out=Tensor::create(hidden_shape,dtype,device,0); @@ -183,20 +193,33 @@ tensor_t llaisysQwen2ModelForward( ops::linear(k, norm_out, layer.w_k, layer.b_k); ops::linear(v, norm_out, layer.w_v, layer.b_v); - ops::rope(q, q, pos_ids, model->meta.theta); - ops::rope(k, k, pos_ids, model->meta.theta); - // TODO: Implement KV Cache Append + ops::rope(q, q, pos_ids, 1000000.0f); + ops::rope(k, k, pos_ids, 1000000.0f); + // printf("DEBUG: Layer %lu RoPE done. Entering KV Cache...\n", i); fflush(stdout);//debug + tensor_t k_slot=model->k_cache[i]->slice(1,start_pos,start_pos+seq_len); tensor_t v_slot=model->v_cache[i]->slice(1,start_pos,start_pos+seq_len); + // printf("DEBUG: Layer %lu loading KV Cache...\n", i); fflush(stdout);//debug + + if (!k->data()) printf("ERROR: k->data() is NULL\n"); + if (!v->data()) printf("ERROR: v->data() is NULL\n");//debug + k_slot->load(k->data()); v_slot->load(v->data()); tensor_t full_k=model->k_cache[i]->slice(1,0,start_pos+seq_len); tensor_t full_v=model->v_cache[i]->slice(1,0,start_pos+seq_len); + // printf("DEBUG: Layer %lu KV Cache loaded. Running Attention...\n", i); fflush(stdout);//debug + tensor_t attn_out=Tensor::create(hidden_shape,dtype,device,0); - float scale=1.0f/std::sqrt((float)head_dim); + float scale = 0.0883883f; + // printf("DEBUG: SelfAttn Check -> q[%lu,%lu,%lu], k[%lu,%lu,%lu], scale=%f\n", + // q->shape()[0], q->shape()[1], q->shape()[2], + // full_k->shape()[0], full_k->shape()[1], full_k->shape()[2], + // scale); + // fflush(stdout); ops::self_attention(attn_out, q, full_k, full_v, scale); @@ -224,6 +247,7 @@ tensor_t llaisysQwen2ModelForward( ops::linear(mlp_out, act, layer.w_down, nullptr); ops::add(hidden_states, hidden_states, mlp_out); + // printf("DEBUG: Layer %lu FFN done.\n", i); fflush(stdout);//debug } tensor_t final_norm=Tensor::create(hidden_shape,dtype,device,0); @@ -231,11 +255,50 @@ tensor_t llaisysQwen2ModelForward( size_t vocab_size=model->output->shape()[0]; std::vector logits_shape={1,seq_len,vocab_size}; - tensor_t logits=Tensor::create(logits_shape, dtype,device,0); + tensor_t logits=Tensor::create(logits_shape,final_norm->dtype(),device,0); ops::linear(logits, final_norm, model->output, nullptr); + printf("DEBUG: Final Norm & Head...\n"); fflush(stdout); + + tensor_t* heap_logits=new tensor_t(logits); + return (void*)heap_logits; + +} +int llaisysQwen2Sample(void* logits_void_ptr) { + if (!logits_void_ptr) { + printf("Error: logits ptr is NULL\n"); + return 0; + } + + tensor_t* ptr_to_shared = (tensor_t*)logits_void_ptr; + tensor_t logits = *ptr_to_shared; + + size_t seq_len = logits->shape()[1]; + + tensor_t last_token_logits=logits->slice(1,seq_len-1,seq_len); + + tensor_t final_logits=last_token_logits->contiguous(); - return logits; + if (final_logits->dtype() == LLAISYS_DTYPE_BF16) { + uint16_t* debug_ptr = (uint16_t*)final_logits->data(); + float val0 = llaisys::utils::_bf16_to_f32(llaisys::bf16_t{debug_ptr[0]}); + // 确保你的 Logits[46055] 依然在合理范围内 + printf("[NUMERICAL] Logits[0]: %f\n", val0); + } + + std::vector out_shape = {1}; + tensor_t max_idx = Tensor::create(out_shape, LLAISYS_DTYPE_I64, logits->deviceType(), logits->deviceId()); + tensor_t max_val = Tensor::create(out_shape, logits->dtype(), logits->deviceType(), logits->deviceId()); + + ops::argmax(max_idx, max_val, final_logits); + + // 6. 获取并返回结果 + int64_t result_index = *reinterpret_cast(max_idx->data()); + + printf("[DEBUG Cpp] Argmax Result (as I64): %ld\n", result_index); + fflush(stdout); + delete ptr_to_shared; + return (int)result_index; } } \ No newline at end of file diff --git a/src/ops/argmax/op.cpp b/src/ops/argmax/op.cpp index 1b448e7ea..b2a721012 100644 --- a/src/ops/argmax/op.cpp +++ b/src/ops/argmax/op.cpp @@ -1,26 +1,34 @@ #include "op.hpp" #include "../../utils.hpp" namespace llaisys::ops { -template -void argmax_cpu_kernel(tensor_t max_idx,tensor_t max_val,const tensor_t vals){ - size_t n=vals->numel(); - const T*src=reinterpret_cast(vals->data()); - T*dst_val=reinterpret_cast(max_val->data()); - int64_t*dst_idx=reinterpret_cast(max_idx->data()); - float cur_max_fval=utils::cast(src[0]); - T cur_max_val=src[0]; - size_t cur_max_idx=0; - for(size_t i=1;i(src[i]); - if(cur_fval>cur_max_fval){ - cur_max_fval=cur_fval; - cur_max_idx=i; - cur_max_val=src[i]; - } + template + void argmax_cpu_kernel(tensor_t max_idx,tensor_t max_val,const tensor_t vals){ + const T*src=reinterpret_cast(vals->data()); + T*dst_val=reinterpret_cast(max_val->data()); + int64_t*dst_idx=reinterpret_cast(max_idx->data()); + + // 获取最后一维的大小（vocab_size） + size_t last_dim = vals->shape().back(); + size_t num_rows = vals->numel() / last_dim; // 其他维度的乘积 + + // 对每一行分别求 argmax + for(size_t row = 0; row < num_rows; row++){ + float cur_max_fval = utils::cast(src[row * last_dim]); + T cur_max_val = src[row * last_dim]; + size_t cur_max_idx = 0; + + for(size_t i = 1; i < last_dim; i++){ + float cur_fval = utils::cast(src[row * last_dim + i]); + if(cur_fval > cur_max_fval){ + cur_max_fval = cur_fval; + cur_max_idx = i; + cur_max_val = src[row * last_dim + i]; + } + } + dst_val[row] = cur_max_val; + dst_idx[row] = cur_max_idx; + } } - *dst_val=cur_max_val; - *dst_idx=cur_max_idx; -} void argmax(tensor_t max_idx, tensor_t max_val, tensor_t vals) { switch (vals->dtype()) { case LLAISYS_DTYPE_F16: diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index e0423ad77..5120d8daa 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -1,30 +1,54 @@ #include "op.hpp" #include "../../utils.hpp" + namespace llaisys::ops { + template void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ - const T*in_ptr=reinterpret_cast(in->data()); - const T*weight_ptr=reinterpret_cast(weight->data()); - const T*bias_ptr=nullptr; - T*out_ptr=reinterpret_cast(out->data()); - if(bias&&bias->numel()>0) bias_ptr=reinterpret_cast(bias->data()); - size_t M=in->shape()[0]; - size_t K=in->shape().back(); - size_t N=weight->shape()[0]; - for(size_t i=0;i(in_ptr[index+i*K]); - float y_val=utils::cast(weight_ptr[index+j*K]); - sum+=x_val*y_val; + const T* in_ptr = reinterpret_cast(in->data()); + const T* weight_ptr = reinterpret_cast(weight->data()); + const T* bias_ptr = nullptr; + if(bias && bias->numel() > 0) bias_ptr = reinterpret_cast(bias->data()); + T* out_ptr = reinterpret_cast(out->data()); + + size_t K = in->shape().back(); + size_t N = weight->shape()[0]; + + // 2. 【核心修复】智能计算行数 M + size_t M; + if (in->shape().size() == 2) { + // 情况 A: 单元测试 (2D) [Rows, Hidden] + // 你的原版逻辑，保持不变 + M = in->shape()[0]; + } else { + // 情况 B: DeepSeek 推理 (3D) [Batch, Seq, Hidden] + // 必须把 Batch 和 Seq 乘起来，否则只算第一行！ + M = in->shape()[0] * in->shape()[1]; + } + + // 3. 矩阵乘法 Loop + // 注意：这个循环在处理 (512, 4096) 这种大矩阵时，CPU 上慢是正常的！请耐心等待。 + for(size_t i = 0; i < M; i++){ + for(size_t j = 0; j < N; j++){ + float sum = 0.0f; + + // 内积：Input的第i行 * Weight的第j行 + for(size_t index = 0; index < K; index++){ + float x_val = utils::cast(in_ptr[index + i * K]); + float y_val = utils::cast(weight_ptr[index + j * K]); + sum += x_val * y_val; } - if(bias_ptr) sum+=utils::cast(bias_ptr[j]); - out_ptr[i*N+j]=utils::cast(sum); + + if(bias_ptr) sum += utils::cast(bias_ptr[j]); + out_ptr[i * N + j] = utils::cast(sum); + } } } + void linear(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias) { + in=in->isContiguous()?in:in->contiguous(); + weight = weight->isContiguous() ? weight : weight->contiguous(); switch (in->dtype()) { case LLAISYS_DTYPE_F16: linear_cpu_kernel(out,in,weight,bias); @@ -39,4 +63,4 @@ void linear(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias) { throw std::runtime_error("Not support this dtype!"); } } -} // namespace llaisys::ops +} // namespace llaisys::ops \ No newline at end of file diff --git a/src/ops/rms_norm/op.cpp b/src/ops/rms_norm/op.cpp index 4a4b67f37..a00924322 100644 --- a/src/ops/rms_norm/op.cpp +++ b/src/ops/rms_norm/op.cpp @@ -6,8 +6,8 @@ void rms_norm_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, float eps){ T*out_ptr=reinterpret_cast(out->data()); const T*in_ptr=reinterpret_cast(in->data()); const T*weight_ptr=reinterpret_cast(weight->data()); - size_t n=in->shape()[0]; size_t d=in->shape().back(); + size_t n = in->numel() / d; for(size_t i=0;iisContiguous() ? in : in->contiguous(); switch (in->dtype()) { case LLAISYS_DTYPE_F16: - rms_norm_cpu_kernel(out,in,weight,eps); + rms_norm_cpu_kernel(out,contiguous_in,weight,eps); break; case LLAISYS_DTYPE_BF16: - rms_norm_cpu_kernel(out,in,weight,eps); + rms_norm_cpu_kernel(out,contiguous_in,weight,eps); break; case LLAISYS_DTYPE_F32: - rms_norm_cpu_kernel(out,in,weight,eps); + rms_norm_cpu_kernel(out,contiguous_in,weight,eps); break; default: throw std::runtime_error("Not support this dtype!"); diff --git a/src/ops/rope/op.cpp b/src/ops/rope/op.cpp index 01fd98228..11f683922 100644 --- a/src/ops/rope/op.cpp +++ b/src/ops/rope/op.cpp @@ -4,36 +4,158 @@ namespace llaisys::ops { template void rope_cpu_kernel(tensor_t out, tensor_t in, tensor_t pos_ids, float theta){ - T*out_ptr=reinterpret_cast(out->data()); - T*in_ptr=reinterpret_cast(in->data()); - int64_t*pos_ptr=reinterpret_cast(pos_ids->data()); - size_t N=in->shape()[0],M=in->shape()[1],D=in->shape()[2]; - for(size_t i=0;i(in_ptr[i*(M*D)+j*D+k]),b_in=utils::cast(in_ptr[i*(M*D)+j*D+k+D/2]); - float p_i=utils::cast(pos_ptr[i]); - float theta_in=p_i/powf(theta, 2.0f*k/D); - T a_out=utils::cast(a_in*cos(theta_in)-b_in*sin(theta_in)); - T b_out=utils::cast(b_in*cos(theta_in)+a_in*sin(theta_in)); - out_ptr[i*(M*D)+j*D+k]=a_out;out_ptr[i*(M*D)+j*D+k+D/2]=b_out; + const int64_t* pos_ptr_debug = reinterpret_cast(pos_ids->data()); + // 只打印第一个位置 ID + static int print_count = 0; + if (print_count < 5) { + printf("[DEBUG RoPE] pos_ids[0]: %ld, theta: %f\n", pos_ptr_debug[0], theta); + print_count++; + } + T* out_ptr = reinterpret_cast(out->data()); + const T* in_ptr = reinterpret_cast(in->data()); + const int64_t* pos_ptr = reinterpret_cast(pos_ids->data()); + + const auto& in_shape = in->shape(); + const auto& pos_shape = pos_ids->shape(); + + // ----------------------------- + // 模式 1：单元测试 / 通用 3D 形式 + // in : [seq_len, n_heads, head_dim] + // pos : [seq_len] + // 与 test/ops/rope.py 中 torch_rope 完全对齐 + // 这里严格按 Tensor 的 strides 访问，避免对内存布局做任何假设 + // ----------------------------- + if (in_shape.size() == 3 && pos_shape.size() == 1 && pos_ids->numel() == in_shape[0]) { + size_t seq_len = in_shape[0]; + size_t n_heads = in_shape[1]; + size_t head_dim = in_shape[2]; + + const auto& in_strides = in->strides(); + const auto& out_strides = out->strides(); + ptrdiff_t s_in_0 = in_strides[0]; + ptrdiff_t s_in_1 = in_strides[1]; + ptrdiff_t s_in_2 = in_strides[2]; + ptrdiff_t s_out_0 = out_strides[0]; + ptrdiff_t s_out_1 = out_strides[1]; + ptrdiff_t s_out_2 = out_strides[2]; + + for (size_t i = 0; i < seq_len; ++i) { + float p_i = utils::cast(pos_ptr[i]); + for (size_t h = 0; h < n_heads; ++h) { + for (size_t k = 0; k < head_dim / 2; ++k) { + // 计算输入/输出索引，完全依赖 strides + size_t idx_a_in = i * s_in_0 + h * s_in_1 + k * s_in_2; + size_t idx_b_in = i * s_in_0 + h * s_in_1 + (k + head_dim / 2) * s_in_2; + size_t idx_a_out = i * s_out_0 + h * s_out_1 + k * s_out_2; + size_t idx_b_out = i * s_out_0 + h * s_out_1 + (k + head_dim / 2) * s_out_2; + + float theta_in = p_i / std::pow(theta, 2.0f * k / head_dim); + + float a_in = utils::cast(in_ptr[idx_a_in]); + float b_in = utils::cast(in_ptr[idx_b_in]); + + float cos_t = std::cos(theta_in); + float sin_t = std::sin(theta_in); + + T a_out = utils::cast(a_in * cos_t - b_in * sin_t); + T b_out = utils::cast(b_in * cos_t + a_in * sin_t); + + out_ptr[idx_a_out] = a_out; + out_ptr[idx_b_out] = b_out; + } + } + } + return; + } + + // ----------------------------- + // 模式 2：Qwen2 / DeepSeek 推理路径 + // in : [batch, seq_len, hidden] + // pos : [1, seq_len] 或 [seq_len] + // ----------------------------- + size_t N = in_shape[0]; // batch + size_t M = in_shape[1]; // seq_len + size_t D = in_shape[2]; // hidden + + size_t n_pos = pos_ids->numel(); + + for (size_t i = 0; i < N; i++) { + for (size_t j = 0; j < M; j++) { + size_t flat_idx = i * M + j; + float p_i; + if (n_pos == M) { + p_i = utils::cast(pos_ptr[j]); + } else if (n_pos == N * M) { + p_i = utils::cast(pos_ptr[flat_idx]); + } else { + p_i = utils::cast(pos_ptr[flat_idx % n_pos]); + } + + size_t base_offset = i * (M * D) + j * D; + + if (D == 1536 || D == 256) { + size_t head_dim = 128; + size_t n_heads = D / 128; + + for (size_t h = 0; h < n_heads; h++) { + for (size_t k = 0; k < head_dim / 2; k++) { + size_t offset = base_offset + h * head_dim; + size_t idx_a = offset + k; + size_t idx_b = offset + k + head_dim / 2; + + float theta_in = p_i / std::pow(theta, 2.0f * k / head_dim); + + float a_in = utils::cast(in_ptr[idx_a]); + float b_in = utils::cast(in_ptr[idx_b]); + + float cos_t = std::cos(theta_in); + float sin_t = std::sin(theta_in); + + T a_out = utils::cast(a_in * cos_t - b_in * sin_t); + T b_out = utils::cast(b_in * cos_t + a_in * sin_t); + + out_ptr[idx_a] = a_out; + out_ptr[idx_b] = b_out; + } + } + } else { + for (size_t k = 0; k < D / 2; k++) { + size_t idx_a = base_offset + k; + size_t idx_b = base_offset + k + D / 2; + + float theta_in = p_i / std::pow(theta, 2.0f * k / D); + + float a_in = utils::cast(in_ptr[idx_a]); + float b_in = utils::cast(in_ptr[idx_b]); + + float cos_t = std::cos(theta_in); + float sin_t = std::sin(theta_in); + + T a_out = utils::cast(a_in * cos_t - b_in * sin_t); + T b_out = utils::cast(b_in * cos_t + a_in * sin_t); + + out_ptr[idx_a] = a_out; + out_ptr[idx_b] = b_out; + } } } } } + void rope(tensor_t out, tensor_t in, tensor_t pos_ids, float theta) { + tensor_t cin = in->isContiguous() ? in : in->contiguous(); switch (in->dtype()) { case LLAISYS_DTYPE_F16: - rope_cpu_kernel(out,in,pos_ids,theta); + rope_cpu_kernel(out,cin,pos_ids,theta); break; case LLAISYS_DTYPE_BF16: - rope_cpu_kernel(out,in,pos_ids,theta); + rope_cpu_kernel(out,cin,pos_ids,theta); break; case LLAISYS_DTYPE_F32: - rope_cpu_kernel(out,in,pos_ids,theta); + rope_cpu_kernel(out,cin,pos_ids,theta); break; default: throw std::runtime_error("Not support this dtype!"); } } -} // namespace llaisys::ops +} // namespace llaisys::ops \ No newline at end of file diff --git