[ET-VK][q8_ops] Add int8x4_buffer_to_nchw shader and refactor Int8x4Staging

Renames Q8taStaging.cpp/h to Int8x4Staging.cpp/h and expands it to cover
the full staging lifecycle for kInt8x4 buffer tensors.

**Rename and split of the old prepack function:**
The old `add_staging_to_int8x4_buffer_node` (which used a static dispatch
node for prepacking TensorRef data into a packed int8x4 buffer) is renamed
to `add_prepack_int8x4_buffer_node` to clarify its role. Two new runtime
staging functions are added alongside it:

- `add_staging_to_int8x4_buffer_node`: reads NCHW data from a staging buffer
  into a kInt8x4 buffer tensor at execute time, using a `DynamicDispatchNode`
  wrapping the existing `nchw_to_int8x4_buffer` shader.
- `add_int8x4_buffer_to_staging_node`: writes packed int8x4 data back from a
  kInt8x4 buffer tensor to a contiguous NCHW staging buffer at execute time,
  using a new `int8x4_buffer_to_nchw` shader.

**New shader (int8x4_buffer_to_nchw.glsl):**
Implements the reverse of `nchw_to_int8x4_buffer`. One thread per output
int32 in the NCHW staging buffer. For each thread it decodes 4 NCHW-ordered
element indices, looks up each element's position in the packed int8x4 buffer
via `tensor4d_idx_to_buf_idx`, extracts the packed byte, and assembles 4
bytes into a single output int32. Works for any GPUMemoryLayout.

**Staging.cpp dispatch:**
`add_staging_to_tensor_node` and `add_tensor_to_staging_node` now both
dispatch to the int8x4-specific functions when the tensor dtype is kInt8x4.
`prepack_op` is updated to call `add_prepack_int8x4_buffer_node`.

**TestQ8taBinary.cpp** is updated to include Int8x4Staging.h and call
`add_prepack_int8x4_buffer_node`.

Differential Revision: [D94364640](https://our.internmc.facebook.com/intern/diff/D94364640/)

[ghstack-poisoned]

Author: ssjia

backends/vulkan/runtime/graph/ops/glsl/int8x4_buffer_to_nchw.glsl

@@ -0,0 +1,75 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#version 450 core
+
+#define PRECISION ${PRECISION}
+
+${define_active_storage_type("buffer")}
+
+layout(std430) buffer;
+
+#include "indexing.glslh"
+
+// Output staging buffer: raw int8 data interpreted as int32 for device compat
+${layout_declare_tensor(B, "w", "nchw_out", "int", "buffer")}
+// Input buffer: packed int8x4 values (each int32 contains 4 packed int8)
+${layout_declare_tensor(B, "r", "t_inp", "int", "buffer")}
+
+// Metadata for input tensor
+${layout_declare_ubo(B, "BufferMetadata", "inp")}
+
+layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
+
+${layout_declare_spec_const(C, "int", "inp_layout", "CONTIG_LAYOUT_INT")}
+
+void main() {
+  // One thread per output int32 in the NCHW staging buffer.
+  // Each output int32 holds 4 consecutive NCHW bytes.
+  const uint out_int32_idx = gl_GlobalInvocationID.x;
+
+  const uint W = inp.sizes[0][0];
+  const uint H = inp.sizes[0][1];
+  const uint C = inp.sizes[0][2];
+  const uint N = inp.sizes[0][3];
+  const uint total_numel = W * H * C * N;
+  const uint num_out_int32s = (total_numel + 3u) / 4u;
+
+  if (out_int32_idx >= num_out_int32s) {
+    return;
+  }
+
+  int output_int32 = 0;
+  [[unroll]] for (int j = 0; j < 4; ++j) {
+    const uint nchw_idx = out_int32_idx * 4u + uint(j);
+    if (nchw_idx >= total_numel) {
+      break;
+    }
+
+    // Convert NCHW linear index to tensor4D (WHCN) coordinates.
+    const uint w = nchw_idx % W;
+    const uint h = (nchw_idx / W) % H;
+    const uint c = (nchw_idx / (W * H)) % C;
+    const uint n = nchw_idx / (W * H * C);
+
+    TensorIndex4D tidx;
+    tidx.data = ivec4(int(w), int(h), int(c), int(n));
+
+    // tensor4d_idx_to_buf_idx returns a linear element index where
+    // element_index / 4 is the int32 slot and element_index % 4 is the byte
+    // position within that int32. This matches the packing order used by
+    // nchw_to_int8x4_buffer when writing to the int8x4 buffer.
+    const int elem_buf_idx = tensor4d_idx_to_buf_idx(inp, tidx, inp_layout);
+    const int int8_val =
+        (t_inp[elem_buf_idx / 4] >> ((elem_buf_idx % 4) * 8)) & 0xFF;
+
+    output_int32 |= (int8_val << (j * 8));
+  }
+
+  nchw_out[out_int32_idx] = output_int32;
+}

