feat(mgb/opr): add lsq opr

GitOrigin-RevId: 45494a2b57

dnn/include/megdnn/oprs/nn.h

@@ -1741,6 +1741,67 @@ protected:
                     const TensorLayout& grad_s, size_t workspace_in_bytes);
 };
 
+class LSQBase : public OperatorBase {
+    DEF_OPR_IMPL_CTOR(LSQBase, OperatorBase);
+    DEF_OPR_PARAM(LSQ);
+
+protected:
+    void deduce_layout_fwd(const TensorLayout& input, TensorLayout& output);
+    void check_layout_fwd(const TensorLayout& input, const TensorLayout& scale,
+                          const TensorLayout& zero_point,
+                          const TensorLayout& grad_scale,
+                          const TensorLayout& output);
+};
+
+class LSQForward : public LSQBase {
+    DEF_OPR_IMPL(LSQForward, LSQBase, 4, 1);
+
+public:
+    virtual void exec(_megdnn_tensor_in input, _megdnn_tensor_in scale,
+                      _megdnn_tensor_in zero_point,
+                      _megdnn_tensor_in grad_scale, _megdnn_tensor_out output,
+                      _megdnn_workspace workspace) = 0;
+    void deduce_layout(const TensorLayout& input, const TensorLayout& scale,
+                       const TensorLayout& zero_point,
+                       const TensorLayout& grad_scale, TensorLayout& output);
+    virtual size_t get_workspace_in_bytes(const TensorLayout& input,
+                                          const TensorLayout& scale,
+                                          const TensorLayout& zero_point,
+                                          const TensorLayout& grad_scale,
+                                          const TensorLayout& output) = 0;
+
+protected:
+    void check_exec(const TensorLayout& input, const TensorLayout& scale,
+                    const TensorLayout& zero_point,
+                    const TensorLayout& grad_scale, const TensorLayout& output,
+                    size_t workspace_in_bytes);
+};
+using LSQ = LSQForward;
+
+class LSQBackward : public LSQBase {
+    DEF_OPR_IMPL(LSQBackward, LSQBase, 5, 2);
+
+public:
+    virtual void exec(_megdnn_tensor_in diff, _megdnn_tensor_in input,
+                      _megdnn_tensor_in scale, _megdnn_tensor_in zero_point,
+                      _megdnn_tensor_in grad_scale, _megdnn_tensor_out grad_x,
+                      _megdnn_tensor_out grad_s,
+                      _megdnn_workspace workspace) = 0;
+    virtual size_t get_workspace_in_bytes(const TensorLayout& diff,
+                                          const TensorLayout& input,
+                                          const TensorLayout& scale,
+                                          const TensorLayout& zero_point,
+                                          const TensorLayout& grad_scale,
+                                          const TensorLayout& grad_x,
+                                          const TensorLayout& grad_s) = 0;
+
+protected:
+    void check_exec(const TensorLayout& diff, const TensorLayout& input,
+                    const TensorLayout& scale, const TensorLayout& zero_point,
+                    const TensorLayout& grad_scale, const TensorLayout& grad_x,
+                    const TensorLayout& grad_s, size_t workspace_in_bytes);
+};
+
 }  // namespace megdnn
 #include "megdnn/internal/opr_header_epilogue.h"
 

dnn/scripts/opr_param_defs.py

@@ -1124,3 +1124,8 @@ Note: NCHW_NCHW4_WEIGHT will auto pad oc and ic, you should remove oc in later o
  add_fields('int32', 'qmin', '-2147483648').
  add_fields('int32', 'qmax', '2147483647')
  )
+(pdef('LSQ').
+ add_fields('int32', 'qmin', '-2147483648').
+ add_fields('int32', 'qmax', '2147483647')
+ )
+

dnn/src/common/elemwise_helper.cpp

@@ -37,6 +37,7 @@ namespace megdnn {
         megdnn_assert(size, "uninitialized ElemwiseOpParamN");
     }
 
+    template struct ElemwiseOpParamN<7>;
     template struct ElemwiseOpParamN<6>;
     template struct ElemwiseOpParamN<5>;
     template struct ElemwiseOpParamN<4>;

dnn/src/common/handle_impl.h

@@ -208,7 +208,9 @@ private:
     cb(FakeQuantBackward) \
     cb(TQTForward) \
     cb(TQTBackward) \
-    cb(CheckHasInf)
+    cb(CheckHasInf) \
+    cb(LSQForward) \
+    cb(LSQBackward)
 
 /*!
  * \brief specialize HandleImpl::create_operator for a single opr type;

dnn/src/common/lsq.cpp

@@ -0,0 +1,69 @@
+/**
+ * \file dnn/src/common/lsq.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "megdnn/oprs.h"
+#include "src/common/utils.h"
+
+namespace megdnn {
+
+void LSQBase::deduce_layout_fwd(const TensorLayout& input,
+                                TensorLayout& output) {
+    output = TensorLayout(input, input.dtype);
+}
+
+void LSQBase::check_layout_fwd(const TensorLayout& input,
+                               const TensorLayout& scale,
+                               const TensorLayout& zero_point,
+                               const TensorLayout& grad_scale,
+                               const TensorLayout& output) {
+    megdnn_assert(input.dtype == dtype::Float32());
+    megdnn_assert(scale.dtype == dtype::Float32());
+    megdnn_assert(zero_point.dtype == dtype::Float32());
+    megdnn_assert(grad_scale.dtype == dtype::Float32());
+    TensorLayout expected;
+    deduce_layout_fwd(input, expected);
+    megdnn_assert_eq_layout(expected, output);
+}
+
+void LSQForward::deduce_layout(const TensorLayout& input,
+                               const TensorLayout& /* scale */,
+                               const TensorLayout& /*zero_point*/,
+                               const TensorLayout& /*grad_scale*/,
+                               TensorLayout& output) {
+    deduce_layout_fwd(input, output);
+}
+
+void LSQForward::check_exec(const TensorLayout& input,
+                            const TensorLayout& scale,
+                            const TensorLayout& zero_point,
+                            const TensorLayout& grad_scale,
+                            const TensorLayout& output,
+                            size_t workspace_in_bytes) {
+    check_layout_fwd(input, scale, zero_point, grad_scale, output);
+    auto required_workspace_space = get_workspace_in_bytes(
+            input, scale, zero_point, grad_scale, output);
+    megdnn_assert(workspace_in_bytes >= required_workspace_space);
+}
+
+void LSQBackward::check_exec(
+        const TensorLayout& diff, const TensorLayout& input,
+        const TensorLayout& scale, const TensorLayout& zero_point,
+        const TensorLayout& grad_scale, const TensorLayout& grad_x,
+        const TensorLayout& grad_s, size_t workspace_in_bytes) {
+    megdnn_assert_eq_shape(diff, input);
+    megdnn_assert_eq_shape(grad_x, input);
+    auto required_worspace_space = get_workspace_in_bytes(
+            diff, input, scale, zero_point, grad_scale, grad_x, grad_s);
+    megdnn_assert(workspace_in_bytes >= required_worspace_space);
+}
+
+}  // namespace megdnn

dnn/src/common/opr_trait.h

@@ -6,7 +6,8 @@
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
  */
 #pragma once
 #include "megdnn/oprs.h"
@@ -121,6 +122,8 @@ DEF(UniformRNG, 1, true, true);
 DEF(GaussianRNG, 1, true, true);
 DEF(ChecksumForward, 1, true, false);
 DEF(CheckHasInf, 2, true, true);
+DEF(LSQForward, 5, true, true);
+DEF(LSQBackward, 7, true, false);
 }  // namespace megdnn
 
 // vim: syntax=cpp.doxygen

dnn/src/cuda/elemwise_helper.cuh

@@ -947,6 +947,119 @@ struct OpCallerUniform<Op, 5, PVis> {
     }
 };
 
+
+//! specialization for arity == 6
+template <class Op, class PVis>
+struct OpCallerUniform<Op, 6, PVis> {
+    Op op;
+    PVis par[6];
+    static const uint32_t packed_size = PVis::packed_size;
+
+    devfunc void thread_init(uint32_t idx) {
+        idx = idx * packed_size;
+        par[0].thread_init(idx);
+        par[1].thread_init(idx);
+        par[2].thread_init(idx);
+        par[3].thread_init(idx);
+        par[4].thread_init(idx);
+        par[5].thread_init(idx);
+    }
+
+    devfunc void on(uint32_t idx) {
+        idx = idx * packed_size;
+        op(idx, par[0].at(idx), par[1].at(idx), par[2].at(idx), par[3].at(idx),
+           par[4].at(idx), par[5].at(idx));
+    }
+
+    devfunc void on(uint32_t idx, uint32_t remain) {
+        idx = idx * packed_size;
+        if (remain >= packed_size) {
+            op(idx, par[0].at(idx), par[1].at(idx), par[2].at(idx),
+               par[3].at(idx), par[4].at(idx), par[5].at(idx));
+        } else {
+            auto ptr0 = par[0].ptr();
+            auto ptr1 = par[1].ptr();
+            auto ptr2 = par[2].ptr();
+            auto ptr3 = par[3].ptr();
+            auto ptr4 = par[4].ptr();
+            auto ptr5 = par[5].ptr();
+            for (int i = 0; i < remain; i++) {
+                op(idx + i, ptr0[par[0].offset(idx + i)],
+                   ptr1[par[1].offset(idx + i)], ptr2[par[2].offset(idx + i)],
+                   ptr3[par[3].offset(idx + i)], ptr4[par[4].offset(idx + i)],
+                   ptr5[par[5].offset(idx + i)]);
+            }
+        }
+    }
+
+    devfunc void next() {
+        par[0].next();
+        par[1].next();
+        par[2].next();
+        par[3].next();
+        par[4].next();
+        par[5].next();
+    }
+};
+
+
+//! specialization for arity == 7
+template <class Op, class PVis>
+struct OpCallerUniform<Op, 7, PVis> {
+    Op op;
+    PVis par[7];
+    static const uint32_t packed_size = PVis::packed_size;
+
+    devfunc void thread_init(uint32_t idx) {
+        idx = idx * packed_size;
+        par[0].thread_init(idx);
+        par[1].thread_init(idx);
+        par[2].thread_init(idx);
+        par[3].thread_init(idx);
+        par[4].thread_init(idx);
+        par[5].thread_init(idx);
+        par[6].thread_init(idx);
+    }
+
+    devfunc void on(uint32_t idx) {
+        idx = idx * packed_size;
+        op(idx, par[0].at(idx), par[1].at(idx), par[2].at(idx), par[3].at(idx),
+           par[4].at(idx), par[5].at(idx), par[6].at(idx));
+    }
+
+    devfunc void on(uint32_t idx, uint32_t remain) {
+        idx = idx * packed_size;
+        if (remain >= packed_size) {
+            op(idx, par[0].at(idx), par[1].at(idx), par[2].at(idx),
+               par[3].at(idx), par[4].at(idx), par[5].at(idx), par[6].at(idx));
+        } else {
+            auto ptr0 = par[0].ptr();
+            auto ptr1 = par[1].ptr();
+            auto ptr2 = par[2].ptr();
+            auto ptr3 = par[3].ptr();
+            auto ptr4 = par[4].ptr();
+            auto ptr5 = par[5].ptr();
+            auto ptr6 = par[6].ptr();
+            for (int i = 0; i < remain; i++) {
+                op(idx + i, ptr0[par[0].offset(idx + i)],
+                   ptr1[par[1].offset(idx + i)], ptr2[par[2].offset(idx + i)],
+                   ptr3[par[3].offset(idx + i)], ptr4[par[4].offset(idx + i)],
+                   ptr5[par[5].offset(idx + i)], ptr6[par[6].offset(idx + i)]);
+            }
+        }
+    }
+
+    devfunc void next() {
+        par[0].next();
+        par[1].next();
+        par[2].next();
+        par[3].next();
+        par[4].next();
+        par[5].next();
+        par[6].next();
+    }
+};
+
 /*!
  * \brief call binary (i.e. arity == 2) operator with different param
  *      visitors