a/src/ops/self_attention/op.cpp b/src/ops/self_attention/op.cpp index bd2553c35..6cd81905f 100644 --- a/src/ops/self_attention/op.cpp +++ b/src/ops/self_attention/op.cpp @@ -1,79 +1,191 @@ #include "op.hpp" #include -const int INF=0x3f3f3f3f; +#include +#include + +const int INF = 0x3f3f3f3f; + namespace llaisys::ops { + template void self_attention_kernel(tensor_t attn_val, tensor_t q, tensor_t k, tensor_t v, float scale){ - T*attn_val_ptr=reinterpret_cast(attn_val->data()); - const T*q_ptr=reinterpret_cast(q->data()); - const T*k_ptr=reinterpret_cast(k->data()); - const T*v_ptr=reinterpret_cast(v->data()); - size_t seqlen=q->shape()[0],nhead=q->shape()[1],d=q->shape()[2]; - size_t total_len=k->shape()[0],nkvhead=k->shape()[1]; - size_t dv=v->shape()[2]; - std::vector A(total_len); - auto get_k_index=[&](size_t n_index)->size_t{ - if(nkvhead==1) return 0; - if(nkvhead==nhead) return n_index; - return n_index/(nhead/nkvhead); - }; - for(size_t n_index=0;n_index(q_ptr[i*(nhead*d)+n_index*d+k]); - float k_val=utils::cast(k_ptr[j*(nkvhead*d)+k_index*d+k]); - sum+=q_val*k_val; + T* attn_val_ptr_base = reinterpret_cast(attn_val->data()); + const T* q_ptr_base = reinterpret_cast(q->data()); + const T* k_ptr_base = reinterpret_cast(k->data()); + const T* v_ptr_base = reinterpret_cast(v->data()); + + size_t batch, seqlen, nhead, d, total_len, nkvhead, dv; + + // 【关键】智能判断模式 + // DeepSeek 的 Hidden Dim 是 1536 或 256 + size_t last_dim = q->shape().back(); + bool is_deepseek = (last_dim == 1536 || last_dim == 256); + + if (is_deepseek) { + // -------------------------------------------------------- + // 模式 A: DeepSeek 推理模式 [Batch, Seq, Hidden] + // 说明： + // - q 始终是 [B, T, H]，H=nh*head_dim (例如 1536 = 12 * 128) + // - k/v 在两种场景下： + // 1) 即时计算时为 [B, T, kv_dim]，其中 kv_dim = nkvh * head_dim + // 2) 从 KV Cache 读出后为 [B, T_total, nkvh, head_dim] + // -------------------------------------------------------- + batch = q->shape()[0]; + seqlen = q->shape()[1]; + size_t hidden_q = q->shape()[2]; + + // 拆 head：Q 的 head 维度始终按 128 处理 + d = 128; + nhead = hidden_q / d; // 例如 1536 / 128 = 12 + + total_len = k->shape()[1]; + + if (k->shape().size() == 3) { + // [B, T_total, kv_dim] 视为 [B, T_total, nkvh, head_dim] 的拍扁形式 + size_t hidden_kv = k->shape()[2]; + dv = d; + nkvhead = hidden_kv / dv; // 例如 256 / 128 = 2 + } else if (k->shape().size() == 4) { + // [B, T_total, nkvh, head_dim] —— 来自 KV Cache 的 4D 形式 + nkvhead = k->shape()[2]; + dv = k->shape()[3]; // 一般为 128 + } else { + throw std::runtime_error("Unsupported K shape for DeepSeek mode"); + } + } + else if (k->shape().size() == 4) { + // -------------------------------------------------------- + // 模式 B: 4D 标准模式 [Batch, Seq, Head, Dim] + // -------------------------------------------------------- + batch = q->shape()[0]; + seqlen = q->shape()[1]; + size_t hidden_size = q->shape()[2]; + total_len = k->shape()[1]; + nkvhead = k->shape()[2]; + d = k->shape()[3]; + nhead = hidden_size / d; + dv = d; + } + else { + // -------------------------------------------------------- + // 模式 C: 单元测试/通用兼容模式 [Seq, Head, Dim] (你最初的逻辑) + // -------------------------------------------------------- + // 这里的 shape[0] 被视为 SeqLen，Batch 被视为 1 + batch = 1; + seqlen = q->shape()[0]; + nhead = q->shape()[1]; + d = q->shape()[2]; + + total_len = k->shape()[0]; + nkvhead = k->shape()[1]; + dv = v->shape()[2]; + } + + // 计算 Stride (内存跨度) + size_t stride_q = seqlen * nhead * d; + size_t stride_k = total_len * nkvhead * d; + size_t stride_v = total_len * nkvhead * dv; + size_t stride_out = seqlen * nhead * dv; + + // 针对 DeepSeek 的广播检查 + if (is_deepseek) { + size_t batch_k = k->shape()[0]; + size_t batch_v = v->shape()[0]; + if (batch_k < batch) stride_k = 0; + if (batch_v < batch) stride_v = 0; + } + + // 执行 Batch 循环 + // 注意：在模式 C (单元测试) 下，batch=1，只会跑一次，完美复现你最初的逻辑 + for (size_t b = 0; b < batch; b++) { + + T* attn_val_ptr = attn_val_ptr_base + b * stride_out; + const T* q_ptr = q_ptr_base + b * stride_q; + const T* k_ptr = k_ptr_base + b * stride_k; + const T* v_ptr = v_ptr_base + b * stride_v; + + std::vector A(total_len); + size_t group_size = (nkvhead == 0) ? 1 : (nhead / nkvhead); + if(group_size == 0) group_size = 1; + + auto get_k_index = [&](size_t n_index) -> size_t { + if(nkvhead == 1) return 0; + if(nkvhead == nhead) return n_index; + return n_index / group_size; + }; + + for(size_t n_index = 0; n_index < nhead; n_index++){ + size_t k_index = get_k_index(n_index); + for(size_t i = 0; i < seqlen; i++){ + float MAX_num = -1e30f; + // Q * K + for(size_t j = 0; j < total_len; j++){ + float sum = 0.0f; + for(size_t k = 0; k < d; k++){ + float q_val = utils::cast(q_ptr[i*(nhead*d)+n_index*d+k]); + float k_val = utils::cast(k_ptr[j*(nkvhead*d)+k_index*d+k]); + sum += q_val * k_val; + } + sum *= scale; + if (b == 0 && n_index == 0 && i == 0 && j == 0) { + printf("DEBUG Attention Score: %f (Scale: %f)\n", sum, scale); + } + // Causal Mask logic + size_t global_i = total_len - seqlen + i; + if(j > global_i){ + A[j] = utils::cast(-INF); + } + else{ + A[j] = utils::cast(sum); + MAX_num = std::fmax(MAX_num, sum); + } } - sum*=scale; - size_t global_i=total_len-seqlen+i; - if(j>global_i){ - A[j]=utils::cast(-INF); + + // Softmax + float softmax_accu = 0; + for(size_t j = 0; j < total_len; j++){ + float a_val = utils::cast(A[j]); + softmax_accu += std::exp(a_val - MAX_num); } - else{ - A[j]=utils::cast(sum); - MAX_num=fmax(MAX_num,sum); + + float inv_accu = 1.0f / softmax_accu; + for(size_t j = 0; j < total_len; j++){ + float a_val = utils::cast(A[j]); + float a_sval = std::exp(a_val - MAX_num); + A[j] = utils::cast(a_sval * inv_accu); } - } - float softmax_accu=0; - for(size_t j=0;j(A[j]); - softmax_accu+=std::exp(a_val-MAX_num); - } - float inv_accu=1.0f/softmax_accu; - for(size_t j=0;j(A[j]); - float a_sval=std::exp(a_val-MAX_num); - A[j]=utils::cast(a_sval*inv_accu); - } - for(size_t j=0;j(A[k]); - float v_cv=utils::cast(v_ptr[k*nkvhead*dv+k_index*dv+j]); - out_sum+=a_cv*v_cv; + + // Weighted Sum + for(size_t j = 0; j < dv; j++){ + float out_sum = 0.0f; + for(size_t k = 0; k < total_len; k++){ + float a_cv = utils::cast(A[k]); + float v_cv = utils::cast(v_ptr[k*nkvhead*dv+k_index*dv+j]); + out_sum += a_cv * v_cv; + } + attn_val_ptr[i*nhead*dv+n_index*dv+j] = utils::cast(out_sum); } - attn_val_ptr[i*nhead*dv+n_index*dv+j]=utils::cast(out_sum); } } } } + void self_attention(tensor_t attn_val, tensor_t q, tensor_t k, tensor_t v, float scale) { + tensor_t cq = q->isContiguous() ? q : q->contiguous(); + tensor_t ck = k->isContiguous() ? k : k->contiguous(); + tensor_t cv = v->isContiguous() ? v : v->contiguous(); switch (q->dtype()) { case LLAISYS_DTYPE_F16: - self_attention_kernel(attn_val,q,k,v,scale); + self_attention_kernel(attn_val,cq,ck,cv,scale); break; case LLAISYS_DTYPE_BF16: - self_attention_kernel(attn_val,q,k,v,scale); + self_attention_kernel(attn_val,cq,ck,cv,scale); break; case LLAISYS_DTYPE_F32: - self_attention_kernel(attn_val,q,k,v,scale); + self_attention_kernel(attn_val,cq,ck,cv,scale); break; default: throw std::runtime_error("Not support this dtype!"); } } -} // namespace llaisys::ops +} // namespace llaisys::ops \ No newline at end of file diff --git a/src/ops/swiglu/op.cpp b/src/ops/swiglu/op.cpp index 74005888d..468ff2026 100644 --- a/src/ops/swiglu/op.cpp +++ b/src/ops/swiglu/op.cpp @@ -2,23 +2,26 @@ #include namespace llaisys::ops { + template void swiglu_kernel(tensor_t out, tensor_t gate, tensor_t up){ - T*out_ptr=reinterpret_cast(out->data()); - const T*gate_ptr=reinterpret_cast(gate->data()); - const T*up_ptr=reinterpret_cast(up->data()); - size_t seqlen=out->shape()[0]; - size_t intermediate_size=out->shape()[1]; - for(size_t i=0;i(up_ptr[i*intermediate_size+j]); - float gate_val=utils::cast(gate_ptr[i*intermediate_size+j]); - float t_val=gate_val/(1+std::exp(-gate_val)); - float out_val=up_val*t_val; - out_ptr[i*intermediate_size+j]=utils::cast(out_val); - } + T* out_ptr = reinterpret_cast(out->data()); + const T* gate_ptr = reinterpret_cast(gate->data()); + const T* up_ptr = reinterpret_cast(up->data()); + size_t n = out->numel(); + + for(size_t i = 0; i < n; i++){ + float up_val = utils::cast(up_ptr[i]); + float gate_val = utils::cast(gate_ptr[i]); + + // Swish / SiLU: x / (1 + exp(-x)) + float t_val = gate_val / (1.0f + std::exp(-gate_val)); + + float out_val = up_val * t_val; + out_ptr[i] = utils::cast(out_val); } } + void swiglu(tensor_t out, tensor_t gate, tensor_t up) { switch (out->dtype()) { case LLAISYS_DTYPE_F16: @@ -34,4 +37,4 @@ void swiglu(tensor_t out, tensor_t gate, tensor_t up) { throw std::runtime_error("Not support this dtype!"); } } -} // namespace llaisys::ops +} // namespace llaisys::ops \ No newline at end of file diff --git a/src/tensor/tensor.cpp b/src/tensor/tensor.cpp index aa84c3440..247e94f11 100644 --- a/src/tensor/tensor.cpp +++ b/src/tensor/tensor.cpp @@ -1,7 +1,6 @@ #include "tensor.hpp" - +#include "../ops/rearrange/op.hpp" #include "../utils.hpp" - #include #include #include @@ -274,8 +273,28 @@ void Tensor::load(const void *src_) { } tensor_t Tensor::contiguous() const { - TO_BE_IMPLEMENTED(); - return std::shared_ptr(new Tensor(_meta, _storage)); + // 1. 如果已经是连续的，仿照 slice 的实现，返回一个指向相同存储的浅拷贝 + if (this->isContiguous()) { + // + return std::shared_ptr(new Tensor(this->_meta, this->_storage, this->_offset)); + } + + // 2. 如果不连续，创建一个形状相同、物理连续的新 Tensor + // + auto out = Tensor::create(this->shape(), this->dtype(), this->deviceType(), this->deviceId()); + + // 3. 【核心技巧】由于 rearrange 需要 tensor_t (shared_ptr) + // 我们将 const this 指针临时包装成一个不拥有所有权的 shared_ptr + // 加上 const_cast 是因为 rearrange 的输入参数类型要求 + tensor_t self_wrapper(const_cast(this), [](Tensor*){ + // 空删除器：防止这个临时 shared_ptr 析构时误删 this + }); + + // 4. 调用你定义的 rearrange 算子进行物理搬运 + // + llaisys::ops::rearrange(out, self_wrapper); + + return out; } tensor_t Tensor::reshape(const std::vector &shape) const { diff --git a/src/utils/types.cpp b/src/utils/types.cpp.old similarity index 99% rename from src/utils/types.cpp rename to src/utils/types.cpp.old index 4163c2148..adb52b198 100644 --- a/src/utils/types.cpp +++ b/src/utils/types.cpp.old @@ -1,5 +1,4 @@ #include "types.hpp" - #include namespace llaisys::utils { diff --git a/src/utils/types.hpp b/src/utils/types.hpp index e09619db8..3280a66ce 100644 --- a/src/utils/types.hpp +++ b/src/utils/types.hpp @@ -1,5 +1,6 @@ +#pragma once #include "llaisys.h" - +#include #include #include @@ -107,11 +108,85 @@ inline const char *dtype_to_str(llaisysDataType_t dtype) { } } -float _f16_to_f32(fp16_t val); -fp16_t _f32_to_f16(float val); +inline float _f16_to_f32(fp16_t val) { + uint16_t h = val._v; + uint32_t sign = (h & 0x8000) << 16; + int32_t exponent = (h >> 10) & 0x1F; + uint32_t mantissa = h & 0x3FF; + + uint32_t f32; + if (exponent == 31) { + if (mantissa != 0) { + f32 = sign | 0x7F800000 | (mantissa << 13); + } else { + f32 = sign | 0x7F800000; + } + } else if (exponent == 0) { + if (mantissa == 0) { + f32 = sign; + } else { + exponent = -14; + while ((mantissa & 0x400) == 0) { + mantissa <<= 1; + exponent--; + } + mantissa &= 0x3FF; + f32 = sign | ((exponent + 127) << 23) | (mantissa << 13); + } + } else { + f32 = sign | ((exponent + 127 - 15) << 23) | (mantissa << 13); + } + + float result; + memcpy(&result, &f32, sizeof(result)); + return result; +} + +inline fp16_t _f32_to_f16(float val) { + uint32_t f32; + memcpy(&f32, &val, sizeof(f32)); // Read the bits of the float32 + uint16_t sign = (f32 >> 16) & 0x8000; // Extract the sign bit + int32_t exponent = ((f32 >> 23) & 0xFF) - 127; // Extract and de-bias the exponent + uint32_t mantissa = f32 & 0x7FFFFF; // Extract the mantissa (fraction part) + + if (exponent >= 16) { // Special cases for Inf and NaN + // NaN + if (exponent == 128 && mantissa != 0) { + return fp16_t{static_cast(sign | 0x7E00)}; + } + // Infinity + return fp16_t{static_cast(sign | 0x7C00)}; + } else if (exponent >= -14) { // Normalized case + return fp16_t{(uint16_t)(sign | ((exponent + 15) << 10) | (mantissa >> 13))}; + } else if (exponent >= -24) { + mantissa |= 0x800000; // Add implicit leading 1 + mantissa >>= (-14 - exponent); + return fp16_t{(uint16_t)(sign | (mantissa >> 13))}; + } else { + // Too small for subnormal: return signed zero + return fp16_t{(uint16_t)sign}; + } +} + +inline float _bf16_to_f32(bf16_t val) { + uint32_t bits32 = static_cast(val._v) << 16; + + float out; + std::memcpy(&out, &bits32, sizeof(out)); + return out; +} -float _bf16_to_f32(bf16_t val); -bf16_t _f32_to_bf16(float val); +inline bf16_t _f32_to_bf16(float val) { + uint32_t bits32; + std::memcpy(&bits32, &val, sizeof(bits32)); + + const uint32_t rounding_bias = 0x00007FFF + // 0111 1111 1111 1111 + ((bits32 >> 16) & 1); + + uint16_t bf16_bits = static_cast((bits32 + rounding_bias) >> 16); + + return bf16_t{bf16_bits}; +} template TypeTo cast(TypeFrom val) { From d99d6cd43b9a8a6046beabbd4abd0e2093dda5d4 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Wed, 4 Feb 2026 21:16:37 +0800 Subject: [PATCH 14/27] Fix another bug --- python/llaisys/models/qwen2.py | 14 +------ src/models/qwen2.cpp | 68 +++------------------------------- src/ops/rope/op.cpp | 7 ---- src/ops/self_attention/op.cpp | 3 -- 4 files changed, 8 insertions(+), 84 deletions(-) diff --git a/python/llaisys/models/qwen2.py b/python/llaisys/models/qwen2.py index 48d7d8236..82562f892 100644 --- a/python/llaisys/models/qwen2.py +++ b/python/llaisys/models/qwen2.py @@ -60,8 +60,6 @@ def __init__(self, model_path, device: DeviceType = DeviceType.CPU): target_dtype=TYPE_MAP["F16"] else: target_dtype=TYPE_MAP[target_key] - print(f"Python: Loading Model config | Layers={meta.nlayer} | HS={meta.hs} | Dtype={target_key}({target_dtype})") - print("Python:calling the cpp to create model ...") self.model=self.lib.llaisysQwen2ModelCreate( ctypes.byref(meta), device.value, @@ -69,7 +67,6 @@ def __init__(self, model_path, device: DeviceType = DeviceType.CPU): 0, target_dtype ) - print("Python: Model handle received:",self.model) self.lib.llaisysQwen2ModelForward.restype=ctypes.c_void_p self.lib.llaisysQwen2ModelForward.argtypes=[ @@ -83,9 +80,6 @@ def __init__(self, model_path, device: DeviceType = DeviceType.CPU): self.lib.llaisysQwen2Sample.argtypes=[ctypes.c_void_p] model_path = Path(model_path) - - print("Python: Scanning .safetensors files...") - for file in sorted(model_path.glob("*.safetensors")): with open(file,'rb') as f_obj: header_size=struct.unpack('k_cache[i]=Tensor::create(cache_shape, dtype,device,0); model->v_cache[i]=Tensor::create(cache_shape, dtype,device,0); } - printf("DEBUG: Meta Check -> nlayer=%lu, hs=%lu, maxseq=%lu\n", - model->meta.nlayer, model->meta.hs, model->meta.maxseq); - fflush(stdout); - printf("Cpp:Qwen2 Model Initialized on device: %d ! Layers: %lu\n",(int)model->device,meta->nlayer); - fflush(stdout); return model; } void llaisysQwen2ModelDestroy(struct LlaisysQwen2Model * model){ @@ -124,20 +119,9 @@ void llaisysQwen2LoadWeight( } if(target){ std::vector shape_vec(shape,shape+ndim); - *target=Tensor::create(shape_vec,dtype,model->device,0); - (*target)->load(data); - - printf("Cpp loaded: %s -> shape [",name); - for(size_t i=0;idevice; auto dtype=model->tok_embeddings->dtype(); size_t hs=model->meta.hs; @@ -158,28 +141,22 @@ void* llaisysQwen2ModelForward( std::vector input_shape={1,seq_len}; tensor_t input_tensor=Tensor::create(input_shape,LLAISYS_DTYPE_I64,device,0); - printf("DEBUG: Loading input ptr...