backends/vulkan/runtime/graph/ops/glsl/int8x4_buffer_to_nchw.yaml

@@ -0,0 +1,11 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+int8x4_buffer_to_nchw:
+  parameter_names_with_default_values:
+    DTYPE: int
+  shader_variants:
+    - NAME: int8x4_buffer_to_nchw

backends/vulkan/runtime/graph/ops/impl/Int8x4Staging.cpp

@@ -0,0 +1,140 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <executorch/backends/vulkan/runtime/graph/ops/impl/Int8x4Staging.h>
+
+#include <executorch/backends/vulkan/runtime/graph/ops/DynamicDispatchNode.h>
+#include <executorch/backends/vulkan/runtime/graph/ops/impl/Common.h>
+#include <executorch/backends/vulkan/runtime/graph/ops/utils/ShaderNameUtils.h>
+
+namespace vkcompute {
+
+void add_prepack_int8x4_buffer_node(
+    ComputeGraph& graph,
+    const ValueRef tensor_data,
+    const ValueRef tensor) {
+  VK_CHECK_COND(graph.dtype_of(tensor) == vkapi::kInt8x4);
+  // TODO(ssjia): Update shaders to handle high-dim tensors
+  VK_CHECK_COND(graph.dim_of(tensor) <= 4);
+
+  std::string kernel_name = "nchw_to_int8x4_buffer";
+
+  vkapi::ParamsBindList param_buffers;
+  param_buffers.append(graph.buffer_meta_ubo(tensor));
+
+  // One thread per texel (each texel = one int32 = 4 packed int8).
+  // Use padded_numel to account for dimension padding in packed int8 layouts
+  // (e.g., kPackedInt8_4C with C=3 pads to C=4).
+  uint32_t num_texels =
+      utils::safe_downcast<uint32_t>(graph.padded_numel_of(tensor) / 4);
+  utils::uvec3 global_wg_size = {num_texels, 1, 1};
+  utils::uvec3 local_wg_size = graph.create_local_wg_size(global_wg_size);
+
+  graph.prepack_nodes().emplace_back(new PrepackNode(
+      graph,
+      VK_KERNEL_FROM_STR(kernel_name),
+      global_wg_size,
+      local_wg_size,
+      // Input and Output
+      tensor_data,
+      tensor,
+      // Parameter Buffers
+      param_buffers,
+      // Specialization Constants
+      {graph.hashed_layout_of(tensor)}));
+}
+
+static utils::uvec3 staging_to_int8x4_buffer_global_wg_size(
+    ComputeGraph* graph,
+    const vkapi::ShaderInfo& shader,
+    const std::vector<ArgGroup>& args,
+    const std::vector<ValueRef>& resize_args) {
+  (void)shader;
+  (void)resize_args;
+  const ValueRef out_tensor = args.at(0).refs.at(0);
+  const uint32_t num_texels =
+      utils::safe_downcast<uint32_t>(graph->padded_numel_of(out_tensor) / 4);
+  return {num_texels, 1, 1};
+}
+
+void add_staging_to_int8x4_buffer_node(
+    ComputeGraph& graph,
+    const ValueRef in_staging,
+    const ValueRef tensor) {
+  VK_CHECK_COND(graph.dtype_of(tensor) == vkapi::kInt8x4);
+  // TODO(ssjia): Update shaders to handle high-dim tensors
+  VK_CHECK_COND(graph.dim_of(tensor) <= 4);
+