dnn/src/cuda/handle_create.cpp

@@ -6,7 +6,8 @@
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
  */
 
 #include "src/common/handle_impl.h"
@@ -15,6 +16,7 @@
 #include "src/cuda/add_update/opr_impl.h"
 #include "src/cuda/argmxx/opr_impl.h"
 #include "src/cuda/argsort/opr_impl.h"
+#include "src/cuda/batch_conv_bias/opr_impl.h"
 #include "src/cuda/batch_normalization/opr_impl.h"
 #include "src/cuda/batched_matrix_mul/opr_impl.h"
 #include "src/cuda/check_has_inf/opr_impl.h"
@@ -35,6 +37,7 @@
 #include "src/cuda/elemwise/opr_impl.h"
 #include "src/cuda/elemwise_multi_type/opr_impl.h"
 #include "src/cuda/eye/opr_impl.h"
+#include "src/cuda/fake_quant/opr_impl.h"
 #include "src/cuda/flip/opr_impl.h"
 #include "src/cuda/gaussian_blur/opr_impl.h"
 #include "src/cuda/group_local/opr_impl.h"
@@ -45,6 +48,7 @@
 #include "src/cuda/local/opr_impl.h"
 #include "src/cuda/local_share/opr_impl.h"
 #include "src/cuda/lrn/opr_impl.h"
+#include "src/cuda/lsq/opr_impl.h"
 #include "src/cuda/mask_conv/opr_impl.h"
 #include "src/cuda/matrix_inverse/opr_impl.h"
 #include "src/cuda/matrix_mul/opr_impl.h"
@@ -56,9 +60,11 @@
 #include "src/cuda/reduce/opr_impl.h"
 #include "src/cuda/relayout/opr_impl.h"
 #include "src/cuda/relayout_format/opr_impl.h"
+#include "src/cuda/remap/opr_impl.h"
 #include "src/cuda/repeat/opr_impl.h"
 #include "src/cuda/resize/opr_impl.h"
 #include "src/cuda/rng/opr_impl.h"
+#include "src/cuda/roi_align/opr_impl.h"
 #include "src/cuda/roi_copy/opr_impl.h"
 #include "src/cuda/roi_pooling/opr_impl.h"
 #include "src/cuda/rotate/opr_impl.h"
@@ -70,16 +76,11 @@
 #include "src/cuda/tensor_remap/opr_impl.h"
 #include "src/cuda/tile/opr_impl.h"
 #include "src/cuda/topk/opr_impl.h"
+#include "src/cuda/tqt/opr_impl.h"
 #include "src/cuda/transpose/opr_impl.h"
 #include "src/cuda/type_cvt/opr_impl.h"
 #include "src/cuda/warp_affine/opr_impl.h"
 #include "src/cuda/warp_perspective/opr_impl.h"
-#include "src/cuda/local_share/opr_impl.h"
-#include "src/cuda/roi_align/opr_impl.h"
-#include "src/cuda/batch_conv_bias/opr_impl.h"
-#include "src/cuda/remap/opr_impl.h"
-#include "src/cuda/fake_quant/opr_impl.h"
-#include "src/cuda/tqt/opr_impl.h"
 
 namespace megdnn {
 namespace cuda {

dnn/src/cuda/lsq/kern.cu

@@ -0,0 +1,30 @@
+/**
+ * \file dnn/src/cuda/lsq/kern.cu
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "./kern.cuh"
+
+namespace megdnn {
+namespace cuda {
+
+#define cb(_dtype)                                                      \
+    INST_RUN_ELEMWISE(LSQKernOp<DTypeTrait<_dtype>::ctype>,             \
+                      DTypeTrait<_dtype>::ctype, 3);                    \
+    INST_RUN_ELEMWISE(LSQBwdKernOp<DTypeTrait<_dtype>::ctype>,          \
+                      DTypeTrait<_dtype>::ctype, 3);                    \
+    INST_RUN_ELEMWISE(LSQKernOpNonContig<DTypeTrait<_dtype>::ctype>,    \
+                      DTypeTrait<_dtype>::ctype, 5);                    \
+    INST_RUN_ELEMWISE(LSQBwdKernOpNonContig<DTypeTrait<_dtype>::ctype>, \
+                      DTypeTrait<_dtype>::ctype, 7);
+cb(megdnn::dtype::Float32)
+
+}  // namespace cuda
+}  // namespace megdnn

dnn/src/cuda/lsq/kern.cuh

@@ -0,0 +1,126 @@
+/**
+ * \file dnn/src/cuda/lsq/kern.cuh
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#pragma once
+
+#include "src/cuda/elemwise_helper.cuh"
+#include "src/cuda/utils.cuh"
+
+#if MEGDNN_CC_HOST
+#include "megdnn/oprs.h"
+#endif
+
+namespace megdnn {
+namespace cuda {
+template <typename ctype>
+struct LSQKernOp {
+    ctype* input;
+    ctype* output;
+    ctype qmin, qmax;
+
+    __device__ void operator()(uint32_t idx, ctype scale, ctype zero_point,
+                               ctype grad_scale) {
+        ctype x = input[idx] / scale + zero_point;
+        x = fmaxf(fminf(x, qmax), qmin);
+        x = round(x);
+        output[idx] = (x - zero_point) * scale;
+    }
+
+#if MEGDNN_CC_HOST
+    LSQKernOp(const TensorND& input, const TensorND& output,
+              const LSQ::Param& param)
+            : input{input.ptr<ctype>()},
+              output{output.ptr<ctype>()},
+              qmin(param.qmin),
+              qmax(param.qmax) {}
+#endif
+};
+
+template <typename ctype>
+struct LSQBwdKernOp {
+    ctype* diff;
+    ctype* input;
+    ctype* grad_x;
+    ctype* grad_s;
+    ctype qmin, qmax;
+
+    __device__ void operator()(uint32_t idx, ctype scale, ctype zero_point,
+                               ctype grad_scale) {
+        ctype x = input[idx] / scale + zero_point;
+        bool ind_small = x < qmin;
+        bool ind_big = x > qmax;
+        bool ind_middle = ind_small ^ ind_big;
+        ind_middle = !ind_middle;
+        grad_s[idx] = ind_small * qmin + ind_big * qmax +
+                      ind_middle * (-x + round(x));
+        grad_s[idx] = grad_s[idx] * grad_scale * diff[idx];
+        grad_x[idx] = ind_middle * diff[idx];
+    }
+
+#if MEGDNN_CC_HOST
+    LSQBwdKernOp(const TensorND& diff, const TensorND& input,
+                 const TensorND& grad_x, const TensorND& grad_s,
+                 const LSQ::Param& param)
+            : diff{diff.ptr<ctype>()},
+              input{input.ptr<ctype>()},
+              grad_x{grad_x.ptr<ctype>()},
+              grad_s{grad_s.ptr<ctype>()},
+              qmin(param.qmin),
+              qmax(param.qmax) {}
+#endif
+};
+
+template <typename ctype>
+struct LSQKernOpNonContig {
+    ctype qmin;
+    ctype qmax;
+
+    __device__ void operator()(uint32_t, ctype& output, ctype& input,
+                               ctype& scale, ctype& zero_point,
+                               ctype grad_scale) {
+        ctype x = input / scale + zero_point;
+        x = fmaxf(fminf(x, qmax), qmin);
+        x = round(x);
+        output = (x - zero_point) * scale;
+    }
+#if MEGDNN_CC_HOST
+    LSQKernOpNonContig(const LSQ::Param& param)
+            : qmin(param.qmin), qmax(param.qmax) {}
+#endif
+};
+
+template <typename ctype>
+struct LSQBwdKernOpNonContig {
+    ctype qmin;
+    ctype qmax;
+
+    __device__ void operator()(uint32_t, ctype& grad_x, ctype& grad_s,
+                               ctype& diff, ctype& input, ctype& scale,
+                               ctype& zero_point, ctype grad_scale) {
+        ctype x = input / scale + zero_point;
+        bool ind_small = x < qmin;
+        bool ind_big = x > qmax;
+        bool ind_middle = ind_small ^ ind_big;
+        ind_middle = !ind_middle;
+        grad_s = ind_small * qmin + ind_big * qmax +
+                 ind_middle * (-x + round(x));
+        grad_s = grad_s * grad_scale * diff;
+        grad_x = ind_middle * diff;
+    }
+#if MEGDNN_CC_HOST
+    LSQBwdKernOpNonContig(const LSQ::Param& param)
+            : qmin(param.qmin), qmax(param.qmax) {}
+#endif
+};
+
+}  // namespace cuda
+}  // namespace megdnn

dnn/src/cuda/lsq/opr_impl.cpp

@@ -0,0 +1,151 @@
+/**
+ * \file dnn/src/cuda/lsq/opr_impl.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "./opr_impl.h"
+#include "./kern.cuh"
+#include "src/common/utils.h"
+namespace megdnn {
+namespace cuda {
+
+void LSQForwardImpl::exec(_megdnn_tensor_in input, _megdnn_tensor_in scale,
+                          _megdnn_tensor_in zero_point,
+                          _megdnn_tensor_in grad_scale,
+                          _megdnn_tensor_out output,
+                          _megdnn_workspace workspace) {
+    check_exec(input.layout, scale.layout, zero_point.layout, grad_scale.layout,
+               output.layout, workspace.size);
+
+    if (!input.layout.is_contiguous() || !output.layout.is_contiguous())
+        return exec_noncontig(input, scale, zero_point, grad_scale, output);
+
+    ElemwiseOpParamN<3> ele_param;
+    ele_param[0] = scale;
+    ele_param[0].layout = ele_param[0].layout.broadcast(input.layout);
+    ele_param[1] = zero_point;
+    ele_param[1].layout = ele_param[1].layout.broadcast(input.layout);
+    ele_param[2] = grad_scale;
+    ele_param[2].layout = ele_param[2].layout.broadcast(input.layout);
+    ele_param.init_from_given_tensor();
+    auto m_param = param();
+    auto stream = cuda_stream(handle());
+
+#define cb(DType)                                                   \
+    if (input.layout.dtype == DType()) {                            \
+        using T = typename DTypeTrait<DType>::ctype;                \
+        run_elemwise<LSQKernOp<T>, T, 3>(ele_param, stream,         \
+                                         {input, output, m_param}); \
+        return;                                                     \
+    }
+    cb(megdnn::dtype::Float32)
+#undef cb
+}
+
+void LSQForwardImpl::exec_noncontig(_megdnn_tensor_in input,
+                                    _megdnn_tensor_in scale,
+                                    _megdnn_tensor_in zero_point,
+                                    _megdnn_tensor_in grad_scale,
+                                    _megdnn_tensor_out output) {
+    ElemwiseOpParamN<5> ele_param;
+    ele_param[0] = output;
+    ele_param[1] = input;
+    ele_param[2] = scale;
+    ele_param[2].layout = ele_param[2].layout.broadcast(input.layout);
+    ele_param[3] = zero_point;
+    ele_param[3].layout = ele_param[3].layout.broadcast(input.layout);
+    ele_param[4] = grad_scale;
+    ele_param[4].layout = ele_param[4].layout.broadcast(input.layout);
+    ele_param.init_from_given_tensor();
+    auto m_param = param();
+    auto stream = cuda_stream(handle());
+
+#define cb(DType)                                                    \
+    if (input.layout.dtype == DType()) {                             \
+        using T = typename DTypeTrait<DType>::ctype;                 \
+        run_elemwise<LSQKernOpNonContig<T>, T, 5>(ele_param, stream, \
+                                                  {m_param});        \
+        return;                                                      \
+    }
+    cb(megdnn::dtype::Float32)
+#undef cb
+}
+
+void LSQBackwardImpl::exec(_megdnn_tensor_in diff, _megdnn_tensor_in input,
+                           _megdnn_tensor_in scale,
+                           _megdnn_tensor_in zero_point,
+                           _megdnn_tensor_in grad_scale,
+                           _megdnn_tensor_out grad_x, _megdnn_tensor_out grad_s,
+                           _megdnn_workspace workspace) {
+    check_exec(diff.layout, input.layout, scale.layout, zero_point.layout,
+               grad_scale.layout, grad_x.layout, grad_s.layout, workspace.size);
+
+    if (!input.layout.is_contiguous() || !diff.layout.is_contiguous() ||
+        !grad_x.layout.is_contiguous() || !grad_s.layout.is_contiguous())
+        return exec_noncontig(diff, input, scale, zero_point, grad_scale,
+                              grad_x, grad_s);
+
+    ElemwiseOpParamN<3> ele_param;
+    ele_param[0] = scale;
+    ele_param[0].layout = ele_param[0].layout.broadcast(input.layout);
+    ele_param[1] = zero_point;
+    ele_param[1].layout = ele_param[1].layout.broadcast(input.layout);
+    ele_param[2] = grad_scale;
+    ele_param[2].layout = ele_param[2].layout.broadcast(input.layout);
+    ele_param.init_from_given_tensor();
+    auto m_param = param();
+    auto stream = cuda_stream(handle());
+
+#define cb(DType)                                                           \
+    if (grad_x.layout.dtype == DType()) {                                   \
+        using T = typename DTypeTrait<DType>::ctype;                        \
+        run_elemwise<LSQBwdKernOp<T>, T, 3>(                                \
+                ele_param, stream, {diff, input, grad_x, grad_s, m_param}); \
+        return;                                                             \
+    }
+    cb(megdnn::dtype::Float32)
+#undef cb
+}
+
+void LSQBackwardImpl::exec_noncontig(_megdnn_tensor_in diff,
+                                     _megdnn_tensor_in input,
+                                     _megdnn_tensor_in scale,
+                                     _megdnn_tensor_in zero_point,
+                                     _megdnn_tensor_in grad_scale,
+                                     _megdnn_tensor_out grad_x,
+                                     _megdnn_tensor_out grad_s) {
+    ElemwiseOpParamN<7> ele_param;
+    ele_param[0] = grad_x;
+    ele_param[1] = grad_s;
+    ele_param[2] = diff;
+    ele_param[3] = input;
+    ele_param[4] = scale;
+    ele_param[4].layout = ele_param[4].layout.broadcast(input.layout);
+    ele_param[5] = zero_point;
+    ele_param[5].layout = ele_param[5].layout.broadcast(input.layout);
+    ele_param[6] = grad_scale;
+    ele_param[6].layout = ele_param[6].layout.broadcast(input.layout);
+    ele_param.init_from_given_tensor();
+    auto m_param = param();
+    auto stream = cuda_stream(handle());
+
+#define cb(DType)                                                       \
+    if (input.layout.dtype == DType()) {                                \
+        using T = typename DTypeTrait<DType>::ctype;                    \
+        run_elemwise<LSQBwdKernOpNonContig<T>, T, 7>(ele_param, stream, \
+                                                     {m_param});        \
+        return;                                                         \
+    }
+    cb(megdnn::dtype::Float32)
+#undef cb
+}
+
+}  // namespace cuda
+}  // namespace megdnn