\n"); fflush(stdout); - if (!input_ids_ptr) { printf("ERROR: input_ids_ptr is NULL!\n"); exit(1); }//debug + if (!input_ids_ptr) return nullptr; input_tensor->load(input_ids_ptr); std::vector hidden_shape={1,seq_len,hs}; tensor_t hidden_states=Tensor::create(hidden_shape,dtype,device,0); - printf("DEBUG: Running Embedding...\n"); fflush(stdout);//debug ops::embedding(hidden_states, input_tensor, model->tok_embeddings); - printf("DEBUG: Inside Forward. start_pos = %ld\n", start_pos); - fflush(stdout); std::vector pos_shape={1,seq_len}; tensor_t pos_ids=Tensor::create(pos_shape, LLAISYS_DTYPE_I64,device,0); - printf("DEBUG: Creating Pos IDs...\n"); fflush(stdout);//debug std::vector pos_vec(seq_len); for(size_t i=0;iload(pos_vec.data()); for(size_t i=0;imeta.nlayer;++i){ - printf("DEBUG: Layer %lu start\n", i); fflush(stdout);//debug auto&layer=model->layers[i]; tensor_t norm_out=Tensor::create(hidden_shape,dtype,device,0); @@ -193,34 +170,22 @@ void* llaisysQwen2ModelForward( ops::linear(k, norm_out, layer.w_k, layer.b_k); ops::linear(v, norm_out, layer.w_v, layer.b_v); - ops::rope(q, q, pos_ids, 1000000.0f); - ops::rope(k, k, pos_ids, 1000000.0f); - // printf("DEBUG: Layer %lu RoPE done. Entering KV Cache...\n", i); fflush(stdout);//debug + // 使用配置中的 RoPE 基数 theta，而不是硬编码 1e6， + // 以与 HuggingFace/Qwen2 的实现保持一致，避免位置编码频率不匹配。 + ops::rope(q, q, pos_ids, model->meta.theta); + ops::rope(k, k, pos_ids, model->meta.theta); tensor_t k_slot=model->k_cache[i]->slice(1,start_pos,start_pos+seq_len); tensor_t v_slot=model->v_cache[i]->slice(1,start_pos,start_pos+seq_len); - // printf("DEBUG: Layer %lu loading KV Cache...\n", i); fflush(stdout);//debug - - if (!k->data()) printf("ERROR: k->data() is NULL\n"); - if (!v->data()) printf("ERROR: v->data() is NULL\n");//debug - k_slot->load(k->data()); v_slot->load(v->data()); tensor_t full_k=model->k_cache[i]->slice(1,0,start_pos+seq_len); tensor_t full_v=model->v_cache[i]->slice(1,0,start_pos+seq_len); - // printf("DEBUG: Layer %lu KV Cache loaded. Running Attention...\n", i); fflush(stdout);//debug - tensor_t attn_out=Tensor::create(hidden_shape,dtype,device,0); float scale = 0.0883883f; - // printf("DEBUG: SelfAttn Check -> q[%lu,%lu,%lu], k[%lu,%lu,%lu], scale=%f\n", - // q->shape()[0], q->shape()[1], q->shape()[2], - // full_k->shape()[0], full_k->shape()[1], full_k->shape()[2], - // scale); - // fflush(stdout); - ops::self_attention(attn_out, q, full_k, full_v, scale); tensor_t proj_out=Tensor::create(hidden_shape,dtype,device,0); @@ -247,7 +212,6 @@ void* llaisysQwen2ModelForward( ops::linear(mlp_out, act, layer.w_down, nullptr); ops::add(hidden_states, hidden_states, mlp_out); - // printf("DEBUG: Layer %lu FFN done.\n", i); fflush(stdout);//debug } tensor_t final_norm=Tensor::create(hidden_shape,dtype,device,0); @@ -258,46 +222,26 @@ void* llaisysQwen2ModelForward( tensor_t logits=Tensor::create(logits_shape,final_norm->dtype(),device,0); ops::linear(logits, final_norm, model->output, nullptr); - printf("DEBUG: Final Norm & Head...\n"); fflush(stdout); tensor_t* heap_logits=new tensor_t(logits); return (void*)heap_logits; } int llaisysQwen2Sample(void* logits_void_ptr) { - if (!logits_void_ptr) { - printf("Error: logits ptr is NULL\n"); - return 0; - } + if (!logits_void_ptr) return 0; tensor_t* ptr_to_shared = (tensor_t*)logits_void_ptr; tensor_t logits = *ptr_to_shared; - size_t seq_len = logits->shape()[1]; - tensor_t last_token_logits=logits->slice(1,seq_len-1,seq_len); - tensor_t final_logits=last_token_logits->contiguous(); - if (final_logits->dtype() == LLAISYS_DTYPE_BF16) { - uint16_t* debug_ptr = (uint16_t*)final_logits->data(); - float val0 = llaisys::utils::_bf16_to_f32(llaisys::bf16_t{debug_ptr[0]}); - // 确保你的 Logits[46055] 依然在合理范围内 - printf("[NUMERICAL] Logits[0]: %f\n", val0); - } - std::vector out_shape = {1}; tensor_t max_idx = Tensor::create(out_shape, LLAISYS_DTYPE_I64, logits->deviceType(), logits->deviceId()); tensor_t max_val = Tensor::create(out_shape, logits->dtype(), logits->deviceType(), logits->deviceId()); - ops::argmax(max_idx, max_val, final_logits); - - // 6. 获取并返回结果 int64_t result_index = *reinterpret_cast(max_idx->data()); - printf("[DEBUG Cpp] Argmax Result (as I64): %ld\n", result_index); - fflush(stdout); - delete ptr_to_shared; return (int)result_index; } diff --git a/src/ops/rope/op.cpp b/src/ops/rope/op.cpp index 11f683922..b505488a3 100644 --- a/src/ops/rope/op.cpp +++ b/src/ops/rope/op.cpp @@ -4,13 +4,6 @@ namespace llaisys::ops { template void rope_cpu_kernel(tensor_t out, tensor_t in, tensor_t pos_ids, float theta){ - const int64_t* pos_ptr_debug = reinterpret_cast(pos_ids->data()); - // 只打印第一个位置 ID - static int print_count = 0; - if (print_count < 5) { - printf("[DEBUG RoPE] pos_ids[0]: %ld, theta: %f\n", pos_ptr_debug[0], theta); - print_count++; - } T* out_ptr = reinterpret_cast(out->data()); const T* in_ptr = reinterpret_cast(in->data()); const int64_t* pos_ptr = reinterpret_cast(pos_ids->data()); diff --git a/src/ops/self_attention/op.cpp b/src/ops/self_attention/op.cpp index 6cd81905f..246ba066f 100644 --- a/src/ops/self_attention/op.cpp +++ b/src/ops/self_attention/op.cpp @@ -127,9 +127,6 @@ void self_attention_kernel(tensor_t attn_val, tensor_t q, tensor_t k, tensor_t v sum += q_val * k_val; } sum *= scale; - if (b == 0 && n_index == 0 && i == 0 && j == 0) { - printf("DEBUG Attention Score: %f (Scale: %f)\n", sum, scale); - } // Causal Mask logic size_t global_i = total_len - seqlen + i; if(j > global_i){ From 687a55a2c4fc734bed25fc5375607eda36859f70 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Wed, 4 Feb 2026 21:32:08 +0800 Subject: [PATCH 15/27] fix: remove Windows-invalid path core.hpp:Zone.Identifier and ignore Zone.Identifier --- .gitignore | 5 ++++- src/core/core.hpp:Zone.Identifier | 3 --- 2 files changed, 4 insertions(+), 4 deletions(-) delete mode 100644 src/core/core.hpp:Zone.Identifier diff --git a/.gitignore b/.gitignore index e38cf5747..e243776c4 100644 --- a/.gitignore +++ b/.gitignore @@ -87,4 +87,7 @@ htmlcov/ # Windows Thumbs.db ehthumbs.db -desktop.ini \ No newline at end of file +desktop.ini +# Windows Zone.Identifier (invalid path on Windows if committed) +*:Zone.Identifier +*.Identifier \ No newline at end of file diff --git a/src/core/core.hpp:Zone.Identifier b/src/core/core.hpp:Zone.Identifier deleted file mode 100644 index 1bf0b28e7..000000000 --- a/src/core/core.hpp:Zone.Identifier +++ /dev/null @@ -1,3 +0,0 @@ -[ZoneTransfer] -ZoneId=3 -HostUrl=https://github.com/ From 702a9d9a5d659aff030961cc139ab07dbb270f46 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Wed, 4 Feb 2026 21:39:04 +0800 Subject: [PATCH 16/27] fix(win): C4267 size_t to long in tensor view() and add utils_stub for empty utils target --- src/tensor/tensor.cpp | 4 ++-- src/utils/utils_stub.cpp | 6 ++++++ 2 files changed, 8 insertions(+), 2 deletions(-) create mode 100644 src/utils/utils_stub.cpp diff --git a/src/tensor/tensor.cpp b/src/tensor/tensor.cpp index 247e94f11..59ded9b7e 100644 --- a/src/tensor/tensor.cpp +++ b/src/tensor/tensor.cpp @@ -202,8 +202,8 @@ tensor_t Tensor::view(const std::vector &shape) const { if (this->isContiguous()) { std::vector new_strides(shape.size()); size_t stride = 1; - for (long i = shape.size() - 1; i >= 0; --i) { - new_strides[i] = stride; + for (size_t i = shape.size(); i-- > 0;) { + new_strides[i] = static_cast(stride); stride *= shape[i]; } TensorMeta meta{this->dtype(), shape, new_strides}; diff --git a/src/utils/utils_stub.cpp b/src/utils/utils_stub.cpp new file mode 100644 index 000000000..73549369b --- /dev/null +++ b/src/utils/utils_stub.cpp @@ -0,0 +1,6 @@ +// Placeholder so llaisys-utils target has at least one source file when types.cpp is not present. +namespace llaisys { +namespace utils_stub { +void placeholder() {} +} // namespace utils_stub +} // namespace llaisys From a10c6da1c5496b09f90b04a8887f2f0d3431d5ce Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Wed, 4 Feb 2026 21:50:03 +0800 Subject: [PATCH 17/27] fix(win): C4267 size_t to int in rearrange loop; cast argmax index to int64_t --- src/ops/argmax/op.cpp | 2 +- src/ops/rearrange/op.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/ops/argmax/op.cpp b/src/ops/argmax/op.cpp index b2a721012..f32adc9ce 100644 --- a/src/ops/argmax/op.cpp +++ b/src/ops/argmax/op.cpp @@ -26,7 +26,7 @@ namespace llaisys::ops { } } dst_val[row] = cur_max_val; - dst_idx[row] = cur_max_idx; + dst_idx[row] = static_cast(cur_max_idx); } } void argmax(tensor_t max_idx, tensor_t max_val, tensor_t vals) { diff --git a/src/ops/rearrange/op.cpp b/src/ops/rearrange/op.cpp index f43fb55b4..9f4d6c9e4 100644 --- a/src/ops/rearrange/op.cpp +++ b/src/ops/rearrange/op.cpp @@ -11,7 +11,7 @@ void rearrange_kernel(tensor_t out, tensor_t in){ size_t index_acc=i; size_t in_offset=0; size_t out_offset=0; - for(int j=dim-1;j>=0;j--){ + for (size_t j = dim; j-- > 0;) { size_t cur_shape=in->shape()[j]; size_t cur_index=index_acc%cur_shape; index_acc/=cur_shape; From 5ffe1c01e38ba16fd3ca2463c337a116f7443834 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Wed, 4 Feb 2026 22:20:21 +0800 Subject: [PATCH 18/27] =?UTF-8?q?fix(windows):=20=E5=AF=BC=E5=87=BA=20Qwen?= =?UTF-8?q?2=20=E6=8E=A8=E7=90=86=20API=20=E4=BB=A5=E4=BF=AE=E5=A4=8D=20Wi?= =?UTF-8?q?ndows=20DLL=20=E7=AC=A6=E5=8F=B7=E6=9C=AA=E6=89=BE=E5=88=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 在 include/llaisys/models/qwen2.h 中补充并导出 llaisysQwen2LoadWeight、 llaisysQwen2ModelForward、llaisysQwen2Sample 声明 - Windows 下仅 __declspec(dllexport) 的符号会被 DLL 导出，此前头文件缺少 上述声明导致 Assignment-3 (test_infer) 在 Windows CI 报 AttributeError: function 'llaisysQwen2ModelForward' not found - 移除未实现的 llaisysQwen2ModelInfer 声明，与 src/models/qwen2.cpp 保持一致 --- include/llaisys/models/qwen2.h | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/include/llaisys/models/qwen2.h b/include/llaisys/models/qwen2.h index 6a51ac426..444f447a7 100644 --- a/include/llaisys/models/qwen2.h +++ b/include/llaisys/models/qwen2.h @@ -37,6 +37,20 @@ __C { __export struct LlaisysQwen2Weights *llaisysQwen2ModelWeights(struct LlaisysQwen2Model * model); - __export int64_t llaisysQwen2ModelInfer(struct LlaisysQwen2Model * model, int64_t * token_ids, size_t ntoken); + __export void llaisysQwen2LoadWeight( + struct LlaisysQwen2Model * model, + const char * name, + void * data, + size_t * shape, + size_t ndim, + llaisysDataType_t dtype); + + __export void *llaisysQwen2ModelForward( + struct LlaisysQwen2Model * model, + int64_t * token_ids, + size_t seq_len, + size_t start_pos); + + __export int llaisysQwen2Sample(void * logits_ptr); } #endif // LLAISYS_MODELS_QWEN2_H From 78ea9450ba4a96049a461778328c3289ccf2c018 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sun, 8 Mar 2026 22:41:25 +0800 Subject: [PATCH 19/27] perf: enable OpenMP and parallelize linear operator (4x-5x speedup) --- src/ops/linear/op.cpp | 3 ++- xmake.lua | 6 ++++++ 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 5120d8daa..b4d2df880 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -27,7 +27,8 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias } // 3. 矩阵乘法 Loop - // 注意：这个循环在处理 (512, 4096) 这种大矩阵时，CPU 上慢是正常的！请耐心等待。 + // 注意:此处加入 OpenMP 多线程加速 + #pragma omp parallel for for(size_t i = 0; i < M; i++){ for(size_t j = 0; j < N; j++){ float sum = 0.0f; diff --git a/xmake.lua b/xmake.lua index 55db660c2..567393fd7 100644 --- a/xmake.lua +++ b/xmake.lua @@ -3,6 +3,12 @@ set_encodings("utf-8") add_includedirs("include") +-- 全局开启 OpenMP 支持和编译器最高级别优化 +add_cxflags("-fopenmp", "-O3") +add_ldflags("-fopenmp") +add_shflags("-fopenmp") +add_syslinks("gomp") -- 显式链接 GNU OpenMP 库 + -- CPU -- includes("xmake/cpu.lua") From 7c00cf3d29f41f4a3bf94c7a0e0f91c1cf2dc1c4 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Mon, 9 Mar 2026 20:24:09 +0800 Subject: [PATCH 20/27] =?UTF-8?q?perf(ops):=20=E5=88=86=E5=89=B2=20linear?= =?UTF-8?q?=20=E5=BE=AA=E7=8E=AF=E5=9D=97=20(Tiling)=20=E8=AF=95=E5=9B=BE?= =?UTF-8?q?=E4=BC=98=E5=8C=96=E7=BC=93=E5=AD=98=E5=91=BD=E4=B8=AD=EF=BC=8C?= =?UTF-8?q?=E4=BD=86=E5=9B=A0=E5=86=85=E9=83=A8=20utils::cast=20=E9=98=BB?= =?UTF-8?q?=E6=96=AD=20SIMD=20=E5=AF=BC=E8=87=B4=E6=94=B6=E7=9B=8A?= =?UTF-8?q?=E5=8F=97=E9=99=90=EF=BC=8C=E4=BD=9C=E4=B8=BA=E5=BC=95=E5=85=A5?= =?UTF-8?q?=20BLAS=20=E5=BA=93=E7=9A=84=E6=80=A7=E8=83=BD=E5=8F=82?= =?UTF-8?q?=E7=85=A7?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- linear.prof | Bin 0 -> 7472848 bytes report_linear.pdf | Bin 0 -> 18930 bytes src/ops/linear/op.cpp | 56 ++++++++++++++++++++++++++++++------------ 3 files changed, 40 insertions(+), 16 deletions(-) create mode 100644 linear.prof create mode 100644 report_linear.pdf diff --git a/linear.prof b/linear.prof new file 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z<#BUJKzr;yc>D?A0rATaSeHOB;h0RC%)1fHXWUo4ugvus>(f0>BnzV7G1voxK28wb z;@lF=%NwLHcqqeS{+PUyNBvHY;0eo?79$iB)UkO|FY>%5tMv=*R9GMKuWRb1n9oe_gf2h;lOy(chbSFsIfckrMT|H&ha+vYCIOoAN8sm zpfmJ!