+  vkapi::ParamsBindList param_buffers;
+  param_buffers.append(graph.buffer_meta_ubo(tensor));
+
+  graph.execute_nodes().emplace_back(new DynamicDispatchNode(
+      graph,
+      VK_KERNEL_FROM_STR("nchw_to_int8x4_buffer"),
+      staging_to_int8x4_buffer_global_wg_size,
+      default_pick_local_wg_size,
+      // Input and Output
+      {{tensor, vkapi::kWrite}, {in_staging, vkapi::kRead}},
+      // Parameter Buffers
+      param_buffers,
+      // Push Constants
+      {},
+      // Specialization Constants
+      {graph.hashed_layout_of(tensor)},
+      // Resize Args
+      {},
+      // Resizing Logic
+      nullptr));
+}
+
+static utils::uvec3 int8x4_buffer_to_staging_global_wg_size(
+    ComputeGraph* graph,
+    const vkapi::ShaderInfo& shader,
+    const std::vector<ArgGroup>& args,
+    const std::vector<ValueRef>& resize_args) {
+  (void)shader;
+  (void)resize_args;
+  const ValueRef in_tensor = args.at(1).refs.at(0);
+  // One thread per output int32 in the NCHW staging buffer.
+  const int32_t numel = graph->numel_of(in_tensor);
+  const uint32_t num_out_int32s =
+      utils::safe_downcast<uint32_t>((numel + 3) / 4);
+  return {num_out_int32s, 1, 1};
+}
+
+void add_int8x4_buffer_to_staging_node(
+    ComputeGraph& graph,
+    const ValueRef tensor,
+    const ValueRef staging_data) {
+  VK_CHECK_COND(graph.dtype_of(tensor) == vkapi::kInt8x4);
+  // TODO(ssjia): Update shaders to handle high-dim tensors
+  VK_CHECK_COND(graph.dim_of(tensor) <= 4);
+
+  vkapi::ParamsBindList param_buffers;
+  param_buffers.append(graph.buffer_meta_ubo(tensor));
+
+  graph.execute_nodes().emplace_back(new DynamicDispatchNode(
+      graph,
+      VK_KERNEL_FROM_STR("int8x4_buffer_to_nchw"),
+      int8x4_buffer_to_staging_global_wg_size,
+      default_pick_local_wg_size,
+      // Input and Output
+      {{staging_data, vkapi::kWrite}, {tensor, vkapi::kRead}},
+      // Parameter Buffers
+      param_buffers,
+      // Push Constants
+      {},
+      // Specialization Constants
+      {graph.hashed_layout_of(tensor)},
+      // Resize Args
+      {},
+      // Resizing Logic
+      nullptr));
+}
+
+} // namespace vkcompute

…kan/runtime/graph/ops/impl/Q8taStaging.h …n/runtime/graph/ops/impl/Int8x4Staging.hbackends/vulkan/runtime/graph/ops/impl/Q8taStaging.h renamed to backends/vulkan/runtime/graph/ops/impl/Int8x4Staging.h backends/vulkan/runtime/graph/ops/impl/Q8taStaging.h renamed to backends/vulkan/runtime/graph/ops/impl/Int8x4Staging.h

@@ -12,9 +12,19 @@
 
 namespace vkcompute {
 
-void add_staging_to_int8x4_buffer_node(
+void add_prepack_int8x4_buffer_node(
     ComputeGraph& graph,
     const ValueRef tensor_data,
     const ValueRef tensor);
 