dnn/src/cuda/lsq/opr_impl.h

@@ -0,0 +1,65 @@
+/**
+ * \file dnn/src/cuda/lsq/opr_impl.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#pragma once
+#include "megdnn/oprs.h"
+#include "src/cuda/utils.h"
+namespace megdnn {
+namespace cuda {
+
+class LSQForwardImpl final : public LSQForward {
+public:
+    using LSQForward::LSQForward;
+    void exec(_megdnn_tensor_in input, _megdnn_tensor_in scale,
+              _megdnn_tensor_in zero_point, _megdnn_tensor_in grad_scale,
+              _megdnn_tensor_out output, _megdnn_workspace workspace) override;
+    size_t get_workspace_in_bytes(const TensorLayout&, /* input */
+                                  const TensorLayout&, /* scale */
+                                  const TensorLayout&, /* zero_point */
+                                  const TensorLayout&, /* grad_scale */
+                                  const TensorLayout& /* output */) override {
+        return 0;
+    }
+
+private:
+    void exec_noncontig(_megdnn_tensor_in input, _megdnn_tensor_in scale,
+                        _megdnn_tensor_in zero_point,
+                        _megdnn_tensor_in grad_scale,
+                        _megdnn_tensor_out output);
+};
+
+class LSQBackwardImpl final : public LSQBackward {
+public:
+    using LSQBackward::LSQBackward;
+    void exec(_megdnn_tensor_in diff, _megdnn_tensor_in input,
+              _megdnn_tensor_in scale, _megdnn_tensor_in zero_point,
+              _megdnn_tensor_in grad_scale, _megdnn_tensor_out grad_x,
+              _megdnn_tensor_out grad_s, _megdnn_workspace workspace) override;
+    size_t get_workspace_in_bytes(const TensorLayout& /* diff */,
+                                  const TensorLayout& /* input */,
+                                  const TensorLayout& /* scale */,
+                                  const TensorLayout& /* zero_point */,
+                                  const TensorLayout& /* grad_scale */,
+                                  const TensorLayout& /* grad_x */,
+                                  const TensorLayout& /* grad_s */) override {
+        return 0;
+    }
+
+private:
+    void exec_noncontig(_megdnn_tensor_in diff, _megdnn_tensor_in input,
+                        _megdnn_tensor_in scale, _megdnn_tensor_in zero_point,
+                        _megdnn_tensor_in grad_scale, _megdnn_tensor_out grad_x,
+                        _megdnn_tensor_out grad_s);
+};
+
+}  // namespace cuda
+}  // namespace megdnn

dnn/src/naive/handle.cpp

@@ -50,6 +50,7 @@
 #include "src/naive/local/opr_impl.h"
 #include "src/naive/local_share/opr_impl.h"
 #include "src/naive/lrn/opr_impl.h"
+#include "src/naive/lsq/opr_impl.h"
 #include "src/naive/mask_conv/opr_impl.h"
 #include "src/naive/matrix_inverse/opr_impl.h"
 #include "src/naive/matrix_mul/opr_impl.h"

dnn/src/naive/lsq/opr_impl.cpp

@@ -0,0 +1,141 @@
+/**
+ * \file dnn/src/naive/lsq/opr_impl.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "src/naive/lsq/opr_impl.h"
+#include <cmath>
+#include "megdnn/tensor_iter.h"
+#include "src/common/elemwise_helper.cuh"
+#include "src/common/utils.h"
+#include "src/naive/handle.h"
+
+namespace {
+using namespace megdnn;
+
+template <typename T>
+void forward_impl(const ElemwiseOpParamN<5> src, float qmin, float qmax) {
+    auto inp = tensor_iter_valonly<T>(src[0]).begin();
+    auto out = tensor_iter_valonly<T>(src[1]).begin();
+    auto scale = tensor_iter_valonly<T>(src[2]).begin();
+    auto zero_point = tensor_iter_valonly<T>(src[3]).begin();
+    auto grad_scale = tensor_iter_valonly<T>(src[4]).begin();
+    size_t total = src[0].layout.total_nr_elems();
+    for (size_t i = 0; i < total; ++i) {
+        T x = (*inp) / (*scale) + (*zero_point);
+        x = x <= qmin ? qmin : x;
+        x = x >= qmax ? qmax : x;
+        x = round(x);
+        *out = (x - (*zero_point)) * (*scale);
+        ++inp;
+        ++out;
+        ++scale;
+        ++zero_point;
+        ++grad_scale;
+    }
+}
+
+template <typename T>
+void backward_impl(const ElemwiseOpParamN<7> src, float qmin, float qmax) {
+    auto diff = tensor_iter_valonly<T>(src[0]).begin();
+    auto input = tensor_iter_valonly<T>(src[1]).begin();
+    auto scale = tensor_iter_valonly<T>(src[2]).begin();
+    auto zero_point = tensor_iter_valonly<T>(src[3]).begin();
+    auto grad_scale = tensor_iter_valonly<T>(src[4]).begin();
+    auto grad_x = tensor_iter_valonly<T>(src[5]).begin();
+    auto grad_s = tensor_iter_valonly<T>(src[6]).begin();
+    size_t total = src[0].layout.total_nr_elems();
+
+    for (size_t i = 0; i < total; ++i) {
+        T x = (*input) / (*scale) + (*zero_point);
+        bool ind_small = x < qmin;
+        bool ind_big = x > qmax;
+        bool ind_middle = ind_small ^ ind_big;
+        ind_middle = !ind_middle;
+
+        *grad_s = ind_small * qmin + ind_big * qmax +
+                  ind_middle * (-x + round(x));
+        *grad_s = (*grad_s) * (*grad_scale) * (*diff);
+        *grad_x = ind_middle * (*diff);
+
+        ++diff;
+        ++input;
+        ++scale;
+        ++zero_point;
+        ++grad_scale;
+        ++grad_x;
+        ++grad_s;
+    }
+}
+
+}  // namespace
+namespace megdnn {
+namespace naive {
+
+void LSQForwardImpl::exec(_megdnn_tensor_in input, _megdnn_tensor_in scale,
+                          _megdnn_tensor_in zero_point,
+                          _megdnn_tensor_in grad_scale,
+                          _megdnn_tensor_out output,
+                          _megdnn_workspace workspace) {
+    check_exec(input.layout, scale.layout, zero_point.layout, grad_scale.layout,
+               output.layout, workspace.size);
+    ElemwiseOpParamN<5> src;
+    src[0] = input;
+    src[1] = output;
+    src[2] = scale;
+    src[2].layout = src[2].layout.broadcast(input.layout);
+    src[3] = zero_point;
+    src[3].layout = src[3].layout.broadcast(input.layout);
+    src[4] = grad_scale;
+    src[4].layout = src[4].layout.broadcast(input.layout);
+#define cb(DType)                                                  \
+    if (input.layout.dtype == DType()) {                           \
+        using T = typename DTypeTrait<DType>::ctype;               \
+        MEGDNN_DISPATCH_CPU_KERN_OPR(                              \
+                forward_impl<T>(src, param().qmin, param().qmax)); \
+        return;                                                    \
+    }
+    cb(dtype::Float32)
+#undef cb
+}
+
+void LSQBackwardImpl::exec(_megdnn_tensor_in diff, _megdnn_tensor_in input,
+                           _megdnn_tensor_in scale,
+                           _megdnn_tensor_in zero_point,
+                           _megdnn_tensor_in grad_scale,
+                           _megdnn_tensor_out grad_x, _megdnn_tensor_out grad_s,
+                           _megdnn_workspace workspace) {
+    check_exec(diff.layout, input.layout, scale.layout, zero_point.layout,
+               grad_scale.layout, grad_x.layout, grad_s.layout, workspace.size);
+    ElemwiseOpParamN<7> src;
+    src[0] = diff;
+    src[1] = input;
+    src[2] = scale;
+    src[2].layout = src[2].layout.broadcast(input.layout);
+    src[3] = zero_point;
+    src[3].layout = src[3].layout.broadcast(input.layout);
+    src[4] = grad_scale;
+    src[4].layout = src[4].layout.broadcast(input.layout);
+    src[5] = grad_x;
+    src[6] = grad_s;
+#define cb(DType)                                                         \
+    if (diff.layout.dtype == DType() && grad_x.layout.dtype == DType() && \
+        input.layout.dtype == DType()) {                                  \
+        using T = typename DTypeTrait<DType>::ctype;                      \
+        MEGDNN_DISPATCH_CPU_KERN_OPR(                                     \
+                backward_impl<T>(src, param().qmin, param().qmax));       \
+        return;                                                           \
+    }
+    cb(dtype::Float32)
+#undef cb
+}
+
+}  // namespace naive
+}  // namespace megdnn

dnn/src/naive/lsq/opr_impl.h

@@ -0,0 +1,53 @@
+/**
+ * \file dnn/src/naive/lsq/opr_impl.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#pragma once
+#include "megdnn/oprs.h"
+
+namespace megdnn {
+namespace naive {
+
+class LSQForwardImpl final : public LSQForward {
+public:
+    using LSQForward::LSQForward;
+    void exec(_megdnn_tensor_in input, _megdnn_tensor_in scale,
+              _megdnn_tensor_in zero_point, _megdnn_tensor_in grad_scale,
+              _megdnn_tensor_out output, _megdnn_workspace workspace) override;
+    size_t get_workspace_in_bytes(const TensorLayout& /* input */,
+                                  const TensorLayout& /* scale */,
+                                  const TensorLayout& /* zero_point */,
+                                  const TensorLayout& /* grad_scale */,
+                                  const TensorLayout& /* output */) override {
+        return 0;
+    }
+};
+
+class LSQBackwardImpl final : public LSQBackward {
+public:
+    using LSQBackward::LSQBackward;
+    void exec(_megdnn_tensor_in diff, _megdnn_tensor_in input,
+              _megdnn_tensor_in scale, _megdnn_tensor_in zero_point,
+              _megdnn_tensor_in grad_scale, _megdnn_tensor_out grad_x,
+              _megdnn_tensor_out grad_s, _megdnn_workspace workspace) override;
+    size_t get_workspace_in_bytes(const TensorLayout& /* diff */,
+                                  const TensorLayout& /* input */,
+                                  const TensorLayout& /* scale */,
+                                  const TensorLayout& /* zero_point */,
+                                  const TensorLayout& /* grad_scale */,
+                                  const TensorLayout& /* grad_x */,
+                                  const TensorLayout& /* grad_s */) override {
+        return 0;
+    }
+};
+
+}  // namespace naive
+}  // namespace megdnn

dnn/test/common/lsq.h

@@ -0,0 +1,53 @@
+/**
+ * \file dnn/test/common/lsq.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#pragma once
+#include "megdnn/basic_types.h"
+#include "megdnn/opr_param_defs.h"
+
+namespace megdnn {
+namespace test {
+namespace lsq {
+
+struct TestArg {
+    param::LSQ param;
+    TensorShape ishape;
+    TensorShape scale_shape;
+    TensorShape zeropoint_shape;
+    TensorShape gradscale_shape;
+    TestArg(param::LSQ param, TensorShape ishape, TensorShape scale_shape,
+            TensorShape zeropoint_shape, TensorShape gradscale_shape)
+            : param(param),
+              ishape(ishape),
+              scale_shape(scale_shape),
+              zeropoint_shape(zeropoint_shape),
+              gradscale_shape(gradscale_shape) {}
+};
+
+inline std::vector<TestArg> get_args() {
+    std::vector<TestArg> args;
+    param::LSQ cur_param;
+
+    cur_param.qmin = -127;
+    cur_param.qmax = 127;
+
+    for (size_t i = 10; i < 30; i += 2) {
+        args.emplace_back(cur_param, TensorShape{10, 64, i, i}, TensorShape{1},
+                          TensorShape{1}, TensorShape{1});
+    }
+
+    return args;
+}
+
+}  // namespace lsq
+}  // namespace test
+}  // namespace megdnn