_b|yk!5D8E$6(9=8yf5bj<&u#|7wK04!i3@(~Zt~=k#oLgP;YQJ(sf>@FOnH zblI*Jag>sZ#H6LUJs03Ay-3#5xGM#qBRe#JKs><2_Gn50IJ8S++(kHdX|9q7e6HIA zvTT>nlg^>6YahsZuDvhIdocX2uP#GCU`os?BJ_9iIztQyuzfiYbs7^x=s1gWqVo=L z-*`y=^hcMDZF^Tj!BLkDFNM4uIU{vpj~rVn&j~)}_^WY#RUMXf6L=1{9SnCG{k%GR E1ExrrTmS$7 literal 0 HcmV?d00001 diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index b4d2df880..cdb51de85 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -18,7 +18,6 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias size_t M; if (in->shape().size() == 2) { // 情况 A: 单元测试 (2D) [Rows, Hidden] - // 你的原版逻辑，保持不变 M = in->shape()[0]; } else { // 情况 B: DeepSeek 推理 (3D) [Batch, Seq, Hidden] @@ -26,23 +25,48 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias M = in->shape()[0] * in->shape()[1]; } - // 3. 矩阵乘法 Loop - // 注意:此处加入 OpenMP 多线程加速 - #pragma omp parallel for - for(size_t i = 0; i < M; i++){ - for(size_t j = 0; j < N; j++){ - float sum = 0.0f; + const size_t BLOCK_SIZE = 64; + + #pragma omp parallel for collapse(2) + for(size_t i0 = 0; i0 < M; i0 += BLOCK_SIZE){ + for(size_t j0 = 0; j0 < N; j0 += BLOCK_SIZE){ - // 内积：Input的第i行 * Weight的第j行 - for(size_t index = 0; index < K; index++){ - float x_val = utils::cast(in_ptr[index + i * K]); - float y_val = utils::cast(weight_ptr[index + j * K]); - sum += x_val * y_val; + size_t i_end = std::min(i0 + BLOCK_SIZE, M); + size_t j_end = std::min(j0 + BLOCK_SIZE, N); + + float block_sum[BLOCK_SIZE][BLOCK_SIZE] = {0.0f}; + + for(size_t k0 = 0; k0 < K; k0 += BLOCK_SIZE){ + size_t k_end = std::min(k0 + BLOCK_SIZE, K); + + for(size_t i = i0; i < i_end; ++i){ + for(size_t j = j0; j < j_end; ++j){ + float sum = 0.0f; + + // 让编译器在这里执行连续内存的向量化点积 + #pragma omp simd reduction(+:sum) + for(size_t k = k0; k < k_end; ++k){ + float x_val = utils::cast(in_ptr[k + i * K]); + float y_val = utils::cast(weight_ptr[k + j * K]); + sum += x_val * y_val; + } + block_sum[i - i0][j - j0] += sum; + } + } } - - if(bias_ptr) sum += utils::cast(bias_ptr[j]); - out_ptr[i * N + j] = utils::cast(sum); - + + // 写回目标张量 + for(size_t i = i0; i < i_end; ++i){ + for(size_t j = j0; j < j_end; ++j){ + float final_val = block_sum[i - i0][j - j0]; + if (bias && bias->numel() > 0) { + float b_val = utils::cast(bias_ptr[j]); + final_val += b_val; + } + out_ptr[j + i * N] = utils::cast(final_val); + } + } + } } } From fb003e377ef619b86e5c1097824c719413616f5d Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Fri, 13 Mar 2026 17:58:53 +0800 Subject: [PATCH 21/27] =?UTF-8?q?perf(linear):=20AVX2=20SIMD=20=E4=BC=98?= =?UTF-8?q?=E5=8C=96=20f32/bf16/fp16=20=E7=9F=A9=E9=98=B5=E4=B9=98?= =?UTF-8?q?=E6=B3=95=EF=BC=8C=E6=8F=90=E5=8F=96=E5=85=B1=E4=BA=AB=20SIMD?= =?UTF-8?q?=20=E5=B7=A5=E5=85=B7=E5=87=BD=E6=95=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - xmake.lua 全局启用 -mavx2 -mfma 编译选项 - 新增 src/utils/simd.hpp: hsum256, avx2_dot, bf16x8_to_f32x8, fp16x8_to_f32x8 - linear/op.cpp 使用 AVX2+FMA 重写三种数据类型的内核，代码量从 576 行降至 437 行 - 单线程性能: f32 ~40 GFLOPS, bf16 ~39 GFLOPS, fp16 ~9.6 GFLOPS --- src/ops/linear/op.cpp | 440 +++++++++++++++++++++++++++++++++++++----- src/utils/simd.hpp | 89 +++++++++ xmake.lua | 4 +- 3 files changed, 484 insertions(+), 49 deletions(-) create mode 100644 src/utils/simd.hpp diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index cdb51de85..1ce038cc2 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -1,91 +1,437 @@ +// simd.hpp MUST be included before op.hpp because op.hpp transitively pulls +// in llaisys.h which defines `#define __C extern "C"`. That macro corrupts +// parameter names inside (e.g. ia32intrin.h:63 `__crc32b(...__C...)`). +// By including simd.hpp first, is parsed before the __C macro exists. +#include "../../utils/simd.hpp" #include "op.hpp" #include "../../utils.hpp" +#include +#include +#include +#include namespace llaisys::ops { +// ============================================================ +// 泛型版本 (fallback): 纯标量, 无 OpenMP, 无 SIMD +// ============================================================ template void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ - const T* in_ptr = reinterpret_cast(in->data()); - const T* weight_ptr = reinterpret_cast(weight->data()); + const T* in_ptr = reinterpret_cast(in->data()); + const T* weight_ptr = reinterpret_cast(weight->data()); const T* bias_ptr = nullptr; - if(bias && bias->numel() > 0) bias_ptr = reinterpret_cast(bias->data()); + if(bias && bias->numel() > 0) bias_ptr = reinterpret_cast(bias->data()); T* out_ptr = reinterpret_cast(out->data()); size_t K = in->shape().back(); size_t N = weight->shape()[0]; - // 2. 【核心修复】智能计算行数 M size_t M; if (in->shape().size() == 2) { - // 情况 A: 单元测试 (2D) [Rows, Hidden] - M = in->shape()[0]; + M = in->shape()[0]; } else { - // 情况 B: DeepSeek 推理 (3D) [Batch, Seq, Hidden] - // 必须把 Batch 和 Seq 乘起来，否则只算第一行！ M = in->shape()[0] * in->shape()[1]; } - const size_t BLOCK_SIZE = 64; - - #pragma omp parallel for collapse(2) - for(size_t i0 = 0; i0 < M; i0 += BLOCK_SIZE){ - for(size_t j0 = 0; j0 < N; j0 += BLOCK_SIZE){ - - size_t i_end = std::min(i0 + BLOCK_SIZE, M); - size_t j_end = std::min(j0 + BLOCK_SIZE, N); - - float block_sum[BLOCK_SIZE][BLOCK_SIZE] = {0.0f}; - - for(size_t k0 = 0; k0 < K; k0 += BLOCK_SIZE){ - size_t k_end = std::min(k0 + BLOCK_SIZE, K); - - for(size_t i = i0; i < i_end; ++i){ - for(size_t j = j0; j < j_end; ++j){ - float sum = 0.0f; - - // 让编译器在这里执行连续内存的向量化点积 - #pragma omp simd reduction(+:sum) - for(size_t k = k0; k < k_end; ++k){ - float x_val = utils::cast(in_ptr[k + i * K]); - float y_val = utils::cast(weight_ptr[k + j * K]); - sum += x_val * y_val; - } - block_sum[i - i0][j - j0] += sum; + for (size_t i = 0; i < M; ++i) { + for (size_t j = 0; j < N; ++j) { + float sum = 0.0f; + for (size_t k = 0; k < K; ++k) { + float x_val = utils::cast(in_ptr[k + i * K]); + float w_val = utils::cast(weight_ptr[k + j * K]); + sum += x_val * w_val; + } + if (bias_ptr) { + sum += utils::cast(bias_ptr[j]); + } + out_ptr[j + i * N] = utils::cast(sum); + } + } +} + +// 提取维度信息: M, K, N +static inline void get_dims(tensor_t in, tensor_t weight, + size_t &M, size_t &K, size_t &N) +{ + const auto& xs = in->shape(); + const auto& ws = weight->shape(); + if (xs.size() == 2) { + M = xs[0]; K = xs[1]; + } else if (xs.size() == 3) { + M = xs[0] * xs[1]; K = xs[2]; + } else { + K = xs.back(); M = in->numel() / K; + } + N = ws[0]; +} + +// ============================================================ +// float32 AVX2+FMA 特化 +// ============================================================ +template<> +void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ + const float* X = reinterpret_cast(in->data()); + const float* W = reinterpret_cast(weight->data()); + const float* b = (bias && bias->numel() > 0) + ? reinterpret_cast(bias->data()) : nullptr; + float* Y = reinterpret_cast(out->data()); + + size_t M, K, N; + get_dims(in, weight, M, K, N); + + if (b) { + for (size_t i = 0; i < M; ++i) { + float* yrow = Y + i * N; + size_t j = 0; + for (; j + 7 < N; j += 8) + _mm256_storeu_ps(yrow + j, _mm256_loadu_ps(b + j)); + for (; j < N; ++j) + yrow[j] = b[j]; + } + } else { + std::memset(Y, 0, M * N * sizeof(float)); + } + + static constexpr size_t BLOCK_K = 512; + static constexpr size_t BLOCK_N = 4; + + for (size_t k0 = 0; k0 < K; k0 += BLOCK_K) { + const size_t kc = std::min(BLOCK_K, K - k0); + + for (size_t j0 = 0; j0 < N; j0 += BLOCK_N) { + const size_t jn = std::min(BLOCK_N, N - j0); + + if (jn == 4) { + const float* w0 = W + (j0 + 0) * K + k0; + const float* w1 = W + (j0 + 1) * K + k0; + const float* w2 = W + (j0 + 2) * K + k0; + const float* w3 = W + (j0 + 3) * K + k0; + + for (size_t i = 0; i < M; ++i) { + const float* xi = X + i * K + k0; + __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); + __m256 a10 = _mm256_setzero_ps(), a11 = _mm256_setzero_ps(); + __m256 a20 = _mm256_setzero_ps(), a21 = _mm256_setzero_ps(); + __m256 a30 = _mm256_setzero_ps(), a31 = _mm256_setzero_ps(); + + size_t k = 0; + for (; k + 15 < kc; k += 16) { + __m256 x0 = _mm256_loadu_ps(xi + k); + __m256 x1 = _mm256_loadu_ps(xi + k + 8); + a00 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w0 + k), a00); + a01 = _mm256_fmadd_ps(x1, _mm256_loadu_ps(w0 + k + 8), a01); + a10 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w1 + k), a10); + a11 = _mm256_fmadd_ps(x1, _mm256_loadu_ps(w1 + k + 8), a11); + a20 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w2 + k), a20); + a21 = _mm256_fmadd_ps(x1, _mm256_loadu_ps(w2 + k + 8), a21); + a30 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w3 + k), a30); + a31 = _mm256_fmadd_ps(x1, _mm256_loadu_ps(w3 + k + 8), a31); + } + for (; k + 7 < kc; k += 8) { + __m256 x0 = _mm256_loadu_ps(xi + k); + a00 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w0 + k), a00); + a10 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w1 + k), a10); + a20 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w2 + k), a20); + a30 = _mm256_fmadd_ps(x0, _mm256_loadu_ps(w3 + k), a30); + } + + float d0 = utils::hsum256(_mm256_add_ps(a00, a01)); + float d1 = utils::hsum256(_mm256_add_ps(a10, a11)); + float d2 = utils::hsum256(_mm256_add_ps(a20, a21)); + float d3 = utils::hsum256(_mm256_add_ps(a30, a31)); + + for (; k < kc; ++k) { + float xk = xi[k]; + d0 += xk * w0[k]; d1 += xk * w1[k]; + d2 += xk * w2[k]; d3 += xk * w3[k]; } + + Y[i*N + j0] += d0; + Y[i*N + j0+1] += d1; + Y[i*N + j0+2] += d2; + Y[i*N + j0+3] += d3; + } + } else { + for (size_t jj = 0; jj < jn; ++jj) { + const float* wj = W + (j0 + jj) * K + k0; + for (size_t i = 0; i < M; ++i) + Y[i*N + j0 + jj] += utils::avx2_dot(X + i*K + k0, wj, kc); } } + } + } +} + +// ============================================================ +// bfloat16 AVX2 特化 +// ============================================================ +template<> +void linear_cpu_kernel(tensor_t out, tensor_t in, + tensor_t weight, tensor_t bias) +{ + const uint16_t* X = reinterpret_cast(in->data()); + const uint16_t* W = reinterpret_cast(weight->data()); + const uint16_t* B = nullptr; + if (bias && bias->numel() > 0) + B = reinterpret_cast(bias->data()); + uint16_t* Y = reinterpret_cast(out->data()); + + size_t M, K, N; + get_dims(in, weight, M, K, N); + + std::vector ybuf(M * N); + + if (B) { + for (size_t i = 0; i < M; ++i) { + float* yrow = ybuf.data() + i * N; + for (size_t j = 0; j < N; ++j) + yrow[j] = utils::cast(llaisys::bf16_t{B[j]}); + } + } else { + std::memset(ybuf.data(), 0, M * N * sizeof(float)); + } + + static constexpr size_t BLOCK_K = 512; + static constexpr size_t BLOCK_N = 4; + + for (size_t k0 = 0; k0 < K; k0 += BLOCK_K) { + const size_t kc = std::min(BLOCK_K, K - k0); + + for (size_t j0 = 0; j0 < N; j0 += BLOCK_N) { + const size_t jn = std::min(BLOCK_N, N - j0); + + if (jn == 4) { + const uint16_t* w0 = W + (j0 + 0) * K + k0; + const uint16_t* w1 = W + (j0 + 1) * K + k0; + const uint16_t* w2 = W + (j0 + 2) * K + k0; + const uint16_t* w3 = W + (j0 + 3) * K + k0; - // 写回目标张量 - for(size_t i = i0; i < i_end; ++i){ - for(size_t j = j0; j < j_end; ++j){ - float final_val = block_sum[i - i0][j - j0]; - if (bias && bias->numel() > 0) { - float b_val = utils::cast(bias_ptr[j]); - final_val += b_val; + for (size_t i = 0; i < M; ++i) { + const uint16_t* xi = X + i * K + k0; + + __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); + __m256 a10 = _mm256_setzero_ps(), a11 = _mm256_setzero_ps(); + __m256 a20 = _mm256_setzero_ps(), a21 = _mm256_setzero_ps(); + __m256 a30 = _mm256_setzero_ps(), a31 = _mm256_setzero_ps(); + + size_t k = 0; + for (; k + 15 < kc; k += 16) { + __m256 x0 = utils::bf16x8_to_f32x8(xi + k); + __m256 x1 = utils::bf16x8_to_f32x8(xi + k + 8); + a00 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w0 + k), a00); + a01 = _mm256_fmadd_ps(x1, utils::bf16x8_to_f32x8(w0 + k + 8), a01); + a10 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w1 + k), a10); + a11 = _mm256_fmadd_ps(x1, utils::bf16x8_to_f32x8(w1 + k + 8), a11); + a20 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w2 + k), a20); + a21 = _mm256_fmadd_ps(x1, utils::bf16x8_to_f32x8(w2 + k + 8), a21); + a30 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w3 + k), a30); + a31 = _mm256_fmadd_ps(x1, utils::bf16x8_to_f32x8(w3 + k + 8), a31); + } + for (; k + 7 < kc; k += 8) { + __m256 x0 = utils::bf16x8_to_f32x8(xi + k); + a00 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w0 + k), a00); + a10 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w1 + k), a10); + a20 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w2 + k), a20); + a30 = _mm256_fmadd_ps(x0, utils::bf16x8_to_f32x8(w3 + k), a30); + } + + float d0 = utils::hsum256(_mm256_add_ps(a00, a01)); + float d1 = utils::hsum256(_mm256_add_ps(a10, a11)); + float d2 = utils::hsum256(_mm256_add_ps(a20, a21)); + float d3 = utils::hsum256(_mm256_add_ps(a30, a31)); + + for (; k < kc; ++k) { + float xk = utils::cast(llaisys::bf16_t{xi[k]}); + d0 += xk * utils::cast(llaisys::bf16_t{w0[k]}); + d1 += xk * utils::cast(llaisys::bf16_t{w1[k]}); + d2 += xk * utils::cast(llaisys::bf16_t{w2[k]}); + d3 += xk * utils::cast(llaisys::bf16_t{w3[k]}); + } + + ybuf[i*N + j0] += d0; + ybuf[i*N + j0 + 1] += d1; + ybuf[i*N + j0 + 2] += d2; + ybuf[i*N + j0 + 3] += d3; + } + } else { + for (size_t jj = 0; jj < jn; ++jj) { + const uint16_t* wj = W + (j0 + jj) * K + k0; + for (size_t i = 0; i < M; ++i) { + const uint16_t* xi = X + i * K + k0; + __m256 acc0 = _mm256_setzero_ps(); + __m256 acc1 = _mm256_setzero_ps(); + size_t k = 0; + for (; k + 15 < kc; k += 16) { + acc0 = _mm256_fmadd_ps(utils::bf16x8_to_f32x8(xi + k), + utils::bf16x8_to_f32x8(wj + k), acc0); + acc1 = _mm256_fmadd_ps(utils::bf16x8_to_f32x8(xi + k + 8), + utils::bf16x8_to_f32x8(wj + k + 8), acc1); + } + for (; k + 7 < kc; k += 8) + acc0 = _mm256_fmadd_ps(utils::bf16x8_to_f32x8(xi + k), + utils::bf16x8_to_f32x8(wj + k), acc0); + float sum = utils::hsum256(_mm256_add_ps(acc0, acc1)); + for (; k < kc; ++k) + sum += utils::cast(llaisys::bf16_t{xi[k]}) + * utils::cast(llaisys::bf16_t{wj[k]}); + ybuf[i*N + j0 + jj] += sum; } - out_ptr[j + i * N] = utils::cast(final_val); } } + } + } + + // f32 → bf16 写回 + for (size_t idx = 0; idx < M * N; ++idx) { + llaisys::bf16_t v = utils::cast(ybuf[idx]); + Y[idx] = v._v; + } +} + +// ============================================================ +// float16 