[ET-VK] Enable buffer implementation of aten.linear (#6608)

* [ET-VK] Allow clone op to transfer between memory layouts and storage types

Pull Request resolved: #6596

## Changes

As title. Extend the functionality of the `aten.clone` operator to allow transitioning the storage type and memory layout between the input to the output tensor.

## Context

This functionality will be used to transition input tensors to the optimal storage type and memory layout before entering the execution of an op. The transition nodes will be added by a memory metadata tagging pass that will be introduced in a subsequent diff.
ghstack-source-id: 251229412
@exported-using-ghexport

Differential Revision: [D65277710](https://our.internmc.facebook.com/intern/diff/D65277710/)

* [ET-VK] Enable buffer implementation of `aten.linear`

Pull Request resolved: #6597

## Changes

As title. Extend the existing buffer implementation of `matmul` to support the linear operator as well.
ghstack-source-id: 251229414
@exported-using-ghexport

Differential Revision: [D65277712](https://our.internmc.facebook.com/intern/diff/D65277712/)

---------

Co-authored-by: Stephen Jia <[email protected]>

Author: pytorchbot

backends/vulkan/runtime/graph/ops/glsl/matmul_naive_buffer.glsl

@@ -16,21 +16,23 @@ ${define_required_extensions(DTYPE)}
 
 layout(std430) buffer;
 
-${layout_declare_tensor(0, "w", "t_out", DTYPE, "buffer")}
-${layout_declare_tensor(1, "r", "t_mat1", DTYPE, "buffer")}
-${layout_declare_tensor(2, "r", "t_mat2", DTYPE, "buffer")}
-${layout_declare_ubo(3, "ivec4", "out_sizes")}
-${layout_declare_ubo(4, "ivec4", "out_strides")}
-${layout_declare_ubo(5, "ivec4", "mat1_sizes")}
-${layout_declare_ubo(6, "ivec4", "mat1_strides")}
-${layout_declare_ubo(7, "ivec4", "mat2_sizes")}
-${layout_declare_ubo(8, "ivec4", "mat2_strides")}
-${layout_declare_ubo(9, "int", "out_numel")}
+${layout_declare_tensor(B, "w", "t_out", DTYPE, "buffer")}
+${layout_declare_tensor(B, "r", "t_mat1", DTYPE, "buffer")}
+${layout_declare_tensor(B, "r", "t_mat2", DTYPE, "buffer")}
+${layout_declare_ubo(B, "ivec4", "out_sizes")}
+${layout_declare_ubo(B, "ivec4", "out_strides")}
+${layout_declare_ubo(B, "ivec4", "mat1_sizes")}
+${layout_declare_ubo(B, "ivec4", "mat1_strides")}
+${layout_declare_ubo(B, "ivec4", "mat2_sizes")}
+${layout_declare_ubo(B, "ivec4", "mat2_strides")}
+${layout_declare_ubo(B, "int", "out_numel")}
 
 #include "indexing_utils.h"
 
 layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
 
+${layout_declare_spec_const(C, "int", "mat2_is_transposed", "0")}
+
 void main() {
   const ivec4 out_bufix = ivec4(
       gl_GlobalInvocationID.x,
@@ -44,15 +46,28 @@ void main() {
 
   int mat1_bufi = tidx_to_bufi(
       ivec4(0, out_bufix.y, out_bufix.z, out_bufix.w), mat1_strides);
-  int mat2_bufi = tidx_to_bufi(
-      ivec4(out_bufix.x, 0, out_bufix.z, out_bufix.w), mat2_strides);
+  int mat2_bufi;
+  if (mat2_is_transposed > 0) {
+    mat2_bufi = tidx_to_bufi(
+        ivec4(0, out_bufix.x, 0, 0), mat2_strides);
+  } else {
+    mat2_bufi = tidx_to_bufi(
+        ivec4(out_bufix.x, 0, out_bufix.z, out_bufix.w), mat2_strides);
+  }
+
+  int mat2_stride;
+  if (mat2_is_transposed > 0) {
+    mat2_stride = mat2_strides.x;
+  } else {
+    mat2_stride = mat2_strides.y;
+  }
 