dnn/test/cuda/lsq.cpp

@@ -0,0 +1,110 @@
+/**
+ * \file dnn/test/cuda/lsq.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "test/common/lsq.h"
+#include "megdnn/oprs.h"
+#include "test/common/checker.h"
+#include "test/cuda/fixture.h"
+
+namespace megdnn {
+namespace test {
+
+using namespace lsq;
+
+TEST_F(CUDA, LSQ) {
+    std::vector<TestArg> args = get_args();
+    auto dtype = dtype::Float32();
+
+    for (auto&& arg : args) {
+        auto param = arg.param;
+        auto ishape = arg.ishape;
+        auto scale_shape = arg.scale_shape;
+        auto zeropoint_shape = arg.zeropoint_shape;
+        auto gradscale_shape = arg.gradscale_shape;
+        Checker<LSQForward> checker(handle_cuda());
+        checker.set_param(param)
+                .set_dtype(0, dtype)
+                .set_dtype(1, dtype)
+                .set_dtype(2, dtype)
+                .set_dtype(3, dtype)
+                .set_dtype(4, dtype)
+                .execs({ishape, scale_shape, zeropoint_shape, gradscale_shape,
+                        ishape});
+    }
+    // test noncontiguous layout
+    for (auto&& arg : args) {
+        auto param = arg.param;
+        auto ishape = arg.ishape;
+        auto sshape = arg.scale_shape;
+        auto zeropoint_shape = arg.zeropoint_shape;
+        auto gradscale_shape = arg.gradscale_shape;
+        Checker<LSQForward> checker(handle_cuda());
+        TensorLayout ilayout(
+                ishape,
+                {(long int)(ishape[1] * ishape[2] * ishape[3] * 2),
+                 (long int)(ishape[2] * ishape[3]), (long int)ishape[3], 1},
+                dtype::Float32());
+        checker.set_param(param).execl({ilayout,
+                                        {sshape, dtype::Float32()},
+                                        {zeropoint_shape, dtype::Float32()},
+                                        {gradscale_shape, dtype::Float32()},
+                                        ilayout});
+    }
+}
+
+TEST_F(CUDA, LSQ_BACKWARD) {
+    std::vector<TestArg> args = get_args();
+    auto dtype = dtype::Float32();
+
+    for (auto&& arg : args) {
+        auto param = arg.param;
+        auto ishape = arg.ishape;
+        auto scale_shape = arg.scale_shape;
+        auto zeropoint_shape = arg.zeropoint_shape;
+        auto gradscale_shape = arg.gradscale_shape;
+        Checker<LSQBackward> checker(handle_cuda());
+        checker.set_param(param)
+                .set_dtype(0, dtype)
+                .set_dtype(1, dtype)
+                .set_dtype(2, dtype)
+                .set_dtype(3, dtype)
+                .set_dtype(4, dtype)
+                .set_dtype(5, dtype)
+                .set_dtype(6, dtype)
+                .execs({ishape, ishape, scale_shape, zeropoint_shape,
+                        gradscale_shape, ishape, ishape});
+    }
+    // test noncontiguous layout
+    for (auto&& arg : args) {
+        auto param = arg.param;
+        auto ishape = arg.ishape;
+        auto sshape = arg.scale_shape;
+        auto zeropoint_shape = arg.zeropoint_shape;
+        auto gradscale_shape = arg.gradscale_shape;
+        Checker<LSQBackward> checker(handle_cuda());
+        TensorLayout ilayout(
+                ishape,
+                {(long int)(ishape[1] * ishape[2] * ishape[3] * 2),
+                 (long int)(ishape[2] * ishape[3]), (long int)ishape[3], 1},
+                dtype::Float32());
+        checker.set_param(param).execl({ilayout,
+                                        ilayout,
+                                        {sshape, dtype::Float32()},
+                                        {zeropoint_shape, dtype::Float32()},
+                                        {gradscale_shape, dtype::Float32()},
+                                        ilayout,
+                                        ilayout});
+    }
+}
+
+}  // namespace test
+}  // namespace megdnn

dnn/test/naive/lsq.cpp

@@ -0,0 +1,45 @@
+/**
+ * \file dnn/test/naive/sliding_window_transpose.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+#include "test/naive/fixture.h"
+
+#include "megdnn/oprs/nn.h"
+#include "test/common/checker.h"
+
+using namespace megdnn;
+using namespace test;
+
+TEST_F(NAIVE, LSQ_FORWARD) {
+    Checker<LSQ> checker(handle(), /* check_dispatch */ false);
+
+    param::LSQ param;
+
+    param.qmin = -127;
+    param.qmax = 127;
+
+    TensorND input =
+            TensorValue({2, 2, 2, 2}, dtype::Float32(),
+                        {0, 1, 3, 4, 1, 2, 4, 5, 3, 4, 6, 7, 4, 5, 7, 8});
+
+    TensorND scale_shape = TensorValue({1}, dtype::Float32(), {2});
+
+    TensorND zero_point = TensorValue({1}, dtype::Float32(), {1});
+
+    TensorND grad_scale = TensorValue({1}, dtype::Float32(), {0.5});
+
+    TensorND output =
+            TensorValue({2, 2, 2, 2}, dtype::Float32(),
+                        {0, 2, 4, 4, 2, 2, 4, 6, 4, 4, 6, 8, 4, 6, 8, 8});
+
+    checker.set_param(param).exect(
+            Testcase{input, scale_shape, zero_point, grad_scale, {}},
+            Testcase{{}, {}, {}, {}, output});
+}

imperative/python/megengine/quantization/__init__.py

@@ -6,7 +6,7 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 
-from .fake_quant import TQT, FakeQuantize
+from .fake_quant import LSQ, TQT, FakeQuantize
 from .observer import (
     ExponentialMovingAverageObserver,
     HistogramObserver,

imperative/python/megengine/quantization/fake_quant.py

@@ -12,13 +12,15 @@ from .. import functional as F
 from ..core.tensor.dtype import QuantDtypeMeta, _builtin_quant_dtypes
 from ..logger import get_logger
 from ..module import Module
-from ..tensor import Parameter
+from ..tensor import Parameter, Tensor
 from .utils import (
+    LSQParams,
     QParams,
     QParamsModuleMixin,
     QuantMode,
     create_qparams,
     fake_quant_tensor,
+    lsq_forward,
     tqt_forward,
 )
 
@@ -117,3 +119,58 @@ class FakeQuantize(_FakeQuantize):
             qparams.dtype_meta, self.dtype
         )
         return fake_quant_tensor(inp, qparams)
+
+
+class LSQ(_FakeQuantize, QParamsModuleMixin):
+    r"""
+    LSQ: https://arxiv.org/pdf/1902.08153.pdf Estimating and scaling the 
+    task loss gradient at each weight and activation layer's quantizer step size
+
+    :param dtype: a string or :class:`~.QuantDtypeMeta` indicating the target
+        quantization dtype of input.
+    :param enable: whether do ``normal_forward`` or ``fake_quant_forward``.
+    :param eps:a small value to avoid division by zero. Default: 1e-5
+    """
+
+    def init(
+        self,
+        dtype: Union[str, QuantDtypeMeta],
+        enable: bool = True,
+        eps: float = 1e-5,
+        **kwargs
+    ):
+        super().__init__(dtype=dtype, enable=enable, **kwargs)
+        self.eps = Tensor(eps, dtype="float32")
+        self.step_size = Parameter(1.0, dtype="float32")
+
+    def set_qparams(self, qparams: LSQParams):
+        self.mode = qparams.mode
+        if qparams.mode == QuantMode.ASYMMERTIC:
+            self.zero_point = qparams.zero_point
+        else:
+            self.zero_point = Tensor([0.0], dtype="float32")
+        if qparams.scale is None:
+            raise AssertionError("Can not get an initialized scale")
+        init_step_size = qparams.scale
+        if init_step_size < self.eps:
+            init_step_size = 0
+        else:
+            init_step_size = init_step_size - self.eps
+        self.step_size = Parameter(init_step_size, dtype="float32")
+
+        self.grad_scale = qparams.grad_scale
+
+    def fake_quant_forward(self, inp, qparams: LSQParams = None):
+        step_size = F.abs(self.step_size) + self.eps
+        return lsq_forward(
+            self.qmin, self.qmax, inp, step_size, self.zero_point, self.grad_scale
+        )
+
+    def get_qparams(self):
+        return LSQParams(
+            mode=self.mode,
+            dtype_meta=self.dtype,
+            scale=F.abs(self.step_size.detach()) + self.eps,
+            zero_point=self.zero_point,
+            grad_scale=self.grad_scale,
+        )

imperative/python/megengine/quantization/utils.py

@@ -43,6 +43,16 @@ def tqt_forward(qmin, qmax, inp, scale):
     return output
 
 
+def lsq_forward(qmin, qmax, inp, step_size, zero_point=None, scale_grad=None):
+    if zero_point is None:
+        zero_point = Tensor([0.0], dtype=np.float32)
+    if scale_grad is None:
+        scale_grad = Tensor([1.0], dtype=np.float32)
+    op = builtin.LSQ(qmin=qmin, qmax=qmax)
+    (output,) = apply(op, inp, step_size, zero_point, scale_grad)
+    return output
+
+
 def register_method_to_class(cls):
     def decorator(func):
         @wraps(func)
@@ -105,6 +115,47 @@ class QParams:
         return "QParams({})".format(content)
 
 
+class LSQParams:
+    """
+    To standardize LSQ's qparams format. If custom
+    qparams is needed, inherit this class and add custom ``__slots__``.
+    """
+
+    __slots__ = "mode", "dtype_meta", "scale", "zero_point", "grad_scale"
+
+    def __init__(
+        self,
+        mode: QuantMode,
+        dtype_meta: QuantDtypeMeta,
+        scale: Tensor,
+        zero_point: Tensor,
+        grad_scale: Tensor,
+    ):
+        self.mode = mode
+        self.dtype_meta = dtype_meta
+        self.scale = scale
+        self.zero_point = zero_point
+        self.grad_scale = grad_scale
+
+    def update(self, lsqparams: "LSQParams"):
+        for key in self.__slots__:
+            setattr(self, key, getattr(lsqparams, key))
+
+    def __eq__(self, other):
+        if len(self.__slots__) != len(other.__slots__):
+            return False
+        for key in self.__slots__:
+            if not hasattr(other, key) or getattr(self, key) != getattr(other, key):
+                return False
+        return True
+
+    def __repr__(self):
+        content = ", ".join(
+            ["{}={}".format(key, getattr(self, key)) for key in self.__slots__]
+        )
+        return "LSQParams({})".format(content)
+
+
 class QParamsModuleMixin(abc.ABC):
     def get_quantized_dtype(self):
         qparams = self.get_qparams()

imperative/python/test/unit/quantization/test_fake_quant.py

@@ -10,6 +10,7 @@ import numpy as np
 import pytest
 
 import megengine as mge
+import megengine.functional as F
 from megengine import tensor
 from megengine.core.autodiff.grad import Function, Grad
 from megengine.core.tensor.dtype import QuantDtypeMeta
@@ -19,6 +20,7 @@ from megengine.quantization.utils import (
     QuantMode,
     create_qparams,
     fake_quant_tensor,
+    lsq_forward,
     tqt_forward,
 )
 