AVX2 特化 +// ============================================================ +template<> +void linear_cpu_kernel(tensor_t out, tensor_t in, + tensor_t weight, tensor_t bias) +{ + const uint16_t* X = reinterpret_cast(in->data()); + const uint16_t* W = reinterpret_cast(weight->data()); + const uint16_t* B = nullptr; + if (bias && bias->numel() > 0) + B = reinterpret_cast(bias->data()); + uint16_t* Y_out = reinterpret_cast(out->data()); + size_t M, K, N; + get_dims(in, weight, M, K, N); + + std::vector ybuf(M * N); + + if (B) { + for (size_t i = 0; i < M; ++i) { + float* yrow = ybuf.data() + i * N; + for (size_t j = 0; j < N; ++j) + yrow[j] = utils::cast(llaisys::fp16_t{B[j]}); } + } else { + std::memset(ybuf.data(), 0, M * N * sizeof(float)); + } + + static constexpr size_t BLOCK_K = 512; + static constexpr size_t BLOCK_N = 4; + + for (size_t k0 = 0; k0 < K; k0 += BLOCK_K) { + const size_t kc = std::min(BLOCK_K, K - k0); + + for (size_t j0 = 0; j0 < N; j0 += BLOCK_N) { + const size_t jn = std::min(BLOCK_N, N - j0); + + if (jn == 4) { + const uint16_t* w0 = W + (j0 + 0) * K + k0; + const uint16_t* w1 = W + (j0 + 1) * K + k0; + const uint16_t* w2 = W + (j0 + 2) * K + k0; + const uint16_t* w3 = W + (j0 + 3) * K + k0; + + for (size_t i = 0; i < M; ++i) { + const uint16_t* xi = X + i * K + k0; + + __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); + __m256 a10 = _mm256_setzero_ps(), a11 = _mm256_setzero_ps(); + __m256 a20 = _mm256_setzero_ps(), a21 = _mm256_setzero_ps(); + __m256 a30 = _mm256_setzero_ps(), a31 = _mm256_setzero_ps(); + + size_t k = 0; + for (; k + 15 < kc; k += 16) { + __m256 x0 = utils::fp16x8_to_f32x8(xi + k); + __m256 x1 = utils::fp16x8_to_f32x8(xi + k + 8); + a00 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w0 + k), a00); + a01 = _mm256_fmadd_ps(x1, utils::fp16x8_to_f32x8(w0 + k + 8), a01); + a10 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w1 + k), a10); + a11 = _mm256_fmadd_ps(x1, utils::fp16x8_to_f32x8(w1 + k + 8), a11); + a20 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w2 + k), a20); + a21 = _mm256_fmadd_ps(x1, utils::fp16x8_to_f32x8(w2 + k + 8), a21); + a30 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w3 + k), a30); + a31 = _mm256_fmadd_ps(x1, utils::fp16x8_to_f32x8(w3 + k + 8), a31); + } + for (; k + 7 < kc; k += 8) { + __m256 x0 = utils::fp16x8_to_f32x8(xi + k); + a00 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w0 + k), a00); + a10 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w1 + k), a10); + a20 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w2 + k), a20); + a30 = _mm256_fmadd_ps(x0, utils::fp16x8_to_f32x8(w3 + k), a30); + } + + float d0 = utils::hsum256(_mm256_add_ps(a00, a01)); + float d1 = utils::hsum256(_mm256_add_ps(a10, a11)); + float d2 = utils::hsum256(_mm256_add_ps(a20, a21)); + float d3 = utils::hsum256(_mm256_add_ps(a30, a31)); + + for (; k < kc; ++k) { + float xk = utils::cast(llaisys::fp16_t{xi[k]}); + d0 += xk * utils::cast(llaisys::fp16_t{w0[k]}); + d1 += xk * utils::cast(llaisys::fp16_t{w1[k]}); + d2 += xk * utils::cast(llaisys::fp16_t{w2[k]}); + d3 += xk * utils::cast(llaisys::fp16_t{w3[k]}); + } + + ybuf[i*N + j0] += d0; + ybuf[i*N + j0 + 1] += d1; + ybuf[i*N + j0 + 2] += d2; + ybuf[i*N + j0 + 3] += d3; + } + } else { + for (size_t jj = 0; jj < jn; ++jj) { + const uint16_t* wj = W + (j0 + jj) * K + k0; + for (size_t i = 0; i < M; ++i) { + const uint16_t* xi = X + i * K + k0; + __m256 acc0 = _mm256_setzero_ps(); + __m256 acc1 = _mm256_setzero_ps(); + size_t k = 0; + for (; k + 15 < kc; k += 16) { + acc0 = _mm256_fmadd_ps(utils::fp16x8_to_f32x8(xi + k), + utils::fp16x8_to_f32x8(wj + k), acc0); + acc1 = _mm256_fmadd_ps(utils::fp16x8_to_f32x8(xi + k + 8), + utils::fp16x8_to_f32x8(wj + k + 8), acc1); + } + for (; k + 7 < kc; k += 8) + acc0 = _mm256_fmadd_ps(utils::fp16x8_to_f32x8(xi + k), + utils::fp16x8_to_f32x8(wj + k), acc0); + float sum = utils::hsum256(_mm256_add_ps(acc0, acc1)); + for (; k < kc; ++k) + sum += utils::cast(llaisys::fp16_t{xi[k]}) + * utils::cast(llaisys::fp16_t{wj[k]}); + ybuf[i*N + j0 + jj] += sum; + } + } + } + } + } + + // f32 → fp16 写回 + for (size_t idx = 0; idx < M * N; ++idx) { + llaisys::fp16_t v = utils::cast(ybuf[idx]); + Y_out[idx] = v._v; } } +// ============================================================ +// 入口函数 +// ============================================================ void linear(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias) { - in=in->isContiguous()?in:in->contiguous(); + in = in->isContiguous() ? in : in->contiguous(); weight = weight->isContiguous() ? weight : weight->contiguous(); switch (in->dtype()) { case LLAISYS_DTYPE_F16: - linear_cpu_kernel(out,in,weight,bias); + linear_cpu_kernel(out, in, weight, bias); break; case LLAISYS_DTYPE_BF16: - linear_cpu_kernel(out,in,weight,bias); + linear_cpu_kernel(out, in, weight, bias); break; case LLAISYS_DTYPE_F32: - linear_cpu_kernel(out,in,weight,bias); + linear_cpu_kernel(out, in, weight, bias); break; default: throw std::runtime_error("Not support this dtype!"); } } -} // namespace llaisys::ops \ No newline at end of file +} // namespace llaisys::ops diff --git a/src/utils/simd.hpp b/src/utils/simd.hpp new file mode 100644 index 000000000..12a237ec5 --- /dev/null +++ b/src/utils/simd.hpp @@ -0,0 +1,89 @@ +#pragma once +#include +#include + +namespace llaisys::utils { + +// ============================================================ +// AVX2 SIMD 辅助函数 +// 供各个 op 的 AVX2 特化共用 +// ============================================================ + +// AVX2 水平求和: 8 个 float → 1 个 float +inline float hsum256(__m256 v) { + __m128 hi = _mm256_extractf128_ps(v, 1); + __m128 lo = _mm256_castps256_ps128(v); + __m128 sum4 = _mm_add_ps(lo, hi); + sum4 = _mm_hadd_ps(sum4, sum4); + sum4 = _mm_hadd_ps(sum4, sum4); + float r; + _mm_store_ss(&r, sum4); + return r; +} + +// AVX2 float32 双累加器点积 +inline float avx2_dot(const float* __restrict__ a, + const float* __restrict__ b, + size_t len) +{ + __m256 acc0 = _mm256_setzero_ps(); + __m256 acc1 = _mm256_setzero_ps(); + + size_t k = 0; + for (; k + 15 < len; k += 16) { + acc0 = _mm256_fmadd_ps(_mm256_loadu_ps(a + k), _mm256_loadu_ps(b + k), acc0); + acc1 = _mm256_fmadd_ps(_mm256_loadu_ps(a + k + 8), _mm256_loadu_ps(b + k + 8), acc1); + } + for (; k + 7 < len; k += 8) { + acc0 = _mm256_fmadd_ps(_mm256_loadu_ps(a + k), _mm256_loadu_ps(b + k), acc0); + } + acc0 = _mm256_add_ps(acc0, acc1); + float dot = hsum256(acc0); + for (; k < len; ++k) { + dot += a[k] * b[k]; + } + return dot; +} + +// ============================================================ +// 批量类型转换 (8 元素 SIMD) +// ============================================================ + +// bf16 x8 → f32 x8 +// bf16 与 f32 共享指数格式, 仅需左移 16 位 +inline __m256 bf16x8_to_f32x8(const uint16_t* p) { + __m128i h8 = _mm_loadu_si128(reinterpret_cast(p)); + __m256i i32 = _mm256_cvtepu16_epi32(h8); + i32 = _mm256_slli_epi32(i32, 16); + return _mm256_castsi256_ps(i32); +} + +// fp16 x8 → f32 x8 +// 提取 sign/exp/mantissa, 重偏指数 (+112), 处理零值 +inline __m256 fp16x8_to_f32x8(const uint16_t* p) { + __m128i h8 = _mm_loadu_si128(reinterpret_cast(p)); + __m256i i32 = _mm256_cvtepu16_epi32(h8); + + __m256i sign = _mm256_slli_epi32( + _mm256_and_si256(i32, _mm256_set1_epi32(0x8000)), 16); + __m256i exp16 = _mm256_and_si256( + _mm256_srli_epi32(i32, 10), _mm256_set1_epi32(0x1F)); + __m256i mant = _mm256_and_si256(i32, _mm256_set1_epi32(0x3FF)); + + __m256i exp32 = _mm256_slli_epi32( + _mm256_add_epi32(exp16, _mm256_set1_epi32(112)), 23); + __m256i mant32 = _mm256_slli_epi32(mant, 13); + + __m256i result = _mm256_or_si256(sign, _mm256_or_si256(exp32, mant32)); + + // 处理零 (exp==0 且 mant==0) + __m256i is_zero = _mm256_cmpeq_epi32( + _mm256_and_si256(i32, _mm256_set1_epi32(0x7FFF)), + _mm256_setzero_si256()); + result = _mm256_andnot_si256(is_zero, result); + result = _mm256_or_si256(result, _mm256_and_si256(is_zero, sign)); + + return _mm256_castsi256_ps(result); +} + +} // namespace llaisys::utils diff --git a/xmake.lua b/xmake.lua index 567393fd7..f67675310 100644 --- a/xmake.lua +++ b/xmake.lua @@ -3,8 +3,8 @@ set_encodings("utf-8") add_includedirs("include") --- 全局开启 OpenMP 支持和编译器最高级别优化 -add_cxflags("-fopenmp", "-O3") +-- 全局开启 OpenMP 支持、最高级别优化、以及 AVX2/FMA 向量化指令集支持 +add_cxflags("-fopenmp", "-O3", "-mavx2", "-mfma") add_ldflags("-fopenmp") add_shflags("-fopenmp") add_syslinks("gomp") -- 显式链接 GNU OpenMP 库 From 2380d3e2e91441e5b5ca2b91123a18611d20fb66 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sat, 14 Mar 2026 09:56:20 +0800 Subject: [PATCH 22/27] =?UTF-8?q?feat(linear):=20=E6=B7=BB=E5=8A=A0OpenMP?= =?UTF-8?q?=E5=A4=9A=E7=BA=BF=E7=A8=8B=E5=B9=B6=E8=A1=8C=E5=8C=96=EF=BC=8C?= =?UTF-8?q?6=E7=BA=BF=E7=A8=8B=E5=8A=A0=E9=80=9F=E7=BA=A63x?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- src/ops/linear/op.cpp | 46 ++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 43 insertions(+), 3 deletions(-) diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 1ce038cc2..3b76eb757 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -9,6 +9,9 @@ #include #include #include +#ifdef _OPENMP +#include +#endif namespace llaisys::ops { @@ -66,7 +69,9 @@ static inline void get_dims(tensor_t in, tensor_t weight, } // ============================================================ -// float32 AVX2+FMA 特化 +// float32 AVX2+FMA 特化 (OpenMP 并行化) +// 保持原始 k0→j0→i 分块顺序以复用 W 块缓存。 +// i 维度行间完全独立，#pragma omp parallel for 在最内层 i 循环上。 // ============================================================ template<> void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ @@ -80,6 +85,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor get_dims(in, weight, M, K, N); if (b) { +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { float* yrow = Y + i * N; size_t j = 0; @@ -107,6 +115,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor const float* w2 = W + (j0 + 2) * K + k0; const float* w3 = W + (j0 + 3) * K + k0; +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { const float* xi = X + i * K + k0; __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); @@ -154,6 +165,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor } else { for (size_t jj = 0; jj < jn; ++jj) { const float* wj = W + (j0 + jj) * K + k0; +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) Y[i*N + j0 + jj] += utils::avx2_dot(X + i*K + k0, wj, kc); } @@ -163,7 +177,8 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor } // ============================================================ -// bfloat16 AVX2 特化 +// bfloat16 AVX2 特化 (OpenMP 并行化) +// ybuf 作为整体预分配，i 循环写入各自行 ybuf[i*N..i*N+N-1]，行间无竞争。 // ============================================================ template<> void linear_cpu_kernel(tensor_t out, tensor_t in, @@ -182,6 +197,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, std::vector ybuf(M * N); if (B) { +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { float* yrow = ybuf.data() + i * N; for (size_t j = 0; j < N; ++j) @@ -206,6 +224,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, const uint16_t* w2 = W + (j0 + 2) * K + k0; const uint16_t* w3 = W + (j0 + 3) * K + k0; +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { const uint16_t* xi = X + i * K + k0; @@ -256,6 +277,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, } else { for (size_t jj = 0; jj < jn; ++jj) { const uint16_t* wj = W + (j0 + jj) * K + k0; +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { const uint16_t* xi = X + i * K + k0; __m256 acc0 = _mm256_setzero_ps(); @@ -282,6 +306,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, } // f32 → bf16 写回 +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t idx = 0; idx < M * N; ++idx) { llaisys::bf16_t v = utils::cast(ybuf[idx]); Y[idx] = v._v; @@ -289,7 +316,8 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, } // ============================================================ -// float16 AVX2 特化 +// float16 AVX2 特化 (OpenMP 并行化) +// ybuf 作为整体预分配，i 循环写入各自行 ybuf[i*N..i*N+N-1]，行间无竞争。 // ============================================================ template<> void linear_cpu_kernel(tensor_t out, tensor_t in, @@ -308,6 +336,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, std::vector ybuf(M * N); if (B) { +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { float* yrow = ybuf.data() + i * N; for (size_t j = 0; j < N; ++j) @@ -332,6 +363,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, const uint16_t* w2 = W + (j0 + 2) * K + k0; const uint16_t* w3 = W + (j0 + 3) * K + k0; +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { const uint16_t* xi = X + i * K + k0; @@ -382,6 +416,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, } else { for (size_t jj = 0; jj < jn; ++jj) { const uint16_t* wj = W + (j0 + jj) * K + k0; +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t i = 0; i < M; ++i) { const uint16_t* xi = X + i * K + k0; __m256 acc0 = _mm256_setzero_ps(); @@ -408,6 +445,9 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, } // f32 → fp16 写回 +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif for (size_t idx = 0; idx < M * N; ++idx) { llaisys::fp16_t v = utils::cast(ybuf[idx]); Y_out[idx] = v._v; From f10b57923a31762f5d2c3b26ac79d01ed6166fe8 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sat, 14 Mar 2026 17:37:25 +0800 Subject: [PATCH 23/27] =?UTF-8?q?fix(linear):=20=E7=94=A8C=E7=BA=A7if?= =?UTF-8?q?=E6=9B=BF=E4=BB=A3OpenMP=20if=E5=AD=90=E5=8F=A5=EF=BC=8C?= =?UTF-8?q?=E6=B6=88=E9=99=A4M=3D1=E6=97=B6GOMP=E8=BF=90=E8=A1=8C=E6=97=B6?= =?UTF-8?q?=E5=BC=80=E9=94=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 添加parallel_for模板函数，M<32时完全绕过OpenMP运行时 - 替换全部10处#pragma omp为parallel_for调用 - 模型推理从104s恢复到28s，修复OpenMP导致的性能回退 --- src/ops/linear/op.cpp | 94 ++++++++++++++++++------------------------- 1 file changed, 40 insertions(+), 54 deletions(-) diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 3b76eb757..99df0b3c0 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -15,6 +15,24 @@ namespace llaisys::ops { +// M 维度低于此阈值时跳过 OpenMP，避免线程创建/同步开销。 +// 模型推理解码阶段 M=1，若不跳过，每次 linear 调用产生数千次无效 barrier。 +static constexpr size_t OMP_M_THRESHOLD = 32; + +// 条件并行 for：当 n >= threshold 时使用 OpenMP 多线程，否则纯串行。 +// 使用 C 级 if 而非 OpenMP if() 子句，后者仍会进入 GOMP 运行时产生开销。 +template +static inline void parallel_for(size_t n, size_t threshold, F&& func) { +#ifdef _OPENMP + if (n >= threshold) { + #pragma omp parallel for schedule(static) + for (size_t i = 0; i < n; ++i) func(i); + return; + } +#endif + for (size_t i = 0; i < n; ++i) func(i); +} + // ============================================================ // 泛型版本 (fallback): 纯标量, 无 OpenMP, 无 SIMD // ============================================================ @@ -85,17 +103,14 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor get_dims(in, weight, M, K, N); if (b) { -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { float* yrow = Y + i * N; size_t j = 0; for (; j + 7 < N; j += 8) _mm256_storeu_ps(yrow + j, _mm256_loadu_ps(b + j)); for (; j < N; ++j) yrow[j] = b[j]; - } + }); } else { std::memset(Y, 0, M * N * sizeof(float)); } @@ -115,10 +130,7 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor const float* w2 = W + (j0 + 2) * K + k0; const float* w3 = W + (j0 + 3) * K + k0; -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { const float* xi = X + i * K + k0; __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); __m256 a10 = _mm256_setzero_ps(), a11 = _mm256_setzero_ps(); @@ -161,15 +173,13 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor Y[i*N + j0+1] += d1; Y[i*N + j0+2] += d2; Y[i*N + j0+3] += d3; - } + }); } else { for (size_t jj = 0; jj < jn; ++jj) { const float* wj = W + (j0 + jj) * K + k0; -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { Y[i*N + j0 + jj] += utils::avx2_dot(X + i*K + k0, wj, kc); + }); } } } @@ -197,14 +207,11 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, std::vector ybuf(M * N); if (B) { -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { float* yrow = ybuf.data() + i * N; for (size_t j = 0; j < N; ++j) yrow[j] = utils::cast(llaisys::bf16_t{B[j]}); - } + }); } else { std::memset(ybuf.data(), 0, M * N * sizeof(float)); } @@ -224,10 +231,7 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, const uint16_t* w2 = W + (j0 + 2) * K + k0; const uint16_t* w3 = W + (j0 + 3) * K + k0; -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { const uint16_t* xi = X + i * K + k0; __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); @@ -273,14 +277,11 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, ybuf[i*N + j0 + 1] += d1; ybuf[i*N + j0 + 2] += d2; ybuf[i*N + j0 + 3] += d3; - } + }); } else { for (size_t jj = 0; jj < jn; ++jj) { const uint16_t* wj = W + (j0 + jj) * K + k0; -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { const uint16_t* xi = X + i * K + k0; __m256 acc0 = _mm256_setzero_ps(); __m256 acc1 = _mm256_setzero_ps(); @@ -299,20 +300,17 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, sum += utils::cast(llaisys::bf16_t{xi[k]}) * utils::cast(llaisys::bf16_t{wj[k]}); ybuf[i*N + j0 + jj] += sum; - } + }); } } } } // f32 → bf16 写回 -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t idx = 0; idx < M * N; ++idx) { + parallel_for(M * N, OMP_M_THRESHOLD, [&](size_t idx) { llaisys::bf16_t v = utils::cast(ybuf[idx]); Y[idx] = v._v; - } + }); } // ============================================================ @@ -336,14 +334,11 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, std::vector ybuf(M * N); if (B) { -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { float* yrow = ybuf.data() + i * N; for (size_t j = 0; j < N; ++j) yrow[j] = utils::cast(llaisys::fp16_t{B[j]}); - } + }); } else { std::memset(ybuf.data(), 0, M * N * sizeof(float)); } @@ -363,10 +358,7 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, const uint16_t* w2 = W + (j0 + 2) * K + k0; const uint16_t* w3 = W + (j0 + 3) * K + k0; -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { const uint16_t* xi = X + i * K + k0; __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); @@ -412,14 +404,11 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, ybuf[i*N + j0 + 1] += d1; ybuf[i*N + j0 + 2] += d2; ybuf[i*N + j0 + 3] += d3; - } + }); } else { for (size_t jj = 0; jj < jn; ++jj) { const uint16_t* wj = W + (j0 + jj) * K + k0; -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t i = 0; i < M; ++i) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { const uint16_t* xi = X + i * K + k0; __m256 acc0 = _mm256_setzero_ps(); __m256 acc1 = _mm256_setzero_ps(); @@ -438,20 +427,17 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, sum += utils::cast(llaisys::fp16_t{xi[k]}) * utils::cast(llaisys::fp16_t{wj[k]}); ybuf[i*N + j0 + jj] += sum; - } + }); } } } } // f32 → fp16 写回 -#ifdef _OPENMP - #pragma omp parallel for schedule(static) -#endif - for (size_t idx = 0; idx < M * N; ++idx) { + parallel_for(M * N, OMP_M_THRESHOLD, [&](size_t idx) { llaisys::fp16_t v = utils::cast(ybuf[idx]); Y_out[idx] = v._v; - } + }); } // ============================================================ From acc5d7500dcf1d804c9036c9bb017b32ddc10fc4 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Sun, 15 Mar 2026 10:57:43 +0800 Subject: [PATCH 24/27] =?UTF-8?q?feat(linear):=20=E9=9B=86=E6=88=90=20Open?= =?UTF-8?q?BLAS=20sgemm=20=E5=8A=A0=E9=80=9F=20linear=20=E7=AE=97=E5=AD=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - 添加 OpenBLAS 编译选项 (xmake.lua), 自动链接 ~/openblas - f32 路径直接调用 cblas_sgemm, 完全替代手写 AVX2 - bf16 路径采用混合策略: - M >= 32: bf16→f32 转换 + cblas_sgemm (权重缓存避免重复转换) - M < 32 (含 M=1 decode): 手写 AVX2 直接读 bf16, 节省内存带宽 - fp16 路径: fp16→f32 + cblas_sgemm (权重缓存) - 发现 OpenBLAS cblas_sbgemm 的 beta 参数和大 K 精度 bug, 已绕过 - 所有 6/6 linear 测试通过, 模型推理 token 精确匹配 --- src/ops/linear/op.cpp | 219 ++++++++++++++++++++++++++++++++++++++---- xmake.lua | 15 +++ 2 files changed, 215 insertions(+), 19 deletions(-) diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 99df0b3c0..3890e26fb 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -12,9 +12,55 @@ #ifdef _OPENMP #include #endif +#ifdef USE_OPENBLAS +#include +#include +#include +#endif namespace llaisys::ops { +#ifdef USE_OPENBLAS +// 权重缓存: bf16/fp16 权重在首次使用时转为 f32 并缓存, 后续调用直接复用。 +// key = (原始权重数据指针, 元素个数, 类型标签)。 +// 类型标签区分同一地址不同 dtype 的数据 (测试场景下内存可能被复用)。 +// 推理场景中权重地址唯一且不释放, 缓存命中率 100%。 +struct WeightCacheKey { + const void* ptr; + size_t count; + int dtype_tag; // 0=bf16, 1=fp16 + bool operator==(const WeightCacheKey& o) const { + return ptr == o.ptr && count == o.count && dtype_tag == o.dtype_tag; + } +}; +struct WeightCacheKeyHash { + size_t operator()(const WeightCacheKey& k) const { + size_t h = std::hash()(k.ptr); + h ^= std::hash()(k.count) << 1; + h ^= std::hash()(k.dtype_tag) << 2; + return h; + } +}; +static std::unordered_map, WeightCacheKeyHash> g_weight_cache; +static std::mutex g_weight_cache_mutex; + +// 查找或创建 f32 权重缓存 (线程安全) +template +static const float* get_cached_f32_weights(const void* key, size_t count, + int dtype_tag, ConvertFn convert_fn) +{ + WeightCacheKey cache_key{key, count, dtype_tag}; + std::lock_guard lock(g_weight_cache_mutex); + auto it = g_weight_cache.find(cache_key); + if (it != g_weight_cache.end()) + return it->second.data(); + auto& buf = g_weight_cache[cache_key]; + buf.resize(count); + convert_fn(buf.data()); + return buf.data(); +} +#endif + // M 维度低于此阈值时跳过 OpenMP，避免线程创建/同步开销。 // 模型推理解码阶段 M=1，若不跳过，每次 linear 调用产生数千次无效 barrier。 static constexpr size_t OMP_M_THRESHOLD = 32; @@ -87,9 +133,9 @@ static inline void get_dims(tensor_t in, tensor_t weight, } // ============================================================ -// float32 AVX2+FMA 特化 (OpenMP 并行化) -// 保持原始 k0→j0→i 分块顺序以复用 W 块缓存。 -// i 维度行间完全独立，#pragma omp parallel for 在最内层 i 循环上。 +// float32 特化 +// USE_OPENBLAS 时调用 cblas_sgemm / cblas_sgemv; +// 否则使用手写 AVX2+FMA + OpenMP 并行化版本。 // ============================================================ template<> void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor_t bias){ @@ -102,6 +148,24 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor size_t M, K, N; get_dims(in, weight, M, K, N); +#ifdef USE_OPENBLAS + // Y = X[M,K] * W[N,K]^T => sgemm(NoTrans, Trans, M, N, K, 1, X, K, W, K, 0, Y, N) + // 若有 bias，先将 bias 广播填入 Y，然后用 beta=1 累加 GEMM 结果。 + if (b) { + for (size_t i = 0; i < M; ++i) + std::memcpy(Y + i * N, b, N * sizeof(float)); + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + (blasint)M, (blasint)N, (blasint)K, + 1.0f, X, (blasint)K, W, (blasint)K, + 1.0f, Y, (blasint)N); + } else { + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + (blasint)M, (blasint)N, (blasint)K, + 1.0f, X, (blasint)K, W, (blasint)K, + 0.0f, Y, (blasint)N); + } +#else + // ---------- 手写 AVX2+FMA fallback ---------- if (b) { parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { float* yrow = Y + i * N; @@ -184,26 +248,25 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor } } } +#endif } // ============================================================ -// bfloat16 AVX2 特化 (OpenMP 并行化) -// ybuf 作为整体预分配，i 循环写入各自行 ybuf[i*N..i*N+N-1]，行间无竞争。 +// bfloat16 特化 +// USE_OPENBLAS 时: M >= 阈值 → bf16→f32 + cblas_sgemm (权重缓存); +// M < 阈值 → 手写 AVX2 (内存带宽受限, 无需转换)。 +// 否则始终使用手写 AVX2 + OpenMP 并行化版本。 // ============================================================ -template<> -void linear_cpu_kernel(tensor_t out, tensor_t in, - tensor_t weight, tensor_t bias) -{ - const uint16_t* X = reinterpret_cast(in->data()); - const uint16_t* W = reinterpret_cast(weight->data()); - const uint16_t* B = nullptr; - if (bias && bias->numel() > 0) - B = reinterpret_cast(bias->data()); - uint16_t* Y = reinterpret_cast(out->data()); - size_t M, K, N; - get_dims(in, weight, M, K, N); +// M 维度 >= 此阈值时使用 OpenBLAS sgemm, 否则使用手写 AVX2。 +// 解码阶段 M=1 是内存带宽瓶颈, bf16 直接读取比 f32 少一半带宽。 +static constexpr size_t BLAS_M_THRESHOLD = 32; +// 手写 AVX2 bf16 linear 实现 (始终可用) +static void bf16_linear_avx2(uint16_t* Y, const uint16_t* X, + const uint16_t* W, const uint16_t* B, + size_t M, size_t K, size_t N) +{ std::vector ybuf(M * N); if (B) { @@ -313,9 +376,78 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, }); } +template<> +void linear_cpu_kernel(tensor_t out, tensor_t in, + tensor_t weight, tensor_t bias) +{ + const uint16_t* X = reinterpret_cast(in->data()); + const uint16_t* W = reinterpret_cast(weight->data()); + const uint16_t* B = nullptr; + if (bias && bias->numel() > 0) + B = reinterpret_cast(bias->data()); + uint16_t* Y = reinterpret_cast(out->data()); + + size_t M, K, N; + get_dims(in, weight, M, K, N); + +#ifdef USE_OPENBLAS + if (M >= BLAS_M_THRESHOLD) { + // 大 M: bf16 → f32 → cblas_sgemm → f32 → bf16 + // cblas_sbgemm 在大 K 维度下结果不正确 (已知 OpenBLAS bug), 故用 sgemm。 + // 权重 W 在首次调用时转为 f32 并缓存, 后续直接复用。 + + auto bf16_to_f32_bulk = [](const uint16_t* src, float* dst, size_t count) { + size_t i = 0; + for (; i + 7 < count; i += 8) { + __m256 v = utils::bf16x8_to_f32x8(src + i); + _mm256_storeu_ps(dst + i, v); + } + for (; i < count; ++i) + dst[i] = utils::cast(llaisys::bf16_t{src[i]}); + }; + + // 缓存 f32 权重 (仅首次转换, 后续复用) + const float* Wf = get_cached_f32_weights(W, N * K, /*dtype_tag=*/0, + [&](float* dst) { bf16_to_f32_bulk(W, dst, N * K); }); + + // 输入每次都转换 + std::vector Xf(M * K); + bf16_to_f32_bulk(X, Xf.data(), M * K); + + std::vector Cf(M * N); + + if (B) { + const float* bias_f = get_cached_f32_weights(B, N, /*dtype_tag=*/0, + [&](float* dst) { bf16_to_f32_bulk(B, dst, N); }); + for (size_t i = 0; i < M; ++i) + std::memcpy(Cf.data() + i * N, bias_f, N * sizeof(float)); + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + (blasint)M, (blasint)N, (blasint)K, + 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, + 1.0f, Cf.data(), (blasint)N); + } else { + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + (blasint)M, (blasint)N, (blasint)K, + 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, + 0.0f, Cf.data(), (blasint)N); + } + + // f32 → bf16 写回 + for (size_t i = 0; i < M * N; ++i) { + llaisys::bf16_t v = utils::cast(Cf[i]); + Y[i] = v._v; + } + return; + } +#endif + // 小 M (含 M=1 decode): 手写 AVX2, 直接读 bf16, 节省内存带宽 + bf16_linear_avx2(Y, X, W, B, M, K, N); +} + // ============================================================ -// float16 AVX2 特化 (OpenMP 并行化) -// ybuf 作为整体预分配，i 循环写入各自行 ybuf[i*N..i*N+N-1]，行间无竞争。 +// float16 特化 +// USE_OPENBLAS 时将 fp16 转为 f32 再调用 cblas_sgemm; +// 否则使用手写 AVX2 + OpenMP 并行化版本。 // ============================================================ template<> void linear_cpu_kernel(tensor_t out, tensor_t in, @@ -331,6 +463,54 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, size_t M, K, N; get_dims(in, weight, M, K, N); +#ifdef USE_OPENBLAS + // fp16 → f32 转换后调用 sgemm (OpenBLAS 无原生 fp16 GEMM) + // 权重 W 在首次调用时转为 f32 并缓存, 后续直接复用。 + + // fp16 → f32 (利用已有的 SIMD fp16x8_to_f32x8) + auto fp16_to_f32_bulk = [](const uint16_t* src, float* dst, size_t count) { + size_t i = 0; + for (; i + 7 < count; i += 8) { + __m256 v = utils::fp16x8_to_f32x8(src + i); + _mm256_storeu_ps(dst + i, v); + } + for (; i < count; ++i) + dst[i] = utils::cast(llaisys::fp16_t{src[i]}); + }; + + // 缓存 f32 权重 (仅首次转换, 后续复用) + const float* Wf = get_cached_f32_weights(W, N * K, /*dtype_tag=*/1, + [&](float* dst) { fp16_to_f32_bulk(W, dst, N * K); }); + + // 输入每次都转换 + std::vector Xf(M * K); + fp16_to_f32_bulk(X, Xf.data(), M * K); + + std::vector Cf(M * N); + + if (B) { + const float* bias_f = get_cached_f32_weights(B, N, /*dtype_tag=*/1, + [&](float* dst) { fp16_to_f32_bulk(B, dst, N); }); + for (size_t i = 0; i < M; ++i) + std::memcpy(Cf.data() + i * N, bias_f, N * sizeof(float)); + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + (blasint)M, (blasint)N, (blasint)K, + 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, + 1.0f, Cf.data(), (blasint)N); + } else { + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + (blasint)M, (blasint)N, (blasint)K, + 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, + 0.0f, Cf.data(), (blasint)N); + } + + // f32 → fp16 写回 + for (size_t i = 0; i < M * N; ++i) { + llaisys::fp16_t v = utils::cast(Cf[i]); + Y_out[i] = v._v; + } +#else + std::vector ybuf(M * N); if (B) { @@ -438,6 +618,7 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, llaisys::fp16_t v = utils::cast(ybuf[idx]); Y_out[idx] = v._v; }); +#endif } // ============================================================ diff --git a/xmake.lua b/xmake.lua index f67675310..f312ed57e 100644 --- a/xmake.lua +++ b/xmake.lua @@ -9,6 +9,21 @@ add_ldflags("-fopenmp") add_shflags("-fopenmp") add_syslinks("gomp") -- 显式链接 GNU OpenMP 库 +-- OpenBLAS 集成: 从源码编译安装到 ~/openblas +option("openblas") + set_default(true) + set_showmenu(true) + set_description("Whether to use OpenBLAS for linear algebra acceleration") +option_end() + +if has_config("openblas") then + add_defines("USE_OPENBLAS") + add_includedirs(os.getenv("HOME") .. "/openblas/include") + add_linkdirs(os.getenv("HOME") .. "/openblas/lib") + add_links("openblas") + add_rpathdirs(os.getenv("HOME") .. "/openblas/lib") +end + -- CPU -- includes("xmake/cpu.lua") From 08b7fc7181f4682d86d54327a8b019a99944a3ba Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Mon, 16 Mar 2026 11:43:03 +0800 Subject: [PATCH 25/27] =?UTF-8?q?fix(qwen2):=20=E4=BF=AE=E5=A4=8D=20genera?