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The following commit(s) were added to refs/heads/master by this push:
new ba8a9d1 support softmin operator with unit test (#12306)
ba8a9d1 is described below
commit ba8a9d13e1b549d061f1933c463cfad5e7bdd7aa
Author: Hao Jin <haoj...@users.noreply.github.com>
AuthorDate: Wed Aug 29 10:35:44 2018 -0700
support softmin operator with unit test (#12306)
---
src/operator/contrib/ctc_loss-inl.h | 7 +--
src/operator/nn/softmax-inl.h | 88 ++++++++++++++++++++--------------
src/operator/nn/softmax.cc | 39 +++++++++++++++
src/operator/nn/softmax.cu | 7 +++
tests/python/unittest/test_operator.py | 24 ++++++++--
5 files changed, 123 insertions(+), 42 deletions(-)
diff --git a/src/operator/contrib/ctc_loss-inl.h
b/src/operator/contrib/ctc_loss-inl.h
index 72209ae..9380be4 100644
--- a/src/operator/contrib/ctc_loss-inl.h
+++ b/src/operator/contrib/ctc_loss-inl.h
@@ -409,7 +409,8 @@ class CTCLossOp : public Operator {
// since the input is activation before softmax and cudnn ctc takes softmax
// apply softmax to inputs first.
- mxnet_op::Softmax<mxnet_op::softmax_fwd>(s, data.dptr_, prob.dptr_,
data.shape_, 2, 1.0);
+ mxnet_op::Softmax<mxnet_op::softmax_fwd, false>(
+ s, data.dptr_, prob.dptr_, data.shape_, 2, 1.0);
CUDNN_CALL(cudnnCTCLoss(s->dnn_handle_,
prob_desc_,
@@ -426,8 +427,8 @@ class CTCLossOp : public Operator {
workspace_bytes));
if (req_grad) {
- mxnet_op::SoftmaxGrad<mshadow_op::mul, mxnet_op::softmax_bwd,
kWriteTo>(s,
- prob.dptr_, grad.dptr_, grad.dptr_, data.shape_, 2, 1.0);
+ mxnet_op::SoftmaxGrad<mshadow_op::mul, mxnet_op::softmax_bwd, kWriteTo,
false>(
+ s, prob.dptr_, grad.dptr_, grad.dptr_, data.shape_, 2, 1.0);
Assign(grad, mxnet::kWriteInplace, grad * alphabet_size);
}
}
diff --git a/src/operator/nn/softmax-inl.h b/src/operator/nn/softmax-inl.h
index 4a19db7..c063e38 100644
--- a/src/operator/nn/softmax-inl.h
+++ b/src/operator/nn/softmax-inl.h
@@ -51,7 +51,7 @@ struct log_softmax_fwd {
};
-template<typename OP, typename DType, int ndim>
+template<typename OP, bool negate, typename DType, int ndim>
inline void Softmax(Stream<cpu> *s, DType *in, DType *out,
Shape<ndim> shape, int axis, const DType temperature) {
index_t M = shape[axis];
@@ -65,30 +65,37 @@ inline void Softmax(Stream<cpu> *s, DType *in, DType *out,
for (int i = 0; i < static_cast<int>(N); ++i) {
index_t base = unravel_dot(i, sshape, stride);
- DType mmax = in[base];
+ DType mmax = negate ? -in[base] : in[base];
+ DType val;
for (index_t j = 1; j < M; ++j) {
- if (mmax < in[base + j*sa]) mmax = in[base + j*sa];
+ val = negate ? -in[base + j*sa] : in[base + j*sa];
+ if (mmax < val) mmax = val;
}
DType sum = DType(0);
+ DType in_val;
// By default temperature is 1.0, and only in reinforcement training
// users would set it to other values.
