Update on "[ET-VK][testing] Add scripts to facilitate operator testiing"

Differential Revision: [D80800081](https://our.internmc.facebook.com/intern/diff/D80800081)

[ghstack-poisoned]

Author: ssjia

backends/vulkan/op_registry.py

@@ -397,14 +397,17 @@ def check_reduce_node(node: torch.fx.Node) -> bool:
                 # If we can't get memory layout information, we'll assume the dims aren't packed
                 pass
 
-        keepdim = node.args[2]
-        if isinstance(keepdim, bool) and not keepdim:
+        def try_find_keepdim_arg(node: torch.fx.Node) -> bool:
+            for arg in node.args:
+                if isinstance(arg, bool):
+                    return arg
+
+            # Assume false by default
             return False
 
-        if len(node.args) > 2:
-            keepdim = node.args[2]
-            if isinstance(keepdim, bool) and not keepdim:
-                return False
+        keepdim = try_find_keepdim_arg(node)
+        if isinstance(keepdim, bool) and not keepdim:
+            return False
 
         return True
 

backends/vulkan/partitioner/vulkan_partitioner.py

@@ -204,7 +204,7 @@ def is_in_local_scalar_dense_chain(self, node: torch.fx.Node) -> Tuple[bool, boo
     def log_skip(self, node: torch.fx.Node, reason: str) -> None:
         if node.op == "call_function":
             logger.info(
-                f"[Vulkan Partitioner] Due to [{reason}], skipping {node.format_node()}"
+                f"[Vulkan Partitioner] Due to [{reason}], skipping {utils.node_io_str(node)}"
             )
 
     def is_node_supported(

backends/vulkan/runtime/graph/ops/glsl/conv2d.glsl

@@ -30,6 +30,8 @@ ${layout_declare_ubo(8, "float", "out_min", "float", "out_max")}
 
 layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
 
+${layout_declare_spec_const(C, "int", "ngroups", "1")}
+
 /*
  * Computes a 2D convolution. Each shader invocation calculates the output at
  * a single output location.
@@ -74,7 +76,18 @@ void main() {
   // Perform the convolution by iterating over the overlay region.
   VEC4_T sum = texelFetch(t_bias, ivec2(pos.z, 0), 0);
   const int ic4 = in_group_size / 4;
-  for (int z4 = 0; z4 < ic4; ++z4, kstart.x += kernel_size.x * 4) {
+
+  int z_start = 0;
+  int z_end = ic4;
+  if (ngroups > 1) {
+    const int group_size = (out_limits.z) / ngroups;
+    const int group_idx = pos.z / group_size;
+
+    z_start = ic4 * group_idx;
+    z_end = z_start + ic4;
+  }
+
+  for (int z4 = z_start; z4 < z_end; ++z4, kstart.x += kernel_size.x * 4) {
     for (int y = start.y, ky = kstart.y; y < end.y; y += dilation.y, ++ky) {
       for (int x = start.x, kx = kstart.x; x < end.x; x += dilation.x, kx += 4) {
         const VEC4_T in_texel = texelFetch(t_in, ivec3(x, y, z4), 0);

backends/vulkan/runtime/graph/ops/glsl/conv2d_dw.glsl

@@ -30,6 +30,8 @@ ${layout_declare_ubo(8, "float", "out_min", "float", "out_max")}
 
 layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
 
+${layout_declare_spec_const(C, "int", "ngroups", "1")}
+
 /*
  * Computes a depthwise convolution. Each shader invocation calculates the
  * output at a single output location.

backends/vulkan/runtime/graph/ops/glsl/conv2d_pw.glsl

@@ -38,6 +38,8 @@ layout(push_constant) uniform restrict Block {
 
 layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
 
+${layout_declare_spec_const(C, "int", "ngroups", "1")}
+
 #extension GL_EXT_control_flow_attributes : require
 
 /*

backends/vulkan/runtime/graph/ops/glsl/conv2d_pw_s1p0.glsl

@@ -40,6 +40,8 @@ layout(push_constant) uniform restrict Block {
 
 layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
 
+${layout_declare_spec_const(C, "int", "ngroups", "1")}
+
 #extension GL_EXT_control_flow_attributes : require
 
 /*

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

@@ -280,9 +280,6 @@ Conv2dMethod get_conv2d_method(
   if (!transposed && weight_sizes.at(0) == groups && weight_sizes.at(1) == 1) {
     return Conv2dMethod::Depthwise;
   }
-  if (groups > 1) {
-    VK_THROW("aten.convolution.default: groups > 1 is not supported yet!");
-  }
   if (transposed) {
     return Conv2dMethod::Transposed;
   }
@@ -601,7 +598,7 @@ void add_conv2d_node(
       // Push Constants
       push_constants,
       // Specialization Constants
-      {},
+      {utils::safe_downcast<int32_t>(groups_val)},
       // Resize Args
       {weight_data, stride, padding, dilation, transposed, output_padding},
       // Resizing Logic

backends/vulkan/targets.bzl

@@ -387,6 +387,8 @@ def define_common_targets(is_fbcode = False):
                 "//executorch/backends/transforms:view_copy_to_squeeze_unsqueeze",
                 "//executorch/backends/vulkan/_passes:vulkan_passes",
                 "//executorch/backends/vulkan/serialization:lib",
+                "//executorch/backends/transforms:remove_getitem_op",
+                "//executorch/backends/xnnpack/_passes:xnnpack_passes",
                 "//executorch/exir/backend:backend_details",
             ],
         )

backends/vulkan/test/op_tests/cases.py

@@ -297,6 +297,28 @@ def get_conv_inputs():
     )
 
     test_cases = [
+        Test(
+            self=(1, 64, 256, 256),
+            weight=(64, 32, 3, 3),
+            bias=None,
+            stride=[1, 1],
+            padding=[1, 1],
+            dilation=[1, 1],
+            transposed=False,
+            output_padding=[0, 0],
+            groups=2,
+        ),
+        Test(
+            self=(1, 16, 3, 3),
+            weight=(16, 8, 3, 3),
+            bias=None,
+            stride=[1, 1],
+            padding=[1, 1],
+            dilation=[1, 1],
+            transposed=False,
+            output_padding=[0, 0],
+            groups=2,
+        ),
         Test(
             self=(1, 6, 40, 50),
             weight=(8, 6, 3, 3),

backends/vulkan/utils.py

@@ -1059,6 +1059,8 @@ def get_node_val_str(node: torch.fx.Node) -> str:
         assert isinstance(node.meta["val"], (list, tuple))
         return f"[{', '.join(get_tensor_val_str(t) for t in node.meta['val'])}]"
     else:
+        if "val" not in node.meta:
+            return str(node)
         return str(node.meta["val"])