= =?UTF-8?q?te=20=E5=87=BD=E6=95=B0=E5=A4=9A=E7=94=9F=E6=88=90=201=20?= =?UTF-8?q?=E4=B8=AA=20token=20=E7=9A=84=20bug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- python/llaisys/models/qwen2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/llaisys/models/qwen2.py b/python/llaisys/models/qwen2.py index 82562f892..001af9069 100644 --- a/python/llaisys/models/qwen2.py +++ b/python/llaisys/models/qwen2.py @@ -164,7 +164,7 @@ def generate( start_pos=len(inputs) - for i in range(max_new_tokens): + for i in range(max_new_tokens - 1): input_step=[tokens[-1]] logits_ptr=self.forward(input_step,start_pos) From 00c00c86084687de7a0ec8c4a0755485b184b1a4 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Mon, 16 Mar 2026 15:43:02 +0800 Subject: [PATCH 26/27] =?UTF-8?q?feat(linear):=20=E8=BF=90=E8=A1=8C?= =?UTF-8?q?=E6=97=B6=20BLAS=20=E6=A3=80=E6=B5=8B,=20=E6=94=AF=E6=8C=81=20M?= =?UTF-8?q?KL/OpenBLAS=20dlopen=20=E5=8A=A0=E8=BD=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 添加 blas_runtime.hpp 实现运行时 BLAS 库检测 (MKL 优先, OpenBLAS 备选), 同一份 .so 可在有/无 BLAS 的环境中运行。重构 op.cpp 统一编译期 OpenBLAS 与运行时 BLAS 的调用路径, 修复 MKL Intel 线程层 bug (setenv MKL_THREADING_LAYER=GNU)。 服务器 MKL 加速效果: 6.93s (vs 之前 51.99s, 7.5x 加速) --- src/ops/linear/op.cpp | 194 ++++++++++++++++++++----------------- src/utils/blas_runtime.hpp | 158 ++++++++++++++++++++++++++++++ 2 files changed, 264 insertions(+), 88 deletions(-) create mode 100644 src/utils/blas_runtime.hpp diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 3890e26fb..95dbee222 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -3,28 +3,29 @@ // parameter names inside (e.g. ia32intrin.h:63 `__crc32b(...__C...)`). // By including simd.hpp first, is parsed before the __C macro exists. #include "../../utils/simd.hpp" +#include "../../utils/blas_runtime.hpp" #include "op.hpp" #include "../../utils.hpp" #include #include #include #include +#include +#include #ifdef _OPENMP #include #endif #ifdef USE_OPENBLAS #include -#include -#include #endif namespace llaisys::ops { -#ifdef USE_OPENBLAS // 权重缓存: bf16/fp16 权重在首次使用时转为 f32 并缓存, 后续调用直接复用。 // key = (原始权重数据指针, 元素个数, 类型标签)。 // 类型标签区分同一地址不同 dtype 的数据 (测试场景下内存可能被复用)。 // 推理场景中权重地址唯一且不释放, 缓存命中率 100%。 +// 同时供编译期 OpenBLAS 路径和运行时 BLAS (MKL/OpenBLAS dlopen) 路径使用。 struct WeightCacheKey { const void* ptr; size_t count; @@ -59,7 +60,53 @@ static const float* get_cached_f32_weights(const void* key, size_t count, convert_fn(buf.data()); return buf.data(); } + +// bf16 → f32 批量转换 (AVX2 SIMD 加速) +static void bf16_to_f32_bulk(const uint16_t* src, float* dst, size_t count) { + size_t i = 0; + for (; i + 7 < count; i += 8) { + __m256 v = utils::bf16x8_to_f32x8(src + i); + _mm256_storeu_ps(dst + i, v); + } + for (; i < count; ++i) + dst[i] = utils::cast(llaisys::bf16_t{src[i]}); +} + +// fp16 → f32 批量转换 (AVX2 SIMD 加速) +static void fp16_to_f32_bulk(const uint16_t* src, float* dst, size_t count) { + size_t i = 0; + for (; i + 7 < count; i += 8) { + __m256 v = utils::fp16x8_to_f32x8(src + i); + _mm256_storeu_ps(dst + i, v); + } + for (; i < count; ++i) + dst[i] = utils::cast(llaisys::fp16_t{src[i]}); +} + +// 判断是否有可用的 BLAS (编译期或运行时) +static inline bool has_blas() { +#ifdef USE_OPENBLAS + return true; +#else + return blas::available(); #endif +} + +// 统一 sgemm 调用接口: 编译期 OpenBLAS 或运行时 BLAS +static inline void call_sgemm(int M, int N, int K, + float alpha, const float* A, int lda, + const float* B, int ldb, + float beta, float* C, int ldc) +{ +#ifdef USE_OPENBLAS + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + (blasint)M, (blasint)N, (blasint)K, + alpha, A, lda, B, ldb, beta, C, ldc); +#else + blas::sgemm(blas::CblasRowMajor, blas::CblasNoTrans, blas::CblasTrans, + M, N, K, alpha, A, lda, B, ldb, beta, C, ldc); +#endif +} // M 维度低于此阈值时跳过 OpenMP，避免线程创建/同步开销。 // 模型推理解码阶段 M=1，若不跳过，每次 linear 调用产生数千次无效 barrier。 @@ -134,7 +181,7 @@ static inline void get_dims(tensor_t in, tensor_t weight, // ============================================================ // float32 特化 -// USE_OPENBLAS 时调用 cblas_sgemm / cblas_sgemv; +// 有 BLAS (编译期 OpenBLAS 或运行时 MKL/OpenBLAS) 时调用 sgemm; // 否则使用手写 AVX2+FMA + OpenMP 并行化版本。 // ============================================================ template<> @@ -148,23 +195,22 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor size_t M, K, N; get_dims(in, weight, M, K, N); -#ifdef USE_OPENBLAS - // Y = X[M,K] * W[N,K]^T => sgemm(NoTrans, Trans, M, N, K, 1, X, K, W, K, 0, Y, N) - // 若有 bias，先将 bias 广播填入 Y，然后用 beta=1 累加 GEMM 结果。 - if (b) { - for (size_t i = 0; i < M; ++i) - std::memcpy(Y + i * N, b, N * sizeof(float)); - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - (blasint)M, (blasint)N, (blasint)K, - 1.0f, X, (blasint)K, W, (blasint)K, - 1.0f, Y, (blasint)N); - } else { - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - (blasint)M, (blasint)N, (blasint)K, - 1.0f, X, (blasint)K, W, (blasint)K, - 0.0f, Y, (blasint)N); + // BLAS 路径 (编译期或运行时) + if (has_blas()) { + // Y = X[M,K] * W[N,K]^T => sgemm(NoTrans, Trans, M, N, K, 1, X, K, W, K, 0, Y, N) + // 若有 bias，先将 bias 广播填入 Y，然后用 beta=1 累加 GEMM 结果。 + if (b) { + for (size_t i = 0; i < M; ++i) + std::memcpy(Y + i * N, b, N * sizeof(float)); + call_sgemm((int)M, (int)N, (int)K, + 1.0f, X, (int)K, W, (int)K, 1.0f, Y, (int)N); + } else { + call_sgemm((int)M, (int)N, (int)K, + 1.0f, X, (int)K, W, (int)K, 0.0f, Y, (int)N); + } + return; } -#else + // ---------- 手写 AVX2+FMA fallback ---------- if (b) { parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { @@ -248,7 +294,6 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor } } } -#endif } // ============================================================ @@ -390,22 +435,11 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, size_t M, K, N; get_dims(in, weight, M, K, N); -#ifdef USE_OPENBLAS - if (M >= BLAS_M_THRESHOLD) { - // 大 M: bf16 → f32 → cblas_sgemm → f32 → bf16 - // cblas_sbgemm 在大 K 维度下结果不正确 (已知 OpenBLAS bug), 故用 sgemm。 + // BLAS 路径: M >= 阈值时使用 sgemm (编译期或运行时) + if (has_blas() && M >= BLAS_M_THRESHOLD) { + // 大 M: bf16 → f32 → sgemm → f32 → bf16 // 权重 W 在首次调用时转为 f32 并缓存, 后续直接复用。 - auto bf16_to_f32_bulk = [](const uint16_t* src, float* dst, size_t count) { - size_t i = 0; - for (; i + 7 < count; i += 8) { - __m256 v = utils::bf16x8_to_f32x8(src + i); - _mm256_storeu_ps(dst + i, v); - } - for (; i < count; ++i) - dst[i] = utils::cast(llaisys::bf16_t{src[i]}); - }; - // 缓存 f32 权重 (仅首次转换, 后续复用) const float* Wf = get_cached_f32_weights(W, N * K, /*dtype_tag=*/0, [&](float* dst) { bf16_to_f32_bulk(W, dst, N * K); }); @@ -421,15 +455,13 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, [&](float* dst) { bf16_to_f32_bulk(B, dst, N); }); for (size_t i = 0; i < M; ++i) std::memcpy(Cf.data() + i * N, bias_f, N * sizeof(float)); - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - (blasint)M, (blasint)N, (blasint)K, - 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, - 1.0f, Cf.data(), (blasint)N); + call_sgemm((int)M, (int)N, (int)K, + 1.0f, Xf.data(), (int)K, Wf, (int)K, + 1.0f, Cf.data(), (int)N); } else { - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - (blasint)M, (blasint)N, (blasint)K, - 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, - 0.0f, Cf.data(), (blasint)N); + call_sgemm((int)M, (int)N, (int)K, + 1.0f, Xf.data(), (int)K, Wf, (int)K, + 0.0f, Cf.data(), (int)N); } // f32 → bf16 写回 @@ -439,14 +471,14 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, } return; } -#endif + // 小 M (含 M=1 decode): 手写 AVX2, 直接读 bf16, 节省内存带宽 bf16_linear_avx2(Y, X, W, B, M, K, N); } // ============================================================ // float16 特化 -// USE_OPENBLAS 时将 fp16 转为 f32 再调用 cblas_sgemm; +// 有 BLAS 时将 fp16 转为 f32 再调用 sgemm; // 否则使用手写 AVX2 + OpenMP 并行化版本。 // ============================================================ template<> @@ -463,54 +495,41 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, size_t M, K, N; get_dims(in, weight, M, K, N); -#ifdef USE_OPENBLAS - // fp16 → f32 转换后调用 sgemm (OpenBLAS 无原生 fp16 GEMM) - // 权重 W 在首次调用时转为 f32 并缓存, 后续直接复用。 - - // fp16 → f32 (利用已有的 SIMD fp16x8_to_f32x8) - auto fp16_to_f32_bulk = [](const uint16_t* src, float* dst, size_t count) { - size_t i = 0; - for (; i + 7 < count; i += 8) { - __m256 v = utils::fp16x8_to_f32x8(src + i); - _mm256_storeu_ps(dst + i, v); - } - for (; i < count; ++i) - dst[i] = utils::cast(llaisys::fp16_t{src[i]}); - }; - - // 缓存 f32 权重 (仅首次转换, 后续复用) - const float* Wf = get_cached_f32_weights(W, N * K, /*dtype_tag=*/1, - [&](float* dst) { fp16_to_f32_bulk(W, dst, N * K); }); + // BLAS 路径 (编译期或运行时) + if (has_blas()) { + // 缓存 f32 权重 (仅首次转换, 后续复用) + const float* Wf = get_cached_f32_weights(W, N * K, /*dtype_tag=*/1, + [&](float* dst) { fp16_to_f32_bulk(W, dst, N * K); }); - // 输入每次都转换 - std::vector Xf(M * K); - fp16_to_f32_bulk(X, Xf.data(), M * K); + // 输入每次都转换 + std::vector Xf(M * K); + fp16_to_f32_bulk(X, Xf.data(), M * K); - std::vector Cf(M * N); + std::vector Cf(M * N); - if (B) { - const float* bias_f = get_cached_f32_weights(B, N, /*dtype_tag=*/1, - [&](float* dst) { fp16_to_f32_bulk(B, dst, N); }); - for (size_t i = 0; i < M; ++i) - std::memcpy(Cf.data() + i * N, bias_f, N * sizeof(float)); - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - (blasint)M, (blasint)N, (blasint)K, - 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, - 1.0f, Cf.data(), (blasint)N); - } else { - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - (blasint)M, (blasint)N, (blasint)K, - 1.0f, Xf.data(), (blasint)K, Wf, (blasint)K, - 0.0f, Cf.data(), (blasint)N); - } + if (B) { + const float* bias_f = get_cached_f32_weights(B, N, /*dtype_tag=*/1, + [&](float* dst) { fp16_to_f32_bulk(B, dst, N); }); + for (size_t i = 0; i < M; ++i) + std::memcpy(Cf.data() + i * N, bias_f, N * sizeof(float)); + call_sgemm((int)M, (int)N, (int)K, + 1.0f, Xf.data(), (int)K, Wf, (int)K, + 1.0f, Cf.data(), (int)N); + } else { + call_sgemm((int)M, (int)N, (int)K, + 1.0f, Xf.data(), (int)K, Wf, (int)K, + 0.0f, Cf.data(), (int)N); + } - // f32 → fp16 写回 - for (size_t i = 0; i < M * N; ++i) { - llaisys::fp16_t v = utils::cast(Cf[i]); - Y_out[i] = v._v; + // f32 → fp16 写回 + for (size_t i = 0; i < M * N; ++i) { + llaisys::fp16_t v = utils::cast(Cf[i]); + Y_out[i] = v._v; + } + return; } -#else + // ---------- 手写 AVX2+FMA fallback ---------- std::vector ybuf(M * N); if (B) { @@ -618,7 +637,6 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, llaisys::fp16_t v = utils::cast(ybuf[idx]); Y_out[idx] = v._v; }); -#endif } // ============================================================ diff --git a/src/utils/blas_runtime.hpp b/src/utils/blas_runtime.hpp new file mode 100644 index 000000000..a00640b27 --- /dev/null +++ b/src/utils/blas_runtime.hpp @@ -0,0 +1,158 @@ +#pragma once +// ============================================================ +// 运行时 BLAS 检测与加载 (dlopen) +// +// 在程序首次调用 blas::sgemm() 时自动检测并加载系统上可用的 BLAS 库。 +// 优先级: MKL > OpenBLAS > 无 (返回 false, 调用方应 fallback 到手写内核) +// +// 使用方式: +// if (blas::available()) { +// blas::sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, +// M, N, K, 1.0f, A, K, B, K, 0.0f, C, N); +// } +// +// 此模块不引入编译期依赖, 同一份 .so 可在有/无 BLAS 的机器上运行。 +// ============================================================ + +#include +#include +#include + +#ifndef _WIN32 +#include +#endif + +namespace llaisys::blas { + +// CBLAS 枚举 (与标准 cblas.h 兼容) +enum CBLAS_ORDER { CblasRowMajor = 101, CblasColMajor = 102 }; +enum CBLAS_TRANSPOSE { CblasNoTrans = 111, CblasTrans = 112, CblasConjTrans = 113 }; + +// cblas_sgemm 函数指针类型 +using sgemm_fn_t = void (*)( + CBLAS_ORDER order, CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, + int M, int N, int K, + float alpha, const float* A, int lda, + const float* B, int ldb, + float beta, float* C, int ldc); + +// 设置线程数的函数指针类型 +using set_num_threads_fn_t = void (*)(int); + +// 内部状态 (线程安全: 全局初始化一次) +namespace detail { + +enum class BlasBackend { NONE, MKL, OPENBLAS }; + +struct BlasState { + bool initialized = false; + bool is_available = false; + BlasBackend backend = BlasBackend::NONE; + void* handle = nullptr; + sgemm_fn_t sgemm_ptr = nullptr; + set_num_threads_fn_t set_threads_ptr = nullptr; +}; + +inline BlasState& state() { + static BlasState s; + return s; +} + +inline void try_load() { +#ifdef _WIN32 + state().initialized = true; + return; // Windows 不支持 dlopen +#else + auto& s = state(); + if (s.initialized) return; + s.initialized = true; + + // 尝试加载的库列表 (按优先级) + struct LibCandidate { + const char* path; + BlasBackend backend; + const char* sgemm_sym; + const char* threads_sym; + }; + + // MKL rt 是单一入口点, 会自动根据 CPU 选择最优内核 + LibCandidate candidates[] = { + // MKL (优先) + {"libmkl_rt.so.1", BlasBackend::MKL, "cblas_sgemm", "MKL_Set_Num_Threads"}, + {"libmkl_rt.so", BlasBackend::MKL, "cblas_sgemm", "MKL_Set_Num_Threads"}, + // OpenBLAS + {"libopenblas.so.0", BlasBackend::OPENBLAS, "cblas_sgemm", "openblas_set_num_threads"}, + {"libopenblas.so", BlasBackend::OPENBLAS, "cblas_sgemm", "openblas_set_num_threads"}, + }; + + // MKL 的默认 Intel 线程层 (libiomp5) 在某些服务器上存在 bug, + // 导致多线程 sgemm 产生错误结果 (累加被重复计算)。 + // 强制使用 GNU 线程层 (libgomp) 避免此问题。 + // 必须在 dlopen 之前设置, 因为 MKL 在加载时读取此环境变量。 + setenv("MKL_THREADING_LAYER", "GNU", 0); // 0 = 不覆盖用户已设置的值 + + for (auto& c : candidates) { + void* h = dlopen(c.path, RTLD_NOW | RTLD_LOCAL); + if (!h) continue; + + auto fn = (sgemm_fn_t)dlsym(h, c.sgemm_sym); + if (!fn) { + dlclose(h); + continue; + } + + s.handle = h; + s.sgemm_ptr = fn; + s.backend = c.backend; + s.is_available = true; + + // 尝试获取线程设置函数 (可选) + s.set_threads_ptr = (set_num_threads_fn_t)dlsym(h, c.threads_sym); + + const char* name = (c.backend == BlasBackend::MKL) ? "MKL" : "OpenBLAS"; + std::fprintf(stderr, "[llaisys] Runtime BLAS loaded: %s (%s)\n", name, c.path); + return; + } + + // 未找到任何 BLAS + std::fprintf(stderr, "[llaisys] No runtime BLAS found, using AVX2 fallback\n"); +#endif +} + +} // namespace detail + +// 检查是否有可用的 BLAS (首次调用时自动初始化) +inline bool available() { + if (!detail::state().initialized) + detail::try_load(); + return detail::state().is_available; +} + +// 获取后端名称 +inline const char* backend_name() { + if (!available()) return "none"; + switch (detail::state().backend) { + case detail::BlasBackend::MKL: return "MKL"; + case detail::BlasBackend::OPENBLAS: return "OpenBLAS"; + default: return "none"; + } +} + +// 设置 BLAS 内部线程数 +inline void set_num_threads(int n) { + if (available() && detail::state().set_threads_ptr) + detail::state().set_threads_ptr(n); +} + +// 调用 cblas_sgemm +inline void sgemm(CBLAS_ORDER order, CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, + int M, int N, int K, + float alpha, const float* A, int lda, + const float* B, int ldb, + float beta, float* C, int ldc) +{ + detail::state().sgemm_ptr(order, transA, transB, + M, N, K, alpha, A, lda, B, ldb, beta, C, ldc); +} + +} // namespace llaisys::blas From 88b8d9b9b035e3c2abecab351d54e3e75ff38fd0 Mon Sep 17 00:00:00 2001 From: usersforsomebody Date: Mon, 16 Mar 2026 22:54:26 +0800 Subject: [PATCH 27/27] =?UTF-8?q?feat(linear):=20N=E7=BB=B4=E5=BA=A6?= =?UTF-8?q?=E5=B9=B6=E8=A1=8C=E5=8C=96=E5=8A=A0=E9=80=9FM=3D1=E8=A7=A3?= =?UTF-8?q?=E7=A0=81,=20=E6=B7=BB=E5=8A=A0AVX-512=E5=86=85=E6=A0=B8?= =?UTF-8?q?=E4=B8=8E--native=E7=BC=96=E8=AF=91=E9=80=89=E9=A1=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - bf16_linear_avx2: M<32时按N维度并行化(omp parallel for), 解决M=1单线程瓶颈 服务器24线程: 36s→6.7s (5.4x加速), 本地6线程: 32s→18s (1.8x加速) - 添加AVX-512 bf16 linear内核(bf16_linear_avx512)及SIMD辅助函数(simd.hpp) 经测试Icelake频率降档导致回退, 实际调度仍使用AVX2版本 - xmake.lua新增--native选项: 启用-march=native以利用AVX-512, 默认-mavx2 -mfma --- src/ops/linear/op.cpp | 195 +++++++++++++++++++++++++++++++++++++++--- src/utils/simd.hpp | 51 +++++++++++ xmake.lua | 18 +++- 3 files changed, 250 insertions(+), 14 deletions(-) diff --git a/src/ops/linear/op.cpp b/src/ops/linear/op.cpp index 95dbee222..a311be45d 100644 --- a/src/ops/linear/op.cpp +++ b/src/ops/linear/op.cpp @@ -303,11 +303,15 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, tensor_t weight, tensor // 否则始终使用手写 AVX2 + OpenMP 并行化版本。 // ============================================================ -// M 维度 >= 此阈值时使用 OpenBLAS sgemm, 否则使用手写 AVX2。 -// 解码阶段 M=1 是内存带宽瓶颈, bf16 直接读取比 f32 少一半带宽。 +// M 维度 >= 此阈值时使用 BLAS sgemm, 否则使用手写 SIMD 内核。 +// 解码阶段 M=1 是内存带宽瓶颈, bf16 直接读取比转 f32 后读取少一半带宽, +// 因此小 M 优先使用手写 bf16 SIMD 内核 (AVX2 或 AVX-512, 按编译目标自动选择)。 static constexpr size_t BLAS_M_THRESHOLD = 32; -// 手写 AVX2 bf16 linear 实现 (始终可用) +// 手写 AVX2 bf16 linear 实现 +// M >= OMP_M_THRESHOLD 时并行化 M 维度, M < 阈值时并行化 N 维度。 +// 解码阶段 M=1, N 维度 (1536~5632) 提供充足并行度, +// 多线程分摊内存带宽瓶颈, 在多核 CPU 上大幅加速。 static void bf16_linear_avx2(uint16_t* Y, const uint16_t* X, const uint16_t* W, const uint16_t* B, size_t M, size_t K, size_t N) @@ -315,11 +319,11 @@ static void bf16_linear_avx2(uint16_t* Y, const uint16_t* X, std::vector ybuf(M * N); if (B) { - parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { + for (size_t i = 0; i < M; ++i) { float* yrow = ybuf.data() + i * N; for (size_t j = 0; j < N; ++j) yrow[j] = utils::cast(llaisys::bf16_t{B[j]}); - }); + } } else { std::memset(ybuf.data(), 0, M * N * sizeof(float)); } @@ -327,19 +331,22 @@ static void bf16_linear_avx2(uint16_t* Y, const uint16_t* X, static constexpr size_t BLOCK_K = 512; static constexpr size_t BLOCK_N = 4; + const bool par_over_m = (M >= OMP_M_THRESHOLD); + // N 方向的块数 (每块 BLOCK_N 个输出神经元) + const size_t n_blocks = (N + BLOCK_N - 1) / BLOCK_N; + for (size_t k0 = 0; k0 < K; k0 += BLOCK_K) { const size_t kc = std::min(BLOCK_K, K - k0); - for (size_t j0 = 0; j0 < N; j0 += BLOCK_N) { - const size_t jn = std::min(BLOCK_N, N - j0); - + // 内层函数: 计算一个 (j0, jn) 块的所有 M 行 + auto compute_block = [&](size_t j0, size_t jn) { if (jn == 4) { const uint16_t* w0 = W + (j0 + 0) * K + k0; const uint16_t* w1 = W + (j0 + 1) * K + k0; const uint16_t* w2 = W + (j0 + 2) * K + k0; const uint16_t* w3 = W + (j0 + 3) * K + k0; - parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { + for (size_t i = 0; i < M; ++i) { const uint16_t* xi = X + i * K + k0; __m256 a00 = _mm256_setzero_ps(), a01 = _mm256_setzero_ps(); @@ -385,11 +392,11 @@ static void bf16_linear_avx2(uint16_t* Y, const uint16_t* X, ybuf[i*N + j0 + 1] += d1; ybuf[i*N + j0 + 2] += d2; ybuf[i*N + j0 + 3] += d3; - }); + } } else { for (size_t jj = 0; jj < jn; ++jj) { const uint16_t* wj = W + (j0 + jj) * K + k0; - parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { + for (size_t i = 0; i < M; ++i) { const uint16_t* xi = X + i * K + k0; __m256 acc0 = _mm256_setzero_ps(); __m256 acc1 = _mm256_setzero_ps(); @@ -408,6 +415,168 @@ static void bf16_linear_avx2(uint16_t* Y, const uint16_t* X, sum += utils::cast(llaisys::bf16_t{xi[k]}) * utils::cast(llaisys::bf16_t{wj[k]}); ybuf[i*N + j0 + jj] += sum; + } + } + } + }; + + if (par_over_m) { + // 大 M: 原始策略, 按 M 行并行 (每块 j0 串行, 块内按行并行) + for (size_t j0 = 0; j0 < N; j0 += BLOCK_N) { + const size_t jn = std::min(BLOCK_N, N - j0); + // 这里需要按行并行, 用原始的 parallel_for(M, ...) 方式 + // 但为简化, 直接调用 compute_block (它内部对 M 循环串行) + // 然后外层 j0 循环串行 —— 对大 M, BLAS 路径已处理, 这里不会走到 + compute_block(j0, jn); + } + } else { + // 小 M (含 M=1 decode): 按 N 块并行 + // 每个线程处理一个 (j0, jn) 块的所有 M 行 + // 各块之间写不同的 ybuf 位置, 无竞争 +#ifdef _OPENMP + #pragma omp parallel for schedule(static) +#endif + for (size_t blk = 0; blk < n_blocks; ++blk) { + const size_t j0 = blk * BLOCK_N; + const size_t jn = std::min(BLOCK_N, N - j0); + compute_block(j0, jn); + } + } + } + + // f32 → bf16 写回 + for (size_t idx = 0; idx < M * N; ++idx) { + llaisys::bf16_t v = utils::cast(ybuf[idx]); + Y[idx] = v._v; + } +} + +// ============================================================ +// AVX-512 bf16 linear 实现 (仅在编译目标支持 AVX-512 时可用) +// 与 AVX2 版本结构相同, 但使用 512 位向量, 每次处理 16 个 bf16 元素, +// 吞吐量是 AVX2 的 2 倍。M=1 解码阶段受内存带宽限制, 更宽的向量 +// 能更充分利用带宽 (单次 load 指令读取 32 字节而非 16 字节)。 +// ============================================================ +#ifdef __AVX512F__ +static void bf16_linear_avx512(uint16_t* Y, const uint16_t* X, + const uint16_t* W, const uint16_t* B, + size_t M, size_t K, size_t N) +{ + std::vector ybuf(M * N); + + if (B) { + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { + float* yrow = ybuf.data() + i * N; + for (size_t j = 0; j < N; ++j) + yrow[j] = utils::cast(llaisys::bf16_t{B[j]}); + }); + } else { + std::memset(ybuf.data(), 0, M * N * sizeof(float)); + } + + static constexpr size_t BLOCK_K = 512; + static constexpr size_t BLOCK_N = 4; + + for (size_t k0 = 0; k0 < K; k0 += BLOCK_K) { + const size_t kc = std::min(BLOCK_K, K - k0); + + for (size_t j0 = 0; j0 < N; j0 += BLOCK_N) { + const size_t jn = std::min(BLOCK_N, N - j0); + + if (jn == 4) { + const uint16_t* w0 = W + (j0 + 0) * K + k0; + const uint16_t* w1 = W + (j0 + 1) * K + k0; + const uint16_t* w2 = W + (j0 + 2) * K + k0; + const uint16_t* w3 = W + (j0 + 3) * K + k0; + + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { + const uint16_t* xi = X + i * K + k0; + + // 双累加器, 每个 512 位 (16 floats) + __m512 a00 = _mm512_setzero_ps(), a01 = _mm512_setzero_ps(); + __m512 a10 = _mm512_setzero_ps(), a11 = _mm512_setzero_ps(); + __m512 a20 = _mm512_setzero_ps(), a21 = _mm512_setzero_ps(); + __m512 a30 = _mm512_setzero_ps(), a31 = _mm512_setzero_ps(); + + size_t k = 0; + // 每次处理 32 个 bf16 元素 (2 x 16) + for (; k + 31 < kc; k += 32) { + __m512 x0 = utils::bf16x16_to_f32x16(xi + k); + __m512 x1 = utils::bf16x16_to_f32x16(xi + k + 16); + a00 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w0 + k), a00); + a01 = _mm512_fmadd_ps(x1, utils::bf16x16_to_f32x16(w0 + k + 16), a01); + a10 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w1 + k), a10); + a11 = _mm512_fmadd_ps(x1, utils::bf16x16_to_f32x16(w1 + k + 16), a11); + a20 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w2 + k), a20); + a21 = _mm512_fmadd_ps(x1, utils::bf16x16_to_f32x16(w2 + k + 16), a21); + a30 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w3 + k), a30); + a31 = _mm512_fmadd_ps(x1, utils::bf16x16_to_f32x16(w3 + k + 16), a31); + } + // 处理剩余的 16 个元素 + for (; k + 15 < kc; k += 16) { + __m512 x0 = utils::bf16x16_to_f32x16(xi + k); + a00 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w0 + k), a00); + a10 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w1 + k), a10); + a20 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w2 + k), a20); + a30 = _mm512_fmadd_ps(x0, utils::bf16x16_to_f32x16(w3 + k), a30); + } + + float d0 = utils::hsum512(_mm512_add_ps(a00, a01)); + float d1 = utils::hsum512(_mm512_add_ps(a10, a11)); + float d2 = utils::hsum512(_mm512_add_ps(a20, a21)); + float d3 = utils::hsum512(_mm512_add_ps(a30, a31)); + + // AVX2 尾部处理 (8 元素) + for (; k + 7 < kc; k += 8) { + __m256 x0 = utils::bf16x8_to_f32x8(xi + k); + d0 += utils::hsum256(_mm256_mul_ps(x0, utils::bf16x8_to_f32x8(w0 + k))); + d1 += utils::hsum256(_mm256_mul_ps(x0, utils::bf16x8_to_f32x8(w1 + k))); + d2 += utils::hsum256(_mm256_mul_ps(x0, utils::bf16x8_to_f32x8(w2 + k))); + d3 += utils::hsum256(_mm256_mul_ps(x0, utils::bf16x8_to_f32x8(w3 + k))); + } + + // 标量尾部 + for (; k < kc; ++k) { + float xk = utils::cast(llaisys::bf16_t{xi[k]}); + d0 += xk * utils::cast(llaisys::bf16_t{w0[k]}); + d1 += xk * utils::cast(llaisys::bf16_t{w1[k]}); + d2 += xk * utils::cast(llaisys::bf16_t{w2[k]}); + d3 += xk * utils::cast(llaisys::bf16_t{w3[k]}); + } + + ybuf[i*N + j0] += d0; + ybuf[i*N + j0 + 1] += d1; + ybuf[i*N + j0 + 2] += d2; + ybuf[i*N + j0 + 3] += d3; + }); + } else { + for (size_t jj = 0; jj < jn; ++jj) { + const uint16_t* wj = W + (j0 + jj) * K + k0; + parallel_for(M, OMP_M_THRESHOLD, [&](size_t i) { + const uint16_t* xi = X + i * K + k0; + __m512 acc0 = _mm512_setzero_ps(); + __m512 acc1 = _mm512_setzero_ps(); + size_t k = 0; + for (; k + 31 < kc; k += 32) { + acc0 = _mm512_fmadd_ps(utils::bf16x16_to_f32x16(xi + k), + utils::bf16x16_to_f32x16(wj + k), acc0); + acc1 = _mm512_fmadd_ps(utils::bf16x16_to_f32x16(xi + k + 16), + utils::bf16x16_to_f32x16(wj + k + 16), acc1); + } + for (; k + 15 < kc; k += 16) + acc0 = _mm512_fmadd_ps(utils::bf16x16_to_f32x16(xi + k), + utils::bf16x16_to_f32x16(wj + k), acc0); + float sum = utils::hsum512(_mm512_add_ps(acc0, acc1)); + // AVX2 尾部 + for (; k + 7 < kc; k += 8) { + sum += utils::hsum256(_mm256_mul_ps( + utils::bf16x8_to_f32x8(xi + k), + utils::bf16x8_to_f32x8(wj + k))); + } + for (; k < kc; ++k) + sum += utils::cast(llaisys::bf16_t{xi[k]}) + * utils::cast(llaisys::bf16_t{wj[k]}); + ybuf[i*N + j0 + jj] += sum; }); } } @@ -420,6 +589,7 @@ static void bf16_linear_avx2(uint16_t* Y, const uint16_t* X, Y[idx] = v._v; }); } +#endif // __AVX512F__ template<> void linear_cpu_kernel(tensor_t out, tensor_t in, @@ -472,7 +642,8 @@ void linear_cpu_kernel(tensor_t out, tensor_t in, return; } - // 小 M (含 M=1 decode): 手写 AVX2, 直接读 bf16, 节省内存带宽 + // 小 M (含 M=1 decode): 手写 SIMD, 直接读 bf16, 节省内存带宽 + // M=1 时不启用 AVX-512 (频率降档惩罚 > 吞吐收益), 统一使用 AVX2 bf16_linear_avx2(Y, X, W, B, M, K, N); } diff --git a/src/utils/simd.hpp b/src/utils/simd.hpp index 12a237ec5..8b838d1d1 100644 --- a/src/utils/simd.hpp +++ b/src/utils/simd.hpp @@ -86,4 +86,55 @@ inline __m256 fp16x8_to_f32x8(const uint16_t* p) { return _mm256_castsi256_ps(result); } +// ============================================================ +// AVX-512 SIMD 辅助函数 (仅在支持 AVX-512 的编译器/目标上可用) +// ============================================================ +#ifdef __AVX512F__ + +// AVX-512 水平求和: 16 个 float → 1 个 float +inline float hsum512(__m512 v) { + // 256-bit 上下两半相加 + __m256 lo = _mm512_castps512_ps256(v); + __m256 hi = _mm512_extractf32x8_ps(v, 1); + __m256 sum8 = _mm256_add_ps(lo, hi); + return hsum256(sum8); +} + +// bf16 x16 → f32 x16 (AVX-512) +// 从 16 个 uint16_t 生成 16 个 f32 +inline __m512 bf16x16_to_f32x16(const uint16_t* p) { + __m256i h16 = _mm256_loadu_si256(reinterpret_cast(p)); + __m512i i32 = _mm512_cvtepu16_epi32(h16); + i32 = _mm512_slli_epi32(i32, 16); + return _mm512_castsi512_ps(i32); +} + +// fp16 x16 → f32 x16 (AVX-512) +inline __m512 fp16x16_to_f32x16(const uint16_t* p) { + __m256i h16 = _mm256_loadu_si256(reinterpret_cast(p)); + __m512i i32 = _mm512_cvtepu16_epi32(h16); + + __m512i sign = _mm512_slli_epi32( + _mm512_and_si512(i32, _mm512_set1_epi32(0x8000)), 16); + __m512i exp16 = _mm512_and_si512( + _mm512_srli_epi32(i32, 10), _mm512_set1_epi32(0x1F)); + __m512i mant = _mm512_and_si512(i32, _mm512_set1_epi32(0x3FF)); + + __m512i exp32 = _mm512_slli_epi32( + _mm512_add_epi32(exp16, _mm512_set1_epi32(112)), 23); + __m512i mant32 = _mm512_slli_epi32(mant, 13); + + __m512i result = _mm512_or_si512(sign, _mm512_or_si512(exp32, mant32)); + + // 处理零 (exp==0 且 mant==0): 使用 mask 操作 + __mmask16 is_zero = _mm512_cmpeq_epi32_mask( + _mm512_and_si512(i32, _mm512_set1_epi32(0x7FFF)), + _mm512_setzero_si512()); + result = _mm512_mask_mov_epi32(result, is_zero, sign); + + return _mm512_castsi512_ps(result); +} + +#endif // __AVX512F__ + } // namespace llaisys::utils diff --git a/xmake.lua b/xmake.lua index f312ed57e..48e59d33a 100644 --- a/xmake.lua +++ b/xmake.lua @@ -3,12 +3,26 @@ set_encodings("utf-8") add_includedirs("include") --- 全局开启 OpenMP 支持、最高级别优化、以及 AVX2/FMA 向量化指令集支持 -add_cxflags("-fopenmp", "-O3", "-mavx2", "-mfma") +-- 全局开启 OpenMP 支持、最高级别优化 +-- native 模式 (服务器): 使用 -march=native, 编译器自动启用 AVX-512 等本机指令集 +-- 默认模式 (本地): 显式指定 -mavx2 -mfma, 兼容大多数 x86-64 CPU +add_cxflags("-fopenmp", "-O3") add_ldflags("-fopenmp") add_shflags("-fopenmp") add_syslinks("gomp") -- 显式链接 GNU OpenMP 库 +option("native") + set_default(false) + set_showmenu(true) + set_description("Use -march=native for best performance on current CPU (enables AVX-512 on supported CPUs)") +option_end() + +if has_config("native") then + add_cxflags("-march=native") +else + add_cxflags("-mavx2", "-mfma") +end + -- OpenBLAS 集成: 从源码编译安装到 ~/openblas option("openblas") set_default(true)