@@ -150,3 +152,78 @@ def test_fakequant():
     zero_point = tensor(1.0 * np.ones((1, 32, 1, 1)), dtype=np.float32)
     scale = tensor(4.0 * np.ones((1, 32, 1, 1)), dtype=np.float32)
     run(zero_point, scale)
+
+
+class LSQ_numpy:
+    def __init__(self, lowerbound, upperbound):
+        super().__init__()
+        self.lowerbound = lowerbound
+        self.upperbound = upperbound
+
+    def forward(self, inp, scale, zero_point, grad_scale):
+        inp_scaled = inp / scale + zero_point
+        inp_clipped = np.maximum(
+            np.minimum(inp_scaled, self.upperbound), self.lowerbound
+        )
+        inp_rounded = np.floor(inp_clipped + 0.5)
+        inp_flq = (inp_rounded - zero_point) * scale
+        self.saved_tensors = (inp_scaled, inp_rounded, scale, grad_scale)
+        return inp_flq
+
+    def backward(self, grad_inp_flq):
+        (inp_scaled, inp_rounded, scale, grad_scale) = self.saved_tensors
+
+        ind_small = inp_scaled < self.lowerbound
+        ind_big = inp_scaled > self.upperbound
+        ind_middle = np.logical_xor(ind_small, ind_big)
+        ind_middle = np.abs(ind_middle - 1)
+
+        grad_s = (
+            ind_small * self.lowerbound
+            + ind_big * self.upperbound
+            + ind_middle * (-inp_scaled + inp_rounded)
+        )
+        grad_s = grad_s * grad_scale * grad_inp_flq
+        grad_s = grad_s.sum()
+        grad_inp = grad_inp_flq * ind_middle
+
+        return grad_inp, grad_s
+
+
+def test_lsq():
+    def preprocess(scale, eps):
+        scale = np.array([0]) if scale < eps else scale - eps
+        return np.abs(scale) + eps
+
+    g = []
+
+    def cb(grad):
+        g.append(grad)
+
+    x = np.random.randint(-128, 128, size=(1, 2, 3, 4)).astype("float32")
+    s = np.random.rand(1)
+    eps = np.array([1e-5], dtype="float32")
+    s = preprocess(s, eps)
+    zero_point = np.array([1.0], dtype="float32")
+    grad_s = np.array([2.0], dtype="float32")
+
+    g_y = np.ones(shape=(1, 2, 3, 4), dtype="float32")
+
+    n = LSQ_numpy(-127, 127)
+    y_np = n.forward(x, s, zero_point, grad_s)
+    g_x_np, g_s_np = n.backward(g_y)
+
+    x = mge.tensor(x, dtype="float32")
+    s = mge.tensor(s, dtype="float32")
+    zero_point = mge.tensor(zero_point, dtype="float32")
+    grad_s = mge.tensor(grad_s, dtype="float32")
+
+    g_y = mge.tensor(g_y, dtype="float32")
+    grad = Grad().wrt(x, s, callback=cb)
+    y = lsq_forward(-127, 127, x, s, zero_point, grad_s)
+    grad(y, g_y)
+    g_x, g_s = g
+
+    np.testing.assert_allclose(y.numpy(), y_np, rtol=1e-7, atol=1e-7)
+    np.testing.assert_allclose(g_x.numpy(), g_x_np, rtol=1e-7, atol=1e-7)
+    np.testing.assert_allclose(g_s.numpy(), g_s_np, rtol=5e-7, atol=5e-7)

imperative/src/impl/ops/specializations.cpp

@@ -6,23 +6,26 @@
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
  */
 
 // FIXME: split this file into separate files for each specialized op
 
 #include "megbrain/imperative/ops/autogen.h"
-#include "megbrain/opr/dnn/convolution.h"
+#include "megbrain/opr/basic_arith.h"
+#include "megbrain/opr/blas.h"
 #include "megbrain/opr/dnn/adaptive_pooling.h"
+#include "megbrain/opr/dnn/convolution.h"
+#include "megbrain/opr/dnn/correlation.h"
 #include "megbrain/opr/dnn/fake_quant.h"
-#include "megbrain/opr/dnn/tqt.h"
-#include "megbrain/opr/dnn/pooling.h"
+#include "megbrain/opr/dnn/images2neibs.h"
 #include "megbrain/opr/dnn/local.h"
+#include "megbrain/opr/dnn/lsq.h"
+#include "megbrain/opr/dnn/pooling.h"
 #include "megbrain/opr/dnn/roi_align.h"
-#include "megbrain/opr/dnn/correlation.h"
 #include "megbrain/opr/dnn/roi_pooling.h"
-#include "megbrain/opr/basic_arith.h"
-#include "megbrain/opr/blas.h"
+#include "megbrain/opr/dnn/tqt.h"
 #include "megbrain/opr/imgproc.h"
 #include "megbrain/opr/indexing.h"
 #include "megbrain/opr/io.h"
@@ -32,40 +35,38 @@
 #include "megbrain/opr/tensor_gen.h"
 #include "megbrain/opr/tensor_manip.h"
 #include "megbrain/opr/utility.h"
-#include "megbrain/opr/dnn/images2neibs.h"
 
 #include "../op_trait.h"
 
 namespace mgb::imperative {
 
-namespace { namespace dimshuffle {
+namespace {
+namespace dimshuffle {
 std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* node_) {
     auto* node = &node_->cast_final_safe<opr::Dimshuffle>();
     std::vector<int> pattern(node->param().pattern_len);
-    for (size_t i = 0; i < node->param().pattern_len; ++ i) {
+    for (size_t i = 0; i < node->param().pattern_len; ++i) {
         pattern[i] = node->param().pattern[i];
     }
     return Dimshuffle::make(pattern);
 }
 
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& ds = static_cast<const Dimshuffle&>(def);
     OperatorNodeConfig config{ds.make_name()};
     return opr::Dimshuffle::make(inputs[0], ds.pattern, 0UL, config);
 }
 
 OP_TRAIT_REG(Dimshuffle, Dimshuffle, opr::Dimshuffle)
-    .make_from_op_node(make_from_op_node)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // dimshuffle
+        .make_from_op_node(make_from_op_node)
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace dimshuffle
+}  // namespace
 
-namespace { namespace add_axis {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace add_axis {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& add_axis = static_cast<const AddAxis&>(def);
     using Desc = opr::AxisAddRemove::AxisDesc;
     std::vector<Desc> param;
@@ -76,15 +77,13 @@ auto apply_on_var_node(
     return opr::AxisAddRemove::make(inputs[0], param, config);
 }
 
-OP_TRAIT_REG(AddAxis, AddAxis)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // add_axis
+OP_TRAIT_REG(AddAxis, AddAxis).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace add_axis
+}  // namespace
 
-namespace { namespace remove_axis {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace remove_axis {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& remove_axis = static_cast<const RemoveAxis&>(def);
     using Desc = opr::AxisAddRemove::AxisDesc;
     std::vector<Desc> param;
@@ -96,36 +95,35 @@ auto apply_on_var_node(
 }
 
 OP_TRAIT_REG(RemoveAxis, RemoveAxis)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // remove_axis
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace remove_axis
+}  // namespace
 
-namespace { namespace top_k {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace top_k {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& topk = static_cast<const TopK&>(def);
     OperatorNodeConfig config{topk.make_name()};
     return opr::TopK::make(inputs[0], inputs[1], topk.param(), config)[0]
-            .node()->owner_opr();
+            .node()
+            ->owner_opr();
 }
 
-OP_TRAIT_REG(TopK, TopK)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // top_k
+OP_TRAIT_REG(TopK, TopK).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace top_k
+}  // namespace
 
-namespace { namespace reduce {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace reduce {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& reduce = static_cast<const Reduce&>(def);
     OperatorNodeConfig config{reduce.make_name()};
     if (inputs.size() > 1) {
         return opr::Reduce::make(inputs[0], reduce.param(), inputs[1], config);
     } else {
-        return opr::Reduce::make(
-            inputs[0], reduce.param(), (cg::VarNode*)nullptr, config);
+        return opr::Reduce::make(inputs[0], reduce.param(),
+                                 (cg::VarNode*)nullptr, config);
     }
 }
 
@@ -135,86 +133,92 @@ std::shared_ptr<OpDef> make_from_op_node(cg::OperatorNodeBase* node_) {
 }
 
 OP_TRAIT_REG(Reduce, Reduce, opr::Reduce)
-    .make_from_op_node(make_from_op_node)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // reduce
+        .make_from_op_node(make_from_op_node)
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace reduce
+}  // namespace
 
-namespace { namespace adaptive_pooling {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace adaptive_pooling {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& pool = static_cast<const AdaptivePooling&>(def);
     OperatorNodeConfig config{pool.make_name()};
-    return opr::AdaptivePooling::make(inputs[0], inputs[1], pool.param(), config);
+    return opr::AdaptivePooling::make(inputs[0], inputs[1], pool.param(),
+                                      config);
 }
 
 OP_TRAIT_REG(AdaptivePooling, AdaptivePooling)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // adaptive_pooling
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace adaptive_pooling
+}  // namespace
 
-namespace { namespace conv_bias {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace conv_bias {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& conv = static_cast<const ConvBias&>(def);
     cg::OperatorNodeConfig config{conv.dtype};
     config.name(conv.make_name());
     if (inputs.size() == 2) {
-        return opr::ConvBias::make(inputs[0], inputs[1], conv.param(), conv.policy(), config);
+        return opr::ConvBias::make(inputs[0], inputs[1], conv.param(),
+                                   conv.policy(), config);
     } else if (inputs.size() == 3) {
-        return opr::ConvBias::make(inputs[0], inputs[1], inputs[2], conv.param(), conv.policy(), config);
+        return opr::ConvBias::make(inputs[0], inputs[1], inputs[2],
+                                   conv.param(), conv.policy(), config);
     } else if (inputs.size() == 4) {
-        return opr::ConvBias::make(inputs[0], inputs[1], inputs[2], inputs[3], conv.param(), conv.policy(), config);
+        return opr::ConvBias::make(inputs[0], inputs[1], inputs[2], inputs[3],
+                                   conv.param(), conv.policy(), config);
     }
     mgb_assert(0);
 }
 
 OP_TRAIT_REG(ConvBias, ConvBias)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // conv_bias
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace conv_bias
+}  // namespace
 
-namespace { namespace batch_conv_bias {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace batch_conv_bias {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& conv = static_cast<const BatchConvBias&>(def);
     cg::OperatorNodeConfig config{conv.dtype};
     config.name(conv.make_name());
     if (inputs.size() == 2) {
-        return opr::BatchConvBias::make(inputs[0], inputs[1], conv.param(), conv.policy(), config);
+        return opr::BatchConvBias::make(inputs[0], inputs[1], conv.param(),
+                                        conv.policy(), config);
     } else if (inputs.size() == 3) {
-        return opr::BatchConvBias::make(inputs[0], inputs[1], inputs[2], conv.param(), conv.policy(), config);
+        return opr::BatchConvBias::make(inputs[0], inputs[1], inputs[2],
+                                        conv.param(), conv.policy(), config);
     } else if (inputs.size() == 4) {
-        return opr::BatchConvBias::make(inputs[0], inputs[1], inputs[2], inputs[3], conv.param(), conv.policy(), config);
+        return opr::BatchConvBias::make(inputs[0], inputs[1], inputs[2],
+                                        inputs[3], conv.param(), conv.policy(),
+                                        config);
     }
     mgb_assert(0);
 }
 
 OP_TRAIT_REG(BatchConvBias, BatchConvBias)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // batch_conv_bias
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace batch_conv_bias
+}  // namespace
 
-namespace { namespace pooling {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace pooling {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& pool = static_cast<const Pooling&>(def);
     OperatorNodeConfig config{pool.make_name()};
     return opr::Pooling::make(inputs[0], pool.param(), config);
 }
-OP_TRAIT_REG(Pooling, Pooling)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // pooling
+OP_TRAIT_REG(Pooling, Pooling).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace pooling
+}  // namespace
 
-namespace { namespace matrix_mul {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace matrix_mul {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& matmul = static_cast<const MatrixMul&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{matmul.make_name()};
@@ -222,14 +226,14 @@ auto apply_on_var_node(
                                 matmul.policy(), config);
 }
 OP_TRAIT_REG(MatrixMul, MatrixMul)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // matrix_mul
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace matrix_mul
+}  // namespace
 
-namespace { namespace batched_matrix_mul {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace batched_matrix_mul {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& matmul = static_cast<const BatchedMatrixMul&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{matmul.make_name()};
@@ -237,166 +241,155 @@ auto apply_on_var_node(
                                        matmul.policy(), config);
 }
 OP_TRAIT_REG(BatchedMatrixMul, BatchedMatrixMul)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // batched_matrix_mul
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace batched_matrix_mul
+}  // namespace
 
-namespace { namespace dot {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace dot {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = def.cast_final_safe<Dot>();
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{op.make_name()};
     return opr::Dot::make(inputs[0], inputs[1], config);
 }
-OP_TRAIT_REG(Dot, Dot)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // dot
+OP_TRAIT_REG(Dot, Dot).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace dot
+}  // namespace
 
-namespace { namespace argsort {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace argsort {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& argsort = static_cast<const Argsort&>(def);
     OperatorNodeConfig config{argsort.make_name()};
     return opr::Argsort::make(inputs[0], argsort.param(), config);
 }
-OP_TRAIT_REG(Argsort, Argsort)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // argsort
+OP_TRAIT_REG(Argsort, Argsort).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace argsort
+}  // namespace
 
-namespace { namespace argmax {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace argmax {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& argmax = static_cast<const Argmax&>(def);
     OperatorNodeConfig config{argmax.make_name()};
     return opr::Argmax::make(inputs[0], argmax.param(), config);
 }
-OP_TRAIT_REG(Argmax, Argmax)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // argmax
+OP_TRAIT_REG(Argmax, Argmax).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace argmax
+}  // namespace
 
-namespace { namespace argmin {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace argmin {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& argmin = static_cast<const Argmin&>(def);
     OperatorNodeConfig config{argmin.make_name()};
     return opr::Argmin::make(inputs[0], argmin.param(), config);
 }
-OP_TRAIT_REG(Argmin, Argmin)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // argmin
+OP_TRAIT_REG(Argmin, Argmin).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace argmin
+}  // namespace
 