// Adding a branch here to save the CPU 'divide-by-1' computation at
runtime
if (temperature == 1.0) {
for (index_t j = 0; j < M; ++j) {
- sum += std::exp(in[base + j*sa] - mmax);
+ in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+ sum += std::exp(in_val - mmax);
}
for (index_t j = 0; j < M; ++j) {
- out[base + j*sa] = OP::Map(in[base + j*sa] - mmax, sum);
+ in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+ out[base + j*sa] = OP::Map(in_val - mmax, sum);
}
} else {
for (index_t j = 0; j < M; ++j) {
- sum += std::exp((in[base + j*sa] - mmax)/temperature);
+ in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+ sum += std::exp((in_val - mmax)/temperature);
}
for (index_t j = 0; j < M; ++j) {
- out[base + j*sa] = OP::Map((in[base + j*sa] - mmax)/temperature, sum);
+ in_val = negate ? -in[base + j*sa] : in[base + j*sa];
+ out[base + j*sa] = OP::Map((in_val - mmax)/temperature, sum);
}
}
}
@@ -111,7 +118,7 @@ struct log_softmax_bwd {
};
-template<typename OP1, typename OP2, int Req, typename DType, int ndim>
+template<typename OP1, typename OP2, int Req, bool negate, typename DType, int
ndim>
inline void SoftmaxGrad(Stream<cpu> *s, DType *out, DType *ograd,
DType *igrad, Shape<ndim> shape, int axis,
const DType temperature) {
@@ -137,12 +144,16 @@ inline void SoftmaxGrad(Stream<cpu> *s, DType *out, DType
*ograd,
DType final_result;
if (temperature == 1.0) {
for (index_t j = 0; j < M; ++j) {
- final_result = OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
+ final_result = negate ?
+ -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) :
+ OP2::Map(ograd[base + j*sa], out[base + j*sa], sum);
KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
}
} else {
for (index_t j = 0; j < M; ++j) {
- final_result = OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) /
temperature;
+ final_result = negate ?
+ -OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) /
temperature :
+ OP2::Map(ograd[base + j*sa], out[base + j*sa], sum) /
temperature;
KERNEL_ASSIGN(igrad[base + j*sa], Req, final_result);
}
}
@@ -151,7 +162,7 @@ inline void SoftmaxGrad(Stream<cpu> *s, DType *out, DType
*ograd,
#ifdef __CUDACC__
-template<int x_bits, typename OP, typename DType, int ndim>
+template<int x_bits, typename OP, bool negate, typename DType, int ndim>
__global__ void softmax_compute_kernel(DType *in, DType *out, index_t M, int
axis,
Shape<ndim> sshape, Shape<ndim> stride,
const double temperature) {
@@ -163,7 +174,7 @@ __global__ void softmax_compute_kernel(DType *in, DType
*out, index_t M, int axi
red::maximum::SetInitValue(smem[x]);
for (index_t i = x; i < M; i += x_size) {
- red::maximum::Reduce(smem[x], in[base + i*sa]);
+ red::maximum::Reduce(smem[x], negate ? -in[base + i*sa] : in[base + i*sa]);
}
__syncthreads();
cuda::Reduce1D<red::maximum, x_bits>(smem);
@@ -172,9 +183,11 @@ __global__ void softmax_compute_kernel(DType *in, DType
*out, index_t M, int axi
__syncthreads();
red::sum::SetInitValue(smem[x]);
+ DType val;
for (index_t i = x; i < M; i += x_size) {
- red::sum::Reduce(smem[x], static_cast<DType>(expf((in[base + i*sa] - smax)/
- static_cast<DType>(temperature))));
+ val = negate ? -in[base + i*sa]:in[base + i*sa];
+ red::sum::Reduce(
+ smem[x], static_cast<DType>(expf((val - smax) /
static_cast<DType>(temperature))));
}
__syncthreads();
cuda::Reduce1D<red::sum, x_bits>(smem);
