Refactor logsoftmax

src/targets/gpu/device/logsoftmax.cpp

@@ -12,65 +12,55 @@ namespace gpu {
 namespace device {
 
 argument logsoftmax(hipStream_t stream,
-                    const migraphx::shape& output_shape,
-                    std::vector<migraphx::argument> args,
+                    argument result,
+                    argument arg,
                     int axis)
 {
 
-    auto lens         = output_shape.lens();
+    auto lens         = result.get_shape().lens();
     auto num_in_batch = lens[axis];
     auto batch_lens   = lens;
     batch_lens[axis]  = 1;
-    migraphx::shape batch_shape{output_shape.type(), batch_lens};
+    shape batch_shape{result.get_shape().type(), batch_lens};
 
-    visit_all(args.back(), args.front())([&](auto output, auto input) {
-        const auto* input_ptr = device_cast(input.data());
-        auto* output_ptr      = device_cast(output.data());
-        visit_tensor_size(batch_shape.lens().size(), [&](auto n_dim) {
-            hip_tensor_descriptor<n_dim> desc_batch(batch_shape);
-            hip_tensor_descriptor<n_dim> desc_data(output_shape);
+    hip_visit_all(result, arg, batch_shape)([&](auto output, auto input, auto batch) {
 
-            // each thread is for one item in the batch
-            gs_launch(stream, batch_shape.elements())([=](auto i) {
-                auto batch_idx = desc_batch.multi(i);
-                auto data_idx  = batch_idx;
+        // each thread is for one item in the batch
+        gs_launch(stream, batch_shape.elements())([=](auto i) {
+            auto batch_idx = batch.multi(i);
+            auto data_idx  = batch_idx;
 
-                // get max
-                auto batch_max = input_ptr[desc_data.linear(batch_idx)];
-                for(std::size_t j = 1; j < num_in_batch; ++j)
-                {
-                    data_idx[axis] = j;
-                    size_t idx     = desc_data.linear(data_idx);
-                    batch_max      = std::max(to_hip_type(batch_max), to_hip_type(input_ptr[idx]));
-                }
+            // get max
+            auto batch_max = input[batch_idx];
+            for(std::size_t j = 1; j < num_in_batch; ++j)
+            {
+                data_idx[axis] = j;
+                batch_max      = std::max(to_hip_type(batch_max), to_hip_type(input[data_idx]));
+            }
 
-                for(std::size_t j = 0; j < num_in_batch; ++j)
-                {
-                    data_idx[axis]  = j;
-                    size_t idx      = desc_data.linear(data_idx);
-                    output_ptr[idx] = input_ptr[idx] - batch_max;
-                }
+            for(std::size_t j = 0; j < num_in_batch; ++j)
+            {
+                data_idx[axis]  = j;
+                output[data_idx] = input[data_idx] - batch_max;
+            }
 
-                auto batch_sum = ::exp(to_hip_type(output_ptr[desc_data.linear(batch_idx)]));
-                for(std::size_t j = 1; j < num_in_batch; ++j)
-                {
-                    data_idx[axis] = j;
-                    size_t idx     = desc_data.linear(data_idx);
-                    batch_sum += ::exp(to_hip_type(output_ptr[idx]));
-                }
-                batch_sum = ::log(to_hip_type(batch_sum));
+            auto batch_sum = ::exp(to_hip_type(output[batch_idx]));
+            for(std::size_t j = 1; j < num_in_batch; ++j)
+            {
+                data_idx[axis] = j;
+                batch_sum += ::exp(to_hip_type(output[data_idx]));
+            }
+            batch_sum = ::log(to_hip_type(batch_sum));
 
-                for(std::size_t j = 0; j < num_in_batch; ++j)
-                {
-                    data_idx[axis] = j;
-                    size_t idx     = desc_data.linear(data_idx);
-                    output_ptr[idx] -= batch_sum;
-                }
-            });
+            for(std::size_t j = 0; j < num_in_batch; ++j)
+            {
+                data_idx[axis] = j;
+                output[data_idx] -= batch_sum;
+            }
         });
     });
 
-    return args.back();
+    return result;
 }
 
 } // namespace device

src/targets/gpu/include/migraphx/gpu/device/logsoftmax.hpp

@@ -11,8 +11,8 @@ namespace gpu {
 namespace device {
 
 argument logsoftmax(hipStream_t stream,
-                    const migraphx::shape& output_shape,
-                    std::vector<migraphx::argument> args,
+                    argument result,
+                    argument arg,
                     int axis);
 
 } // namespace device

src/targets/gpu/logsoftmax.cpp

@@ -16,10 +16,10 @@ shape hip_logsoftmax::compute_shape(const std::vector<shape>& inputs) const
 }
 
 argument hip_logsoftmax::compute(context& ctx,
-                                 const shape& output_shape,
+                                 const shape&,
                                  const std::vector<argument>& args) const
 {
-    return device::logsoftmax(ctx.get_stream().get(), output_shape, args, op.axis);
+    return device::logsoftmax(ctx.get_stream().get(), args[1], args[0], op.axis);
 }
 
 } // namespace gpu