[ET-VK] support biases in buffer-based linear shader

Differential Revision: D69247282

Pull Request resolved: #8284

Author: SS-JIA

backends/vulkan/runtime/graph/ops/glsl/matmul_naive_buffer.glsl renamed to backends/vulkan/runtime/graph/ops/glsl/addmm_naive_buffer.glsl

@@ -10,6 +10,9 @@
 
 #define PRECISION ${PRECISION}
 
+$if HAS_BIAS:
+  #define HAS_BIAS
+
 #define T ${buffer_scalar_type(DTYPE)}
 
 ${define_required_extensions(DTYPE)}
@@ -19,13 +22,17 @@ layout(std430) buffer;
 ${layout_declare_tensor(B, "w", "t_out", DTYPE, "buffer")}
 ${layout_declare_tensor(B, "r", "t_mat1", DTYPE, "buffer")}
 ${layout_declare_tensor(B, "r", "t_mat2", DTYPE, "buffer")}
+$if HAS_BIAS:
+  ${layout_declare_tensor(B, "r", "t_bias", 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")}
 ${layout_declare_ubo(B, "int", "out_numel")}
+$if HAS_BIAS:
+  ${layout_declare_ubo(B, "float", "alpha", "float", "beta")}
 
 #include "indexing_utils.h"
 
@@ -34,25 +41,25 @@ 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(
+  const ivec4 out_tidx = ivec4(
       gl_GlobalInvocationID.x,
       gl_GlobalInvocationID.y,
       gl_GlobalInvocationID.z % out_sizes.z,
       gl_GlobalInvocationID.z / out_sizes.z);
 
-  if (any(greaterThanEqual(out_bufix, out_sizes))) {
+  if (any(greaterThanEqual(out_tidx, out_sizes))) {
     return;
   }
 
   int mat1_bufi = tidx_to_bufi(
-      ivec4(0, out_bufix.y, out_bufix.z, out_bufix.w), mat1_strides);
+      ivec4(0, out_tidx.y, out_tidx.z, out_tidx.w), mat1_strides);
   int mat2_bufi;
   if (mat2_is_transposed > 0) {
     mat2_bufi = tidx_to_bufi(
-        ivec4(0, out_bufix.x, 0, 0), mat2_strides);
+        ivec4(0, out_tidx.x, 0, 0), mat2_strides);
   } else {
     mat2_bufi = tidx_to_bufi(
-        ivec4(out_bufix.x, 0, out_bufix.z, out_bufix.w), mat2_strides);
+        ivec4(out_tidx.x, 0, out_tidx.z, out_tidx.w), mat2_strides);
   }
 
   int mat2_stride;
@@ -70,6 +77,10 @@ void main() {
     mat2_bufi += mat2_stride;
   }
 
-  const int out_bufi = tidx_to_bufi(out_bufix, out_strides);
+  const int out_bufi = tidx_to_bufi(out_tidx, out_strides);
+#ifdef HAS_BIAS
+  t_out[out_bufi] = T(alpha) * T(sum) + T(beta) * t_bias[out_tidx.x];
+#else
   t_out[out_bufi] = T(sum);
+#endif // HAS_BIAS
 }

backends/vulkan/runtime/graph/ops/glsl/matmul_naive_buffer.yaml renamed to backends/vulkan/runtime/graph/ops/glsl/addmm_naive_buffer.yaml

@@ -4,13 +4,16 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-matmul_naive_buffer:
+addmm_naive_buffer:
   parameter_names_with_default_values:
     DTYPE: float
     STORAGE: buffer
+    HAS_BIAS: false
   generate_variant_forall:
     DTYPE:
       - VALUE: float
       - VALUE: half
   shader_variants:
     - NAME: matmul_naive_buffer
+    - NAME: addmm_naive_buffer
+      HAS_BIAS: true

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

@@ -84,7 +84,7 @@ struct Params final {
   float beta;
 };
 
-void add_addmm_naive_node(
+void add_addmm_naive_texture_node(
     ComputeGraph& graph,
     const ValueRef self_data,
     const ValueRef mat1,
@@ -134,6 +134,69 @@ void add_addmm_naive_node(
       {mat2_is_transposed}));
 }
 