@@ -183,11 +196,12 @@ __global__ void softmax_compute_kernel(DType *in, DType
*out, index_t M, int axi
__syncthreads();
for (index_t i = x; i < M; i += x_size) {
- out[base + i*sa] = OP::Map((in[base + i*sa] -
smax)/static_cast<DType>(temperature), ssum);
+ val = negate ? -in[base + i*sa] : in[base + i*sa];
+ out[base + i*sa] = OP::Map((val - smax)/static_cast<DType>(temperature),
ssum);
}
}
-template<typename OP, typename DType, int ndim>
+template<typename OP, bool negate, typename DType, int ndim>
inline void Softmax(Stream<gpu> *s, DType *in, DType *out,
Shape<ndim> shape, int axis, const double temperature) {
const int x_bits = 7;
@@ -198,14 +212,14 @@ inline void Softmax(Stream<gpu> *s, DType *in, DType *out,
Shape<ndim> sshape = shape;
sshape[axis] = 1;
- softmax_compute_kernel<x_bits, OP, DType, ndim>
+ softmax_compute_kernel<x_bits, OP, negate, DType, ndim>
<<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
in, out, M, axis, sshape, stride, temperature);
MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_compute_kernel);
}
-template<int x_bits, typename OP1, typename OP2, int Req, typename DType, int
ndim>
+template<int x_bits, typename OP1, typename OP2, int Req, bool negate,
typename DType, int ndim>
__global__ void softmax_gradient_kernel(DType *out, DType *ograd, DType *igrad,
index_t M, int axis, Shape<ndim>
sshape,
Shape<ndim> stride, const double
temperature) {
@@ -228,13 +242,15 @@ __global__ void softmax_gradient_kernel(DType *out, DType
*ograd, DType *igrad,
DType final_result;
for (index_t i = x; i < M; i += x_size) {
final_result =
- OP2::Map(ograd[base + i*sa], out[base + i*sa], ssum) /
static_cast<DType>(temperature);
- KERNEL_ASSIGN(igrad[base + i*sa], Req, final_result);
+ negate ?
+ -OP2::Map(ograd[base + i*sa], out[base + i*sa], ssum) :
+ OP2::Map(ograd[base + i*sa], out[base + i*sa], ssum);
+ KERNEL_ASSIGN(igrad[base + i*sa], Req, final_result /
static_cast<DType>(temperature));
}
}
-template<typename OP1, typename OP2, int Req, typename DType, int ndim>
+template<typename OP1, typename OP2, int Req, bool negate, typename DType, int
ndim>
inline void SoftmaxGrad(Stream<gpu> *s, DType *out, DType *ograd,
DType *igrad, Shape<ndim> shape, int axis,
const double temperature) {
@@ -246,7 +262,7 @@ inline void SoftmaxGrad(Stream<gpu> *s, DType *out, DType
*ograd,
Shape<ndim> sshape = shape;
sshape[axis] = 1;
- softmax_gradient_kernel<x_bits, OP1, OP2, Req, DType, ndim>
+ softmax_gradient_kernel<x_bits, OP1, OP2, Req, negate, DType, ndim>
<<<N, x_size, 0, mshadow::Stream<gpu>::GetStream(s)>>>(
out, ograd, igrad, M, axis, sshape, stride, temperature);
MSHADOW_CUDA_POST_KERNEL_CHECK(softmax_gradient_kernel);
@@ -267,7 +283,7 @@ struct SoftmaxParam : public dmlc::Parameter<SoftmaxParam> {
}
};
-template<typename xpu, typename OP>
+template<typename xpu, typename OP, bool negate = false>
void SoftmaxCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
@@ -283,19 +299,19 @@ void SoftmaxCompute(const nnvm::NodeAttrs& attrs,
TShape shape = AxisShapeCompact(inputs[0].shape_, &axis, true);
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
if (shape.ndim() == 2) {
- Softmax<OP>(ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
- outputs[0].dptr<DType>(), shape.get<2>(), axis,
- static_cast<DType>(temperature));
