[GitHub] apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add temperature parameter in Softmax and SoftmaxOutput operator
apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add
temperature parameter in Softmax and SoftmaxOutput operator
URL: https://github.com/apache/incubator-mxnet/pull/11356#discussion_r198675428
##
File path: src/operator/nn/softmax-inl.h
##
@@ -127,7 +137,7 @@ inline void SoftmaxGrad(Stream *s, DType *out, DType
*ograd,
#ifdef __CUDACC__
template
__global__ void softmax_compute_kernel(DType *in, DType *out, index_t M, int
axis,
- Shape sshape, Shape stride)
{
+ Shape sshape, Shape stride,
float temperature) {
Review comment:
I have added const qualifier following your suggestion.
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[GitHub] apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add temperature parameter in Softmax and SoftmaxOutput operator
apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add
temperature parameter in Softmax and SoftmaxOutput operator
URL: https://github.com/apache/incubator-mxnet/pull/11356#discussion_r197304019
##
File path: src/operator/nn/softmax-inl.h
##
@@ -145,23 +155,36 @@ __global__ void softmax_compute_kernel(DType *in, DType
*out, index_t M, int axi
__syncthreads();
red::sum::SetInitValue(smem[x]);
- for (index_t i = x; i < M; i += x_size) {
-red::sum::Reduce(smem[x], static_cast(expf(in[base + i*sa] -
smax)));
+ if (temperature == 1.0) {
+for (index_t i = x; i < M; i += x_size) {
+ red::sum::Reduce(smem[x], static_cast(expf(in[base + i*sa] -
smax)));
+}
+ } else {
+for (index_t i = x; i < M; i += x_size) {
+ red::sum::Reduce(smem[x], static_cast(expf((in[base + i*sa] -
smax)/temperature)));
+}
}
+
__syncthreads();
cuda::Reduce1D(smem);
__syncthreads();
DType ssum = smem[0];
__syncthreads();
- for (index_t i = x; i < M; i += x_size) {
-out[base + i*sa] = OP::Map(in[base + i*sa] - smax, ssum);
+ if (temperature == 1.0) {
Review comment:
I agree with you about the overhead of the branching. However, there is a
trade off in performance over complexity here. It really depends on how
critical this piece of computation is to the overall performance of the
network. In this case, I would assume softmax is a function called very often
in the last layer of a neural network. But I am not an expert to oversee the
performance impact.
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[GitHub] apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add temperature parameter in Softmax and SoftmaxOutput operator
apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add
temperature parameter in Softmax and SoftmaxOutput operator
URL: https://github.com/apache/incubator-mxnet/pull/11356#discussion_r197303252
##
File path: src/operator/nn/softmax-inl.h
##
@@ -127,7 +137,7 @@ inline void SoftmaxGrad(Stream *s, DType *out, DType
*ograd,
#ifdef __CUDACC__
template
__global__ void softmax_compute_kernel(DType *in, DType *out, index_t M, int
axis,
- Shape sshape, Shape stride)
{
+ Shape sshape, Shape stride,
float temperature) {
Review comment:
Thanks for your suggestion. But I do not seem to find the convention of
using const for pass-by-value parameter in the Google style guide:
```
If a function guarantees that it will not modify an argument passed by
reference or by pointer, the corresponding function parameter should be a
reference-to-const (const T&) or pointer-to-const (const T*), respectively.
```
In fact, adding unnecessary const declaration will put restriction on the
caller of the library function.
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[GitHub] apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add temperature parameter in Softmax and SoftmaxOutput operator
apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add
temperature parameter in Softmax and SoftmaxOutput operator
URL: https://github.com/apache/incubator-mxnet/pull/11356#discussion_r197280278
##
File path: src/operator/nn/softmax-inl.h
##
@@ -127,7 +137,7 @@ inline void SoftmaxGrad(Stream *s, DType *out, DType
*ograd,
#ifdef __CUDACC__
template
__global__ void softmax_compute_kernel(DType *in, DType *out, index_t M, int
axis,
- Shape sshape, Shape stride)
{
+ Shape sshape, Shape stride,
float temperature) {
Review comment:
This is pass-by-value so it does not make any difference.
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[GitHub] apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add temperature parameter in Softmax and SoftmaxOutput operator
apeforest commented on a change in pull request #11356: [MXNET-560][WIP] Add
temperature parameter in Softmax and SoftmaxOutput operator
URL: https://github.com/apache/incubator-mxnet/pull/11356#discussion_r197279433
##
File path: src/operator/nn/softmax-inl.h
##
@@ -145,23 +155,36 @@ __global__ void softmax_compute_kernel(DType *in, DType
*out, index_t M, int axi
__syncthreads();
red::sum::SetInitValue(smem[x]);
- for (index_t i = x; i < M; i += x_size) {
-red::sum::Reduce(smem[x], static_cast(expf(in[base + i*sa] -
smax)));
+ if (temperature == 1.0) {
+for (index_t i = x; i < M; i += x_size) {
+ red::sum::Reduce(smem[x], static_cast(expf(in[base + i*sa] -
smax)));
+}
+ } else {
+for (index_t i = x; i < M; i += x_size) {
+ red::sum::Reduce(smem[x], static_cast(expf((in[base + i*sa] -
smax)/temperature)));
+}
}
+
__syncthreads();
cuda::Reduce1D(smem);
__syncthreads();
DType ssum = smem[0];
__syncthreads();
- for (index_t i = x; i < M; i += x_size) {
-out[base + i*sa] = OP::Map(in[base + i*sa] - smax, ssum);
+ if (temperature == 1.0) {
Review comment:
I added this if condition for performance concern. I'd assume (correct me if
I'm wrong) 90% of cases the temperature to softmax is set to 1. Adding a
dividep-by-1.0 operation to expf((in[base + i*sa] - smax)/t) will slow down
this computation and I am not aware any compiler can optimize away this.
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