-namespace { namespace warp_perspective {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace warp_perspective {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& warp = static_cast<const WarpPerspective&>(def);
     OperatorNodeConfig config{warp.make_name()};
     if (inputs.size() == 3) {
-        return opr::WarpPerspective::make(inputs[0], inputs[1], inputs[2], warp.param(), config);
+        return opr::WarpPerspective::make(inputs[0], inputs[1], inputs[2],
+                                          warp.param(), config);
     } else {
         mgb_assert(inputs.size() == 4);
-        return opr::WarpPerspective::make(
-            inputs[0], inputs[1], inputs[2], inputs[3], warp.param(), config);
+        return opr::WarpPerspective::make(inputs[0], inputs[1], inputs[2],
+                                          inputs[3], warp.param(), config);
     }
 }
 OP_TRAIT_REG(WarpPerspective, WarpPerspective)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // warp_perspective
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace warp_perspective
+}  // namespace
 
-namespace { namespace group_local {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace group_local {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& local = static_cast<const GroupLocal&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{local.make_name()};
     return opr::GroupLocal::make(inputs[0], inputs[1], local.param(), config);
 }
 OP_TRAIT_REG(GroupLocal, GroupLocal)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // group_local
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace group_local
+}  // namespace
 
-namespace { namespace indexing_one_hot {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace indexing_one_hot {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const IndexingOneHot&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{op.make_name()};
     return opr::IndexingOneHot::make(inputs[0], inputs[1], op.param(), config);
 }
 OP_TRAIT_REG(IndexingOneHot, IndexingOneHot)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // indexing_one_hot
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace indexing_one_hot
+}  // namespace
 
-namespace { namespace indexing_set_one_hot {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace indexing_set_one_hot {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const IndexingSetOneHot&>(def);
     mgb_assert(inputs.size() == 3);
     OperatorNodeConfig config{op.make_name()};
-    return opr::IndexingSetOneHot::make(inputs[0], inputs[1], inputs[2], op.param(), config);
+    return opr::IndexingSetOneHot::make(inputs[0], inputs[1], inputs[2],
+                                        op.param(), config);
 }
 OP_TRAIT_REG(IndexingSetOneHot, IndexingSetOneHot)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // indexing_set_one_hot
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace indexing_set_one_hot
+}  // namespace
 
-namespace { namespace typecvt {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace typecvt {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const TypeCvt&>(def);
     mgb_assert(inputs.size() == 1);
     OperatorNodeConfig config{op.make_name()};
     return opr::TypeCvt::make(inputs[0], op.dtype, config);
 }
-OP_TRAIT_REG(TypeCvt, TypeCvt)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // typecvt
+OP_TRAIT_REG(TypeCvt, TypeCvt).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace typecvt
+}  // namespace
 
-namespace { namespace concat {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace concat {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const Concat&>(def);
     cg::OperatorNodeConfig config{op.comp_node};
     config.name(op.make_name());
     return opr::Concat::make(inputs, op.axis, config);
 }
-OP_TRAIT_REG(Concat, Concat)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // concat
+OP_TRAIT_REG(Concat, Concat).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace concat
+}  // namespace
 
-namespace { namespace copy {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace copy {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const Copy&>(def);
     mgb_assert(inputs.size() == 1);
     cg::OperatorNodeConfig config{op.comp_node};
     config.name(op.make_name());
     return opr::Copy::make(inputs[0], config);
 }
-OP_TRAIT_REG(Copy, Copy)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // copy
+OP_TRAIT_REG(Copy, Copy).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace copy
+}  // namespace
 
 namespace { namespace assert_equal {
 auto apply_on_var_node(
@@ -408,81 +401,81 @@ auto apply_on_var_node(
     } else {
         // workaround for MiniGraph, which only allow one opr in the graph
         mgb_assert(inputs.size() == 3);
-        return opr::AssertEqual::make(inputs[0], inputs[1], inputs[2], op.param(), {});
+        return opr::AssertEqual::make(inputs[0], inputs[1], inputs[2],
+                                      op.param(), {});
     }
 }
 
 OP_TRAIT_REG(AssertEqual, AssertEqual)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // assert_equal
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace assert_equal
+}  // namespace
 
-namespace { namespace roi_align {
-VarNodeArray apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace roi_align {
+VarNodeArray apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const ROIAlign&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{op.make_name()};
-    auto* opr = opr::ROIAlign::make(
-        inputs[0], inputs[1], op.param(), config).node()->owner_opr();
+    auto* opr = opr::ROIAlign::make(inputs[0], inputs[1], op.param(), config)
+                        .node()
+                        ->owner_opr();
     return {opr->output(0), opr->output(1)};
 }
 OP_TRAIT_REG(ROIAlign, ROIAlign)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // roi_align
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace roi_align
+}  // namespace
 
-namespace { namespace correlation {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace correlation {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const Correlation&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{op.make_name()};
-    return opr::Correlation::make(
-        inputs[0], inputs[1], op.param(), config);
+    return opr::Correlation::make(inputs[0], inputs[1], op.param(), config);
 }
 OP_TRAIT_REG(Correlation, Correlation)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // correlation
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace correlation
+}  // namespace
 
 #if MGB_CUDA
-namespace { namespace nvof {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace nvof {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const NvOf&>(def);
     mgb_assert(inputs.size() == 1);
     OperatorNodeConfig config{op.make_name()};
     return opr::NvOf::make(inputs[0], op.param(), config);
 }
-OP_TRAIT_REG(NvOf, NvOf)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // nvof
+OP_TRAIT_REG(NvOf, NvOf).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace nvof
+}  // namespace
 #endif
 
-namespace { namespace linspace {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace linspace {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const Linspace&>(def);
     mgb_assert(inputs.size() == 3);
     cg::OperatorNodeConfig config{op.comp_node};
     config.name(op.make_name());
-    return opr::Linspace::make(inputs[0], inputs[1], inputs[2], op.param(), config);
+    return opr::Linspace::make(inputs[0], inputs[1], inputs[2], op.param(),
+                               config);
 }
 OP_TRAIT_REG(Linspace, Linspace)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // linspace
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace linspace
+}  // namespace
 
-namespace { namespace eye {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace eye {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const Eye&>(def);
     mgb_assert(inputs.size() == 1);
     cg::OperatorNodeConfig config{op.comp_node};
@@ -490,58 +483,59 @@ auto apply_on_var_node(
     opr::Eye::Param param{op.k, op.dtype.enumv()};
     return opr::Eye::make(inputs[0], param, config);
 }
-OP_TRAIT_REG(Eye, Eye)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // eye
+OP_TRAIT_REG(Eye, Eye).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace eye
+}  // namespace
 
-namespace { namespace roi_pooling {
-VarNodeArray apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace roi_pooling {
+VarNodeArray apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const ROIPooling&>(def);
     mgb_assert(inputs.size() == 3);
     OperatorNodeConfig config{op.make_name()};
-    auto* opr = opr::ROIPooling::make(
-        inputs[0], inputs[1], inputs[2], op.param(), config
-    ).node()->owner_opr();
+    auto* opr = opr::ROIPooling::make(inputs[0], inputs[1], inputs[2],
+                                      op.param(), config)
+                        .node()
+                        ->owner_opr();
     return {opr->output(0), opr->output(1)};
 }
 OP_TRAIT_REG(ROIPooling, ROIPooling)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // roi_pooling
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace roi_pooling
+}  // namespace
 
-namespace { namespace remap {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace remap {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const Remap&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{op.make_name()};
     return opr::Remap::make(inputs[0], inputs[1], op.param(), config);
 }
-OP_TRAIT_REG(Remap, Remap)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // remap
+OP_TRAIT_REG(Remap, Remap).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace remap
+}  // namespace
 
 namespace {
 auto get_index(
-    const VarNodeArray& inputs, size_t vidx,
-    const std::vector<std::tuple<int8_t, bool, bool, bool, bool>>& mask) {
+        const VarNodeArray& inputs, size_t vidx,
+        const std::vector<std::tuple<int8_t, bool, bool, bool, bool>>& mask) {
     size_t length = mask.size();
     opr::Subtensor::IndexDesc ret(length);
-    for (size_t i = 0; i < length; ++ i) {
+    for (size_t i = 0; i < length; ++i) {
         auto&& [axis, begin, end, step, idx] = mask[i];
         ret[i].axis = axis;
         if (idx) {
             ret[i].idx = inputs[vidx++];
         } else {
             mgb_assert(begin || end || step);
-            if (begin) ret[i].begin = inputs[vidx++];
-            if (end) ret[i].end = inputs[vidx++];
-            if (step) ret[i].step = inputs[vidx++];
+            if (begin)
+                ret[i].begin = inputs[vidx++];
+            if (end)
+                ret[i].end = inputs[vidx++];
+            if (step)
+                ret[i].step = inputs[vidx++];
         }
     }
     mgb_assert(vidx == inputs.size());
@@ -550,19 +544,19 @@ auto get_index(
 #define IN1 inputs[0]
 #define IN2 inputs[0], inputs[1]
 
-#define FANCY_INDEXING_IMPL(NAME, NR_INPUT) \
-namespace NAME##_impl { \
-auto apply_on_var_node( \
-        const OpDef& def, \
-        const VarNodeArray& inputs) { \
-    auto&& op = static_cast<const NAME&>(def); \
-    OperatorNodeConfig config{op.make_name()}; \
-    return opr::NAME::make(IN##NR_INPUT, get_index(inputs, NR_INPUT, op.items), config); \
-} \
-OP_TRAIT_REG(NAME, NAME) \
-    .apply_on_var_node(apply_on_var_node) \
-    .fallback(); \
-}
+#define FANCY_INDEXING_IMPL(NAME, NR_INPUT)                                    \
+    namespace NAME##_impl {                                                    \
+        auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) { \
+            auto&& op = static_cast<const NAME&>(def);                         \
+            OperatorNodeConfig config{op.make_name()};                         \
+            return opr::NAME::make(IN##NR_INPUT,                               \
+                                   get_index(inputs, NR_INPUT, op.items),      \
+                                   config);                                    \
+        }                                                                      \
+        OP_TRAIT_REG(NAME, NAME)                                               \
+                .apply_on_var_node(apply_on_var_node)                          \
+                .fallback();                                                   \
+    }
 
 FANCY_INDEXING_IMPL(Subtensor, 1)
 FANCY_INDEXING_IMPL(SetSubtensor, 2)
@@ -580,76 +574,88 @@ FANCY_INDEXING_IMPL(BatchedSetMeshIndexing, 2)
 #undef FANCY_INDEXING_IMPL
 #undef IN1
 #undef IN2
-} // anonymous namespace
+}  // anonymous namespace
 
-namespace { namespace fake_quant {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace fake_quant {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const FakeQuant&>(def);
     mgb_assert(inputs.size() == 3);
     OperatorNodeConfig config{op.make_name()};
-    return opr::FakeQuant::make(inputs[0], inputs[1], inputs[2], op.param(), config);
+    return opr::FakeQuant::make(inputs[0], inputs[1], inputs[2], op.param(),
+                                config);
 }
 OP_TRAIT_REG(FakeQuant, FakeQuant)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // fake_quant
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace fake_quant
+}  // namespace
 
-namespace { namespace tqt {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace tqt {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const TQT&>(def);
     mgb_assert(inputs.size() == 2);
     OperatorNodeConfig config{op.make_name()};
     return opr::TQT::make(inputs[0], inputs[1], op.param(), config);
 }
-OP_TRAIT_REG(TQT, TQT)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}}  // tqt
+OP_TRAIT_REG(TQT, TQT).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace tqt
+}  // namespace
 
-namespace { namespace elemwise_multi_type {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace elemwise_multi_type {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const ElemwiseMultiType&>(def);
     OperatorNodeConfig config{op.dtype};
     config.name(op.make_name());
     return opr::ElemwiseMultiType::make(inputs, op.param(), config);
 }
 OP_TRAIT_REG(ElemwiseMultiType, ElemwiseMultiType)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // elemwise_multi_type
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace elemwise_multi_type
+}  // namespace
 
-namespace { namespace svd {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace svd {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const SVD&>(def);
     mgb_assert(inputs.size() == 1);
     OperatorNodeConfig config{op.make_name()};
     return opr::SVD::make(inputs[0], op.param(), config)[0]
-        .node()->owner_opr()->usable_output();
+            .node()
+            ->owner_opr()
+            ->usable_output();
 }
-OP_TRAIT_REG(SVD, SVD)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // svd
+OP_TRAIT_REG(SVD, SVD).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace svd
+}  // namespace
 