   T sum = T(0.0);
   for (int i = 0; i < mat1_sizes.x; ++i) {
     sum += t_mat1[mat1_bufi] * t_mat2[mat2_bufi];
 
     mat1_bufi += mat1_strides.x;
-    mat2_bufi += mat2_strides.y;
+    mat2_bufi += mat2_stride;
   }
 
   const int out_bufi = tidx_to_bufi(out_bufix, out_strides);

backends/vulkan/runtime/graph/ops/impl/Linear.cpp

@@ -279,9 +279,12 @@ void linear(ComputeGraph& graph, const std::vector<ValueRef>& args) {
   ValueRef weight = prepack_standard(
       graph, weight_data, graph.storage_type_of(out), utils::kWidthPacked);
   ValueRef mat2_is_transposed = graph.add_scalar(true);
+
   if (graph.val_is_none(bias)) {
     return add_matmul_node(graph, input, weight, out, mat2_is_transposed);
   } else {
+    // Buffer implementation does not yet support biases
+    VK_CHECK_COND(!graph.is_buffer_storage(out));
     return add_addmm_node(
         graph,
         bias,

backends/vulkan/runtime/graph/ops/impl/MatMul.cpp

@@ -77,6 +77,11 @@ void add_matmul_naive_buffer_node(
       graph.size_at<uint32_t>(-2, out),
       graph.size_at<uint32_t>(-3, out) * graph.size_at<uint32_t>(-4, out)};
 
+  int mat2_is_transposed_val = (mat2_is_transposed != kDummyValueRef &&
+                                graph.get_bool(mat2_is_transposed))
+      ? 1
+      : 0;
+
   graph.execute_nodes().emplace_back(new DispatchNode(
       graph,
       VK_KERNEL_FROM_STR(kernel_name),
@@ -96,7 +101,7 @@ void add_matmul_naive_buffer_node(
           graph.numel_ubo(out),
       },
       // Specialization Constants
-      {},
+      {mat2_is_transposed_val},
       // Resizing Logic
       resize_matmul_node,
       {mat2_is_transposed}));

backends/vulkan/test/op_tests/cases.py

@@ -126,8 +126,7 @@ def get_addmm_inputs():
 ]
 
 
-@register_test_suite("aten.linear.default")
-def get_linear_inputs():
+def get_linear_texture_inputs():
     MKN_list = common_MKN_list
 
     inputs_list = [((M, K), (N, K), None) for M, K, N in MKN_list]
@@ -142,9 +141,32 @@ def get_linear_inputs():
         "utils::kWidthPacked",
         "utils::kChannelsPacked",
     ]
+    test_suite.test_name_suffix = "texture"
+    return test_suite
+
+
+def get_linear_buffer_inputs():
+    MKN_list = common_MKN_list
+
+    inputs_list = [((M, K), (N, K), None) for M, K, N in MKN_list]
+    inputs_list += [((3, M, K), (N, K), None) for M, K, N in MKN_list]
+
+    test_suite = VkTestSuite(inputs_list)
+    test_suite.dtypes = ["at::kFloat"]
+    test_suite.layouts = [
+        "utils::kWidthPacked",
+        "utils::kChannelsPacked",
+    ]
+    test_suite.storage_types = ["utils::kBuffer"]
+    test_suite.test_name_suffix = "buffer"
     return test_suite
 
 
+@register_test_suite("aten.linear.default")
+def get_linear_test_suites():
+    return [get_linear_texture_inputs(), get_linear_buffer_inputs()]
+
+
 @register_test_suite("aten._weight_int8pack_mm.default")
 def get_weight_int8pack_mm_inputs():
     MKN_list = common_MKN_list