+ Softmax<OP, negate>(ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+ outputs[0].dptr<DType>(), shape.get<2>(), axis,
+ static_cast<DType>(temperature));
} else {
- Softmax<OP>(ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
- outputs[0].dptr<DType>(), shape.get<3>(), axis,
- static_cast<DType>(temperature));
+ Softmax<OP, negate>(ctx.get_stream<xpu>(), inputs[0].dptr<DType>(),
+ outputs[0].dptr<DType>(), shape.get<3>(), axis,
+ static_cast<DType>(temperature));
}
});
}
-template<typename xpu, typename OP1, typename OP2>
+template<typename xpu, typename OP1, typename OP2, bool negate = false>
void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
@@ -311,13 +327,13 @@ void SoftmaxGradCompute(const nnvm::NodeAttrs& attrs,
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_, DType, {
MXNET_ASSIGN_REQ_SWITCH(req[0], Req, {
if (shape.ndim() == 2) {
- SoftmaxGrad<OP1, OP2, Req>(ctx.get_stream<xpu>(),
inputs[1].dptr<DType>(),
- inputs[0].dptr<DType>(),
outputs[0].dptr<DType>(),
- shape.get<2>(), axis,
static_cast<DType>(temperature));
+ SoftmaxGrad<OP1, OP2, Req, negate>(ctx.get_stream<xpu>(),
inputs[1].dptr<DType>(),
+ inputs[0].dptr<DType>(),
outputs[0].dptr<DType>(),
+ shape.get<2>(), axis,
static_cast<DType>(temperature));
} else {
- SoftmaxGrad<OP1, OP2, Req>(ctx.get_stream<xpu>(),
inputs[1].dptr<DType>(),
- inputs[0].dptr<DType>(),
outputs[0].dptr<DType>(),
- shape.get<3>(), axis,
static_cast<DType>(temperature));
+ SoftmaxGrad<OP1, OP2, Req, negate>(ctx.get_stream<xpu>(),
inputs[1].dptr<DType>(),
+ inputs[0].dptr<DType>(),
outputs[0].dptr<DType>(),
+ shape.get<3>(), axis,
static_cast<DType>(temperature));
}
});
});
diff --git a/src/operator/nn/softmax.cc b/src/operator/nn/softmax.cc
index 0fad3d6..88b7b5f 100644
--- a/src/operator/nn/softmax.cc
+++ b/src/operator/nn/softmax.cc
@@ -109,6 +109,45 @@ MXNET_OPERATOR_REGISTER_BINARY(_backward_softmax)
.set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu,
op::mshadow_op::mul,
mxnet_op::softmax_bwd>);
+MXNET_OPERATOR_REGISTER_UNARY(softmin)
+.describe(R"code(Applies the softmin function.
+
+The resulting array contains elements in the range (0,1) and the elements
along the given axis sum
+up to 1.
+
+.. math::
+ softmin(\mathbf{z/t})_j = \frac{e^{-z_j/t}}{\sum_{k=1}^K e^{-z_k/t}}
+
+for :math:`j = 1, ..., K`
+
+t is the temperature parameter in softmax function. By default, t equals 1.0
+
+Example::
+
+ x = [[ 1. 2. 3.]
+ [ 3. 2. 1.]]
+
+ softmin(x,axis=0) = [[ 0.88079703, 0.5, 0.11920292],
+ [ 0.11920292, 0.5, 0.88079703]]
+
+ softmin(x,axis=1) = [[ 0.66524094, 0.24472848, 0.09003057],
+ [ 0.09003057, 0.24472848, 0.66524094]]
+
+)code" ADD_FILELINE)
+.set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<nnvm::FListOutputNames>("FListOutputNames",
+ [](const NodeAttrs& attrs) {
+ return std::vector<std::string>{"output"};
+})
+.set_attr<FCompute>("FCompute<cpu>", SoftmaxCompute<cpu,
mxnet_op::softmax_fwd, true>)
+.set_attr<nnvm::FGradient>("FGradient",
ElemwiseGradUseOut{"_backward_softmin"})
+.add_arguments(SoftmaxParam::__FIELDS__());
+
+MXNET_OPERATOR_REGISTER_BINARY(_backward_softmin)
+.set_attr_parser(ParamParser<SoftmaxParam>)
+.set_attr<FCompute>("FCompute<cpu>", SoftmaxGradCompute<cpu,
op::mshadow_op::mul,
+ mxnet_op::softmax_bwd,
true>);
+
MXNET_OPERATOR_REGISTER_UNARY(log_softmax)
.describe(R"code(Computes the log softmax of the input.