-namespace { namespace images2neibs {
-auto apply_on_var_node(
-        const OpDef& def,
-        const VarNodeArray& inputs) {
+namespace {
+namespace images2neibs {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
     auto&& op = static_cast<const Images2Neibs&>(def);
     OperatorNodeConfig config{op.make_name()};
     return opr::Images2Neibs::make(inputs[0], op.param(), config);
 }
 OP_TRAIT_REG(Images2Neibs, Images2Neibs)
-    .apply_on_var_node(apply_on_var_node)
-    .fallback();
-}} // images2neibs
+        .apply_on_var_node(apply_on_var_node)
+        .fallback();
+}  // namespace images2neibs
+}  // namespace
+
+namespace {
+namespace lsq {
+auto apply_on_var_node(const OpDef& def, const VarNodeArray& inputs) {
+    auto&& op = static_cast<const LSQ&>(def);
+    mgb_assert(inputs.size() == 4);
+    OperatorNodeConfig config{op.make_name()};
+    return opr::LSQ::make(inputs[0], inputs[1], inputs[2], inputs[3],
+                          op.param(), config);
+}
+OP_TRAIT_REG(LSQ, LSQ).apply_on_var_node(apply_on_var_node).fallback();
+}  // namespace lsq
+}  // namespace
 
-} // namespace mgb::imperative
+}  // namespace mgb::imperative

imperative/src/test/backward_graph.cpp

@@ -6,22 +6,24 @@
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
  */
 
 #include "./helper.h"
+#include "megbrain/imperative/backward_graph_opt.h"
+#include "megbrain/imperative/ops/autogen.h"
+#include "megbrain/imperative/ops/opr_attr.h"
 #include "megbrain/opr/basic_arith.h"
 #include "megbrain/opr/dnn/batch_norm.h"
-#include "megbrain/imperative/ops/opr_attr.h"
-#include "megbrain/imperative/ops/autogen.h"
-#include "megbrain/imperative/backward_graph_opt.h"
 
 using namespace mgb;
 using namespace cg;
 using namespace imperative;
 
 template <typename T>
-T prepare_backward_graph_inputs(const BackwardGraphResult& bg, const T& inputs, const T& outputs, const T& grads) {
+T prepare_backward_graph_inputs(const BackwardGraphResult& bg, const T& inputs,
+                                const T& outputs, const T& grads) {
     T ret;
     size_t i = 0;
     for (auto&& t : inputs) {
@@ -54,7 +56,9 @@ T expand_grads(const U& bg, const T& outputs) {
 }
 
 template <typename T>
-T prepare_optimized_backward_inputs(const OptimizedBackwardGraphResult& bg, const T& precomp, const T& inputs, const T& outputs, const T& grads) {
+T prepare_optimized_backward_inputs(const OptimizedBackwardGraphResult& bg,
+                                    const T& precomp, const T& inputs,
+                                    const T& outputs, const T& grads) {
     T ret = precomp;
     size_t i = 0;
     for (auto&& t : inputs) {
@@ -75,7 +79,8 @@ T prepare_optimized_backward_inputs(const OptimizedBackwardGraphResult& bg, cons
     return ret;
 }
 
-SmallVector<TensorPtr> apply_shared_on_physical_tensor(std::shared_ptr<OpDef> def, SmallVector<TensorPtr> inputs) {
+SmallVector<TensorPtr> apply_shared_on_physical_tensor(
+        std::shared_ptr<OpDef> def, SmallVector<TensorPtr> inputs) {
     return OpDef::apply_on_physical_tensor(*def, inputs);
 }
 
@@ -83,7 +88,7 @@ TEST(TestImperative, BackwardGraphBasic) {
     HostTensorGenerator<> gen;
     SmallVector<HostTensorND> hvs;
     SmallVector<TensorPtr> inputs;
-    for(size_t i = 0; i < 2; ++ i) {
+    for (size_t i = 0; i < 2; ++i) {
         hvs.push_back(*gen({42}));
         inputs.push_back(Tensor::make(hvs.back()));
     }
@@ -97,7 +102,8 @@ TEST(TestImperative, BackwardGraphBasic) {
     for (auto&& i : inputs) {
         input_descs.push_back({i->layout(), i->comp_node()});
     }
-    auto result = OpDef::make_backward_graph(*attr, input_descs, {true, true}, {true});
+    auto result = OpDef::make_backward_graph(*attr, input_descs, {true, true},
+                                             {true});
     auto&& save_for_backward = result.save_for_backward;
     auto&& input_has_grad = result.input_has_grad;
 
@@ -106,9 +112,9 @@ TEST(TestImperative, BackwardGraphBasic) {
     hvs.push_back(*gen({42}));
     inputs.push_back(Tensor::make(hvs.back()));
     mgb_assert(save_for_backward.size() == inputs.size());
-    for (size_t i = 0; i < inputs.size(); ++ i) {
+    for (size_t i = 0; i < inputs.size(); ++i) {
         if (!save_for_backward[i]) {
-            inputs[i].reset(); // drop unused tensor
+            inputs[i].reset();  // drop unused tensor
         }
     }
     SmallVector<TensorPtr> backward_graph_inputs;
@@ -118,13 +124,11 @@ TEST(TestImperative, BackwardGraphBasic) {
         }
     }
     inputs.clear();
-    auto input_grads = result.backward.apply(
-            backward_graph_inputs,
-            apply_shared_on_physical_tensor,
-            [&](auto&& x){ return x; }
-    );
+    auto input_grads = result.backward.apply(backward_graph_inputs,
+                                             apply_shared_on_physical_tensor,
+                                             [&](auto&& x) { return x; });
     mgb_assert(input_grads.size() == input_has_grad.size());
-    for (size_t i = 0; i < input_has_grad.size(); ++ i) {
+    for (size_t i = 0; i < input_has_grad.size(); ++i) {
         mgb_assert(input_has_grad[i] == static_cast<bool>(input_grads[i]));
     }
 
@@ -133,9 +137,10 @@ TEST(TestImperative, BackwardGraphBasic) {
         res.emplace_back();
         res.back().copy_from(i->dev_tensor()).sync();
     }
-    for (size_t i = 0; i < 42; ++ i) {
-        for (size_t j = 0; j < 1; ++ j) {
-            ASSERT_EQ(hvs[2].ptr<float>()[i] * hvs[j].ptr<float>()[i], res[j ^ 1].ptr<float>()[i]);
+    for (size_t i = 0; i < 42; ++i) {
+        for (size_t j = 0; j < 1; ++j) {
+            ASSERT_EQ(hvs[2].ptr<float>()[i] * hvs[j].ptr<float>()[i],
+                      res[j ^ 1].ptr<float>()[i]);
         }
     }
 }
@@ -152,7 +157,8 @@ TEST(TestImperative, BackwardGraphIdentity) {
 
     SmallVector<LogicalTensorDesc> input_descs;
     input_descs.push_back({a->layout(), a->comp_node()});
-    auto result = OpDef::make_backward_graph(*attr, input_descs, {true}, {true});
+    auto result =
+            OpDef::make_backward_graph(*attr, input_descs, {true}, {true});
     auto&& save_for_backward = result.save_for_backward;
     auto&& input_has_grad = result.input_has_grad;
 
@@ -160,9 +166,9 @@ TEST(TestImperative, BackwardGraphIdentity) {
     inputs.push_back(outputs[0]);
     inputs.push_back(dc);
     mgb_assert(save_for_backward.size() == inputs.size());
-    for (size_t i = 0; i < inputs.size(); ++ i) {
+    for (size_t i = 0; i < inputs.size(); ++i) {
         if (!save_for_backward[i]) {
-            inputs[i].reset(); // drop unused tensor
+            inputs[i].reset();  // drop unused tensor
         }
     }
     SmallVector<TensorPtr> backward_graph_inputs;
@@ -172,19 +178,17 @@ TEST(TestImperative, BackwardGraphIdentity) {
         }
     }
     inputs.clear();
-    auto input_grads = result.backward.apply(
-            backward_graph_inputs,
-            apply_shared_on_physical_tensor,
-            [&](auto&& x){ return x; }
-    );
+    auto input_grads = result.backward.apply(backward_graph_inputs,
+                                             apply_shared_on_physical_tensor,
+                                             [&](auto&& x) { return x; });
     mgb_assert(input_grads.size() == input_has_grad.size());
-    for (size_t i = 0; i < input_has_grad.size(); ++ i) {
+    for (size_t i = 0; i < input_has_grad.size(); ++i) {
         mgb_assert(input_has_grad[i] == static_cast<bool>(input_grads[i]));
     }
 
     HostTensorND hv;
     hv.copy_from(input_grads[0]->dev_tensor()).sync();
-    for (size_t i = 0; i < 42; ++ i) {
+    for (size_t i = 0; i < 42; ++i) {
         ASSERT_EQ(host_dc->ptr<float>()[i], hv.ptr<float>()[i]);
     }
 }
@@ -192,7 +196,7 @@ TEST(TestImperative, BackwardGraphIdentity) {
 TEST(TestImperative, BatchNormGrad) {
     auto cn = CompNode::load("xpux");
     using Param = opr::BatchNorm::Param;
-    size_t N=2, C=3, H=5, W=5;
+    size_t N = 2, C = 3, H = 5, W = 5;
     LogicalTensorDesc inp{TensorLayout{{N, C, H, W}, dtype::Float32()}, cn};
     LogicalTensorDesc stat{TensorLayout{{C}, dtype::Float32()}, cn};
     {
@@ -202,7 +206,8 @@ TEST(TestImperative, BatchNormGrad) {
         param.fwd_mode = Param::FwdMode::TRAINING;
         attr.param.write_pod(param);
         OpDef::make_backward_graph(attr, {inp, stat, stat, stat, stat},
-            {true, true ,true, false, false}, {false, false, false, false, true});
+                                   {true, true, true, false, false},
+                                   {false, false, false, false, true});
     }
     {
         auto op = OprAttr::make("BatchNorm");
@@ -210,8 +215,8 @@ TEST(TestImperative, BatchNormGrad) {
         Param param;
         param.fwd_mode = Param::FwdMode::TRAINING;
         attr.param.write_pod(param);
-        OpDef::make_backward_graph(attr, {inp, stat, stat},
-            {true, true ,true}, {false, false, true});
+        OpDef::make_backward_graph(attr, {inp, stat, stat}, {true, true, true},
+                                   {false, false, true});
     }
 }
 
@@ -220,7 +225,8 @@ TEST(TestImperative, OptimizedBackwardGraphBasic) {
     LogicalTensorDesc desc = {TensorLayout(dtype::Float32()), cn};
     HostTensorGenerator<> gen;
     auto op = std::shared_ptr<OpDef>(Elemwise::make(Elemwise::Mode::ADD));
-    auto bg = OpDef::make_backward_graph(*op, {desc, desc}, {true, true}, {true});
+    auto bg =
+            OpDef::make_backward_graph(*op, {desc, desc}, {true, true}, {true});
     auto obg = OptimizedBackwardGraphResult(bg);
     ASSERT_EQ(obg.save_for_backward.size(), 4);
     ASSERT_FALSE(obg.save_for_backward[0]);
@@ -235,30 +241,30 @@ TEST(TestImperative, OptimizedBackwardGraphBasic) {
     auto dc_tn = Tensor::make(*dc_hv);
     auto c_tn = OpDef::apply_on_physical_tensor(*op, {a_tn, b_tn})[0];
 
-    auto backward_graph_inputs = prepare_backward_graph_inputs<SmallVector<TensorPtr>>(bg, {a_tn, b_tn}, {c_tn}, {dc_tn});
-    auto grads = expand_grads(bg, bg.backward.apply(
-            backward_graph_inputs,
-            apply_shared_on_physical_tensor,
-            [&](auto&& x){ return x; }
-    ));
+    auto backward_graph_inputs =
+            prepare_backward_graph_inputs<SmallVector<TensorPtr>>(
+                    bg, {a_tn, b_tn}, {c_tn}, {dc_tn});
+    auto grads =
+            expand_grads(bg, bg.backward.apply(backward_graph_inputs,
+                                               apply_shared_on_physical_tensor,
+                                               [&](auto&& x) { return x; }));
 
-    auto precomp = obg.precomp.apply(
-            SmallVector<TensorPtr>{a_tn, b_tn, c_tn},
-            apply_shared_on_physical_tensor,
-            [&](auto&& x){ return x; }
-    );
+    auto precomp = obg.precomp.apply(SmallVector<TensorPtr>{a_tn, b_tn, c_tn},
+                                     apply_shared_on_physical_tensor,
+                                     [&](auto&& x) { return x; });
     ASSERT_EQ(precomp.size(), 2);
     ASSERT_EQ(precomp[0]->shape().ndim, 1);
     ASSERT_LE(precomp[0]->shape()[0], 2);
     ASSERT_EQ(precomp[1]->shape().ndim, 1);
     ASSERT_LE(precomp[1]->shape()[0], 2);
 
-    auto backward_inputs = prepare_optimized_backward_inputs<SmallVector<TensorPtr>>(obg, precomp, {a_tn, b_tn}, {c_tn}, {dc_tn});
-    auto grads2 = expand_grads(obg, obg.backward.apply(
-            backward_inputs,
-            apply_shared_on_physical_tensor,
-            [&](auto&& x){ return x; }
-    ));
+    auto backward_inputs =
+            prepare_optimized_backward_inputs<SmallVector<TensorPtr>>(
+                    obg, precomp, {a_tn, b_tn}, {c_tn}, {dc_tn});
+    auto grads2 = expand_grads(
+            obg,
+            obg.backward.apply(backward_inputs, apply_shared_on_physical_tensor,
+                               [&](auto&& x) { return x; }));
 
