Merge branch 'opt_log_softmax_new_device_code' into argmax_min

src/include/migraphx/shape.hpp

@@ -99,6 +99,8 @@ struct shape
     /// Map element index to space index
     std::size_t index(std::size_t i) const;
 
+    std::vector<std::size_t> multi(std::size_t i) const;
+
     /// Returns true if the shape is packed with no padding
     bool packed() const;
     /// Returns true is the shape has been transposed. That is the strides are not in descending

src/shape.cpp

@@ -138,6 +138,24 @@ std::size_t shape::index(std::size_t i) const
         return result;
     }
 }
+
+std::vector<std::size_t> shape::multi(std::size_t i) const
+{
+    assert(this->standard());
+
+    std::vector<std::size_t> indices(lens().size());
+    std::transform(strides().begin(),
+                   strides().end(),
+                   lens().begin(),
+                   indices.begin(),
+                   [&](std::size_t stride, std::size_t len) {
+                       assert(len > 0 and stride > 0);
+                       return (i / stride) % len;
+                   });
+
+    return indices;
+}
+
 bool shape::packed() const { return this->elements() == this->element_space(); }
 
 bool shape::transposed() const

src/targets/cpu/lowering.cpp

@@ -529,22 +529,6 @@ struct cpu_softmax
 
     std::string name() const { return "cpu::softmax"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
-
-    std::vector<size_t> compute_batch_indices(size_t idx, const shape& s) const
-    {
-        std::vector<std::size_t> indices(s.lens().size());
-        std::transform(s.strides().begin(),
-                       s.strides().end(),
-                       s.lens().begin(),
-                       indices.begin(),
-                       [&](std::size_t stride, std::size_t len) {
-                           assert(len > 0 and stride > 0);
-                           return (idx / stride) % len;
-                       });
-
-        return indices;
-    }
-
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
     {
         argument result{output_shape};
@@ -559,8 +543,7 @@ struct cpu_softmax
                                               std::numeric_limits<value_type>::lowest());
             std::vector<value_type> batch_sum(batch_shape.elements(), value_type(0));
             par_for(batch_shape.elements(), [&](auto i) {
-                auto idx = this->compute_batch_indices(i, batch_shape);
-
+                auto idx = batch_shape.multi(i);
                 for(size_t j = 0; j < n_dims; ++j)
                 {
                     idx[op.axis] = j;
@@ -604,22 +587,6 @@ struct cpu_logsoftmax
 
     std::string name() const { return "cpu::logsoftmax"; }
     shape compute_shape(const std::vector<shape>& inputs) const { return op.compute_shape(inputs); }
-
-    std::vector<size_t> compute_batch_indices(size_t idx, const shape& s) const
-    {
-        std::vector<std::size_t> indices(s.lens().size());
-        std::transform(s.strides().begin(),
-                       s.strides().end(),
-                       s.lens().begin(),
-                       indices.begin(),
-                       [&](std::size_t stride, std::size_t len) {
-                           assert(len > 0 and stride > 0);
-                           return (idx / stride) % len;
-                       });
-
-        return indices;
-    }
-
     argument compute(context&, const shape& output_shape, std::vector<argument> args) const
     {
         argument result{output_shape};
@@ -637,7 +604,7 @@ struct cpu_logsoftmax
             std::vector<value_type> batch_sum(batch_shape.elements(), value_type(0));
 
             par_for(batch_shape.elements(), [&](auto i) {
-                auto idx = this->compute_batch_indices(i, batch_shape);
+                auto idx = batch_shape.multi(i);
                 for(size_t j = 0; j < n_dims; ++j)
                 {
                     idx[op.axis] = j;