This is equivalent to computing softmax followed by log.
diff --git a/src/operator/nn/softmax.cu b/src/operator/nn/softmax.cu
index 8274642..254e726 100644
--- a/src/operator/nn/softmax.cu
+++ b/src/operator/nn/softmax.cu
@@ -35,6 +35,13 @@ NNVM_REGISTER_OP(_backward_softmax)
.set_attr<FCompute>("FCompute<gpu>", SoftmaxGradCompute<gpu,
op::mshadow_op::mul,
mxnet_op::softmax_bwd>);
+NNVM_REGISTER_OP(softmin)
+.set_attr<FCompute>("FCompute<gpu>", SoftmaxCompute<gpu,
mxnet_op::softmax_fwd, true>);
+
+NNVM_REGISTER_OP(_backward_softmin)
+.set_attr<FCompute>("FCompute<gpu>", SoftmaxGradCompute<gpu,
op::mshadow_op::mul,
+ mxnet_op::softmax_bwd,
true>);
+
NNVM_REGISTER_OP(log_softmax)
.set_attr<FCompute>("FCompute<gpu>", SoftmaxCompute<gpu,
mxnet_op::log_softmax_fwd>);
diff --git a/tests/python/unittest/test_operator.py
b/tests/python/unittest/test_operator.py
index 38c90e6..5bd88dd 100644
--- a/tests/python/unittest/test_operator.py
+++ b/tests/python/unittest/test_operator.py
@@ -267,11 +267,8 @@ def test_rnnrelu_dropout():
out[0].wait_to_read()
def np_softmax(x, axis=-1, temperature=1.0):
- # fix for old numpy on Travis not supporting keepdims
- # x = x - np.max(x, axis=-1, keepdims=True)
x = x - np.max(x, axis=axis, keepdims=True)
x = np.exp(x/temperature)
- # x /= np.sum(x, axis=-1, keepdims=True)
x /= np.sum(x, axis=axis, keepdims=True)
return x
@@ -4535,6 +4532,27 @@ def test_where():
test_invalid_shape()
test_1d_cond()
+
+@with_seed()
+def test_softmin():
+ for ndim in range(1, 5):
+ for dtype in [np.float16, np.float32, np.float64]:
+ rtol, atol = (1e-2, 5e-3) if dtype is np.float16 else (1e-3, 1e-3)
+ shape = np.random.randint(1, 5, size=ndim)
+ axis = np.random.randint(-ndim, ndim)
+ data = np.random.uniform(-2, 2, size=shape).astype(dtype)
+ data = data / 10 if dtype is np.float16 else data
+ sym = mx.sym.softmin(axis=axis)
+ expected_fwd = np_softmax(-data, axis=axis)
+ expected_bwd = np.zeros(shape)
+ check_symbolic_forward(sym, [data], [expected_fwd], atol=atol,
dtype=dtype)
+ for req in ['null', 'add', 'write']:
+ check_symbolic_backward(sym, [data],
[np.ones(expected_fwd.shape)], [expected_bwd],
+ rtol=rtol, atol=atol, grad_req=req,
dtype=dtype)
+ if dtype is not np.float16:
+ check_numeric_gradient(sym, [data], rtol=rtol, atol=atol,
dtype=dtype)
+
+
@with_seed()
def test_new_softmax():
for ndim in range(1, 5):