+void add_staging_to_int8x4_buffer_node(
+    ComputeGraph& graph,
+    const ValueRef in_staging,
+    const ValueRef tensor);
+
+void add_int8x4_buffer_to_staging_node(
+    ComputeGraph& graph,
+    const ValueRef tensor,
+    const ValueRef staging_data);
+
 } // namespace vkcompute

backends/vulkan/runtime/graph/ops/impl/Q8taStaging.cpp

@@ -12,7 +12,7 @@
 
 #include <executorch/backends/vulkan/runtime/graph/ops/DynamicDispatchNode.h>
 #include <executorch/backends/vulkan/runtime/graph/ops/impl/Common.h>
-#include <executorch/backends/vulkan/runtime/graph/ops/impl/Q8taStaging.h>
+#include <executorch/backends/vulkan/runtime/graph/ops/impl/Int8x4Staging.h>
 #include <executorch/backends/vulkan/runtime/graph/ops/utils/ShaderNameUtils.h>
 #include <executorch/backends/vulkan/runtime/graph/ops/utils/StagingUtils.h>
 
@@ -27,6 +27,10 @@ void add_staging_to_tensor_node(
     const ValueRef out_tensor) {
   VK_CHECK_COND(graph.val_is_staging(in_staging));
 
+  if (graph.dtype_of(out_tensor) == vkapi::kInt8x4) {
+    return add_staging_to_int8x4_buffer_node(graph, in_staging, out_tensor);
+  }
+
   vkapi::ShaderInfo shader = get_nchw_to_tensor_shader(
       graph,
       out_tensor,
@@ -104,6 +108,10 @@ void add_tensor_to_staging_node(
     const ValueRef out_staging) {
   VK_CHECK_COND(graph.val_is_staging(out_staging));
 
+  if (graph.dtype_of(in_tensor) == vkapi::kInt8x4) {
+    return add_int8x4_buffer_to_staging_node(graph, in_tensor, out_staging);
+  }
+
   vkapi::ShaderInfo shader = get_tensor_to_nchw_shader(
       graph,
       in_tensor,
@@ -329,7 +337,7 @@ ValueRef prepack_int4_linear_weight_transposed_interleaved(
 
 void prepack_op(ComputeGraph& graph, const std::vector<ValueRef>& args) {
   if (graph.dtype_of(args[1]) == vkapi::kInt8x4) {
-    return add_staging_to_int8x4_buffer_node(graph, args[0], args[1]);
+    return add_prepack_int8x4_buffer_node(graph, args[0], args[1]);
   }
   return add_prepack_standard_node(graph, args[0], args[1]);
 }

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

@@ -8,9 +8,9 @@
 
 #include <executorch/backends/vulkan/runtime/graph/ops/OperatorRegistry.h>
 
+#include <executorch/backends/vulkan/runtime/graph/ops/impl/Int8x4Staging.h>
 #include <executorch/backends/vulkan/runtime/graph/ops/impl/Q8taBinary.h>
 #include <executorch/backends/vulkan/runtime/graph/ops/impl/Q8taQuantizeDequantize.h>
-#include <executorch/backends/vulkan/runtime/graph/ops/impl/Q8taStaging.h>
 
 namespace vkcompute {
 
@@ -62,7 +62,7 @@ void q8ta_add_test(ComputeGraph& graph, const std::vector<ValueRef>& args) {
   if (input_b_is_int8) {
     // Input B is a pre-quantized int8 TensorRef; prepack directly into packed
     // int8x4 format
-    add_staging_to_int8x4_buffer_node(graph, input_b, packed_int8_input_b);
+    add_prepack_int8x4_buffer_node(graph, input_b, packed_int8_input_b);
   } else {
     // Input B is a float tensor; quantize at runtime
     add_q8ta_quantize_node(