     ASSERT_EQ(grads2.size(), 2);
     MGB_ASSERT_TENSOR_EQ(grads[0]->get_value(), grads2[0]->get_value());

src/core/include/megbrain/ir/ops.td

@@ -271,6 +271,7 @@ def BatchedSetMeshIndexing: FancyIndexingBase<"BatchedSetMeshIndexing">;
 def FakeQuant: MgbHashableOp<"FakeQuant", [FakeQuantParam]>;
 def AssertEqual: MgbHashableOp<"AssertEqual",[AssertEqualParam]>;
 def TQT: MgbHashableOp<"TQT", [TQTParam]>;
+def LSQ: MgbHashableOp<"LSQ", [LSQParam]>;
 def ElemwiseMultiType: MgbHashableOp<"ElemwiseMultiType", [ElemwiseMultiTypeParam]> {
   let extraArguments = (ins
     MgbDTypeAttr:$dtype

src/opr/impl/dnn/dnn.oprdecl

@@ -324,5 +324,7 @@ decl_opr('FakeQuant',
 decl_opr('TQT',
          inputs=[Doc('src','input tensor'),Doc('scale','scale tensor')],
          params='TQT')
-
+decl_opr('LSQ',
+         inputs=[Doc('src','input tensor'),Doc('scale','scale tensor'),Doc('zero_point','zero point tensor'),Doc('grad_scale','grad scale tensor')],
+         params='LSQ')
 # vim: ft=python

src/opr/impl/dnn/dnn.sereg.h

@@ -6,20 +6,22 @@
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
- * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
  */
 
+#include "megbrain/opr/dnn/adaptive_pooling.h"
 #include "megbrain/opr/dnn/batch_norm.h"
 #include "megbrain/opr/dnn/convolution.h"
 #include "megbrain/opr/dnn/correlation.h"
+#include "megbrain/opr/dnn/fake_quant.h"
 #include "megbrain/opr/dnn/images2neibs.h"
-#include "megbrain/opr/dnn/pooling.h"
-#include "megbrain/opr/dnn/adaptive_pooling.h"
-#include "megbrain/opr/dnn/roi_pooling.h"
-#include "megbrain/opr/dnn/roi_align.h"
 #include "megbrain/opr/dnn/local.h"
 #include "megbrain/opr/dnn/lrn.h"
-#include "megbrain/opr/dnn/fake_quant.h"
+#include "megbrain/opr/dnn/lsq.h"
+#include "megbrain/opr/dnn/pooling.h"
+#include "megbrain/opr/dnn/roi_align.h"
+#include "megbrain/opr/dnn/roi_pooling.h"
 #include "megbrain/opr/dnn/tqt.h"
 #include "megbrain/serialization/sereg.h"
 #include "megdnn/opr_param_defs.h"
@@ -183,7 +185,8 @@ struct ConvLoadDumpImpl {
     static void dump(OprDumpContext& ctx, const cg::OperatorNodeBase& opr_) {
         auto&& opr = opr_.cast_final_safe<Opr>();
         ctx.write_param<ConvParam>(opr.param());
-        ctx.write_param<megdnn::param::ExecutionPolicy>(opr.execution_policy_transient());
+        ctx.write_param<megdnn::param::ExecutionPolicy>(
+                opr.execution_policy_transient());
     }
 
     static VarNode* make(const cg::VarNodeArray& inputs, const ConvParam& param,
@@ -251,6 +254,20 @@ struct OprMaker<opr::TQTBackward, 3> {
     }
 };
 
+template <>
+struct OprMaker<opr::LSQBackward, 5> {
+    using Param = opr::LSQBackward::Param;
+    static cg::OperatorNodeBase* make(const Param& param,
+                                      const cg::VarNodeArray& i,
+                                      ComputingGraph& graph,
+                                      const OperatorNodeConfig& config) {
+        MGB_MARK_USED_VAR(graph);
+        return opr::LSQBackward::make(i[0], i[1], i[2], i[3], i[4], param,
+                                      config)[0]
+                .node()
+                ->owner_opr();
+    }
+};
 template <>
 struct OprLoadDumpImpl<opr::AdaptivePoolingBackward, 0>
         : public PoolingLoadDumpImpl<opr::AdaptivePoolingBackward,
@@ -587,6 +604,8 @@ MGB_SEREG_OPR(FakeQuant, 3);
 MGB_SEREG_OPR(FakeQuantBackward, 4);
 MGB_SEREG_OPR(TQT, 2);
 MGB_SEREG_OPR(TQTBackward, 3);
+MGB_SEREG_OPR(LSQ, 4);
+MGB_SEREG_OPR(LSQBackward, 5);
 }  // namespace opr
 
 

src/opr/impl/dnn/lsq.cpp

@@ -0,0 +1,90 @@
+/**
+ * \file src/opr/impl/dnn/lsq.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "megbrain/opr/dnn/lsq.h"
+#include "../internal/megdnn_opr_wrapper.inl"
+#include "megbrain/graph/grad_impl.h"
+#include "megbrain/opr/basic_arith_wrapper.h"
+#include "megbrain/opr/internal/out_shape_by_sym_var.h"
+#include "megbrain/opr/tensor_manip.h"
+#include "megbrain/opr/utility.h"
+
+using namespace mgb;
+using namespace opr;
+
+MGB_DYN_TYPE_OBJ_FINAL_IMPL(LSQForward);
+MEGDNN_OPR_INIT4(LSQForward, "lsq_fwd");
+
+#ifdef MGB_ENABLE_GRAD
+MGB_IMPL_OPR_GRAD(LSQForward) {
+    SymbolVarArray grad =
+            LSQBackward::make(out_grad[0], opr.input(0), opr.input(1),
+                              opr.input(2), opr.input(3), opr.param());
+
+    if (wrt_idx == 0) {
+        return grad[0].node();
+    } else if (wrt_idx == 1) {
+        return reduce_sum(grad[1], GetVarShape::make(opr.input(wrt_idx)))
+                .node();
+    } else {
+        return nullptr;
+    }
+}
+#endif
+
+MGB_DYN_TYPE_OBJ_FINAL_IMPL(LSQBackward);
+
+LSQBackward::LSQBackward(VarNode* y_grad, VarNode* x, VarNode* scale,
+                         VarNode* zero_point, VarNode* grad_scale,
+                         const Param& param, const OperatorNodeConfig& config)
+        : Super({x->owner_graph(),
+                 config,
+                 "lsq_bwd",
+                 {y_grad, x, scale, zero_point, grad_scale}},
+                1, true) {
+    init_megdnn_opr(*this, param);
+    add_input({y_grad, x, scale, zero_point, grad_scale});
+}
+
+SymbolVarArray LSQBackward::make(SymbolVar y_grad, SymbolVar x, SymbolVar scale,
+                                 SymbolVar zero_point, SymbolVar grad_scale,
+                                 const Param& param,
+                                 const OperatorNodeConfig& config) {
+    auto&& out = x.node()->owner_graph()
+                         ->insert_opr(std::make_unique<LSQBackward>(
+                                 y_grad.node(), x.node(), scale.node(),
+                                 zero_point.node(), grad_scale.node(), param,
+                                 config))
+                         ->output();
+    SymbolVarArray ret(out.size());
+    for (size_t i = 0; i < ret.size(); ++i) {
+        ret[i] = out[i];
+    }
+    return ret;
+}
+
+void LSQBackward::init_output_static_infer_desc() {
+    using namespace cg::static_infer;
+    auto&& mgr = owner_graph()->static_infer_manager();
+
+    mgr.register_shape_infer(output(0),
+                             ShapeInferDesc::make_identity(input(1)));
+    mgr.register_shape_infer(output(1),
+                             ShapeInferDesc::make_identity(input(1)));
+    this->init_output_static_infer_desc_workspace(
+            intl::AutoAddWorkspaceNeedLimitGetter<megdnn::LSQBackward>::val);
+}
+
+void LSQBackward::init_output_dtype() {
+    output(0)->dtype(input(1)->dtype());
+    output(1)->dtype(input(2)->dtype());
+}

src/opr/include/megbrain/opr/dnn/lsq.h

@@ -0,0 +1,50 @@
+/**
+ * \file src/opr/include/megbrain/opr/dnn/lsq.h
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#pragma once
+#include "megbrain/opr/internal/megdnn_opr_wrapper.h"
+#include "megdnn/oprs.h"
+namespace mgb {
+namespace opr {
+
+MGB_DEFINE_OPR_CLASS(LSQForward,
+                     intl::MegDNNOprWrapperFwd<megdnn::LSQForward>)  // {
+public:
+LSQForward(VarNode* src, VarNode* scale, VarNode* zero_point,
+           VarNode* grad_scale, const Param& param,
+           const OperatorNodeConfig& config);
+
+static SymbolVar make(SymbolVar src, SymbolVar scale, SymbolVar zero_point,
+                      SymbolVar grad_scale, const Param& param = {},
+                      const OperatorNodeConfig& config = {});
+};
+using LSQ = LSQForward;
+
+MGB_DEFINE_OPR_CLASS(LSQBackward,
+                     intl::MegDNNOprWrapperBwd<megdnn::LSQBackward>)  // {
+public:
+LSQBackward(VarNode* y_grad, VarNode* x, VarNode* scale, VarNode* zero_point,
+            VarNode* grad_scale, const Param& param,
+            const OperatorNodeConfig& config);
+
+static SymbolVarArray make(SymbolVar y_grad, SymbolVar x, SymbolVar scale,
+                           SymbolVar zero_point, SymbolVar grad_scale,
+                           const Param& param = {},
+                           const OperatorNodeConfig& config = {});
+
+private:
+void init_output_static_infer_desc() override;
+void init_output_dtype() override;
+};
+
+}  // namespace opr
+}  // namespace mgb

src/opr/test/dnn/lsq.cpp

@@ -0,0 +1,78 @@
+/**
+ * \file src/opr/test/dnn/lsq.cpp
+ * MegEngine is Licensed under the Apache License, Version 2.0 (the "License")
+ *
+ * Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or
+ * implied.
+ */
+
+#include "megbrain/opr/dnn/lsq.h"
+#include "megbrain/comp_node_env.h"
+#include "megbrain/test/autocheck.h"
+
+using namespace std;
+using namespace mgb;
+
+namespace {
+
+void run() {
+    using Checker = AutoOprChecker<4, 1>;
+
+    auto make_graph =
+            [&](const Checker::SymInpArray& inputs) -> Checker::SymOutArray {
+        auto o0 = opr::LSQForward::make(inputs[0], inputs[1], inputs[2],
+                                        inputs[3]);
+        return {o0};
+    };
+
+    auto fwd = [&](Checker::NumOutArray& dest, Checker::NumInpArray inp) {
+        auto opr = MegDNNHandle::get(
+                           CompNodeEnv::from_comp_node(CompNode::default_cpu()))
+                           ->create_operator<megdnn::LSQForward>();
+        dest[0].dtype(dtype::Float32())
+                .comp_node(inp[0]->comp_node())
+                .resize(inp[0]->shape());
+        opr->exec(inp[0]->as_megdnn(), inp[1]->as_megdnn(), inp[2]->as_megdnn(),
+                  inp[3]->as_megdnn(), dest[0].as_megdnn(), {});
+    };
+
+    auto gen = [&](HostTensorND& src) {
+        HostTensorGenerator<dtype::Float32, RandomDistribution::GAUSSIAN>
+                src_gen(10.f);
+        src = *src_gen(src.shape(), src.comp_node());
+    };
+
+    Checker::RunOptions opt;
+    opt.numdiff_max_err = 1e-5;
+
+    Checker checker{make_graph, fwd};
+    checker.set_input_generator(0, gen)
+            .set_input_generator(1, gen)
+            .set_input_generator(2, gen)
+            .set_input_generator(3, gen)
+            .set_input_allow_grad(0, false)
+            .set_input_allow_grad(1, false)
+            .set_input_allow_grad(2, false)
+            .set_input_allow_grad(3, false)
+            .set_output_allow_grad(0, false);
+    checker.run({TensorShape{1, 2, 3, 4}, TensorShape{1}, TensorShape{1},
+                 TensorShape{1}},
+                opt)
+            .run({TensorShape{2, 3, 8, 8}, TensorShape{1}, TensorShape{1},
+                  TensorShape{1}},
+                 opt)
+            .run({TensorShape{1, 3, 4, 4}, TensorShape{1}, TensorShape{1},
+                  TensorShape{1}},
+                 opt);
+}
+
+}  // anonymous namespace
+
+TEST(TestOprDNN, LSQForward) {
+    REQUIRE_GPU(1);
+    run();
+}

src/serialization/impl/schema.fbs

@@ -107,6 +107,7 @@ union OperatorParam {
     param.FakeQuant = 73,
     param.TQT = 74,
     param.Correlation = 75,
+    param.LSQ = 76,
 }
 
 table Operator {