+void add_addmm_naive_buffer_node(
+    ComputeGraph& graph,
+    const ValueRef self_data,
+    const ValueRef mat1,
+    const ValueRef mat2_data,
+    const ValueRef beta,
+    const ValueRef alpha,
+    const ValueRef out,
+    const Params& params,
+    const ValueRef mat2_is_transposed) {
+  (void)beta;
+  (void)alpha;
+  ValueRef mat2 = prepack_standard(
+      graph,
+      mat2_data,
+      graph.storage_type_of(out),
+      utils::kHeightPacked,
+      /*passthrough = */ true);
+  ValueRef self = prepack_standard(
+      graph,
+      self_data,
+      graph.storage_type_of(out),
+      utils::kWidthPacked,
+      /*passthrough = */ true);
+
+  std::string kernel_name = "addmm_naive_buffer";
+  add_dtype_suffix(kernel_name, graph.dtype_of(out));
+
+  utils::uvec3 global_size = {
+      graph.size_at<uint32_t>(-1, out),
+      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),
+      global_size,
+      graph.create_local_wg_size(global_size),
+      // Inputs and Outputs
+      {{out, vkapi::kWrite}, {{mat1, mat2, self}, vkapi::kRead}},
+      // Shader params buffers
+      {
+          graph.sizes_ubo(out),
+          graph.strides_ubo(out),
+          graph.sizes_ubo(mat1),
+          graph.strides_ubo(mat1),
+          graph.sizes_ubo(mat2),
+          graph.strides_ubo(mat2),
+          graph.numel_ubo(out),
+          graph.create_params_buffer(params),
+      },
+      // Specialization Constants
+      {mat2_is_transposed_val},
+      // Resizing Logic
+      resize_addmm_node,
+      {mat2_is_transposed}));
+}
+
 void add_addmm_optimized_node(
     ComputeGraph& graph,
     const ValueRef self_data,
@@ -246,11 +309,14 @@ void add_addmm_node(
   }
 
   Params params = {alpha_val, beta_val};
-  if (graph.packed_dim_of(mat1) == WHCN::kChannelsDim) {
+  if (graph.is_buffer_storage(out)) {
+    add_addmm_naive_buffer_node(
+        graph, self, mat1, mat2, beta, alpha, out, params, mat2_is_transposed);
+  } else if (graph.packed_dim_of(mat1) == WHCN::kChannelsDim) {
     add_addmm_optimized_node(
         graph, self, mat1, mat2, beta, alpha, out, params, mat2_is_transposed);
   } else if (graph.packed_dim_of(mat1) == WHCN::kWidthDim) {
-    add_addmm_naive_node(
+    add_addmm_naive_texture_node(
         graph, self, mat1, mat2, beta, alpha, out, params, mat2_is_transposed);
   } else {
     VK_THROW("Input should be channel packed or width packed.");
@@ -283,8 +349,6 @@ void linear(ComputeGraph& graph, const std::vector<ValueRef>& args) {
   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/test/op_tests/cases.py

@@ -126,7 +126,8 @@ def get_addmm_inputs():
 ]
 
 
-def get_linear_texture_inputs():
+@register_test_suite("aten.linear.default")
+def get_linear_inputs():
     MKN_list = common_MKN_list
 
     inputs_list = [((M, K), (N, K), None) for M, K, N in MKN_list]
@@ -141,32 +142,10 @@ def get_linear_texture_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"
+    test_suite.storage_types = ["utils::kBuffer", "utils::kTexture3D"]
     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 = [

backends/vulkan/test/test_vulkan_delegate.py

@@ -1711,3 +1711,26 @@ def forward(self, x):
             (torch.ones(size=[5, 4, 1, 2, 6]),),
             expect_no_delegates=True,
         )
+
+    def test_vulkan_backend_large_linear_layer(self):
+        class LinearModel(torch.nn.Module):
+            def __init__(
+                self, n_pca_basis: int, n_sh_basis: int, n_gaussians: int
+            ) -> None:
+                super(LinearModel, self).__init__()
+                self.fc1 = torch.nn.Linear(
+                    n_pca_basis, (n_sh_basis + 3 + 3 + 4) * n_gaussians
+                )
+
+            def forward(self, x: torch.Tensor):
+                out = self.fc1(x)
+                return out
+
+        n_pca_basis = 64
+        n_sh_basis = 6
+        n_gaussians = 2**16
+
+        self.lower_module_and_test_output(
+            LinearModel(n_pca_basis, n_sh_basis, n_gaussians),
+            (torch.ones(n_pca_basis),),
+        )