[ET-VK] Implementation of to_dim_order_copy (#15677)

Title says it all!

Previously, to_dim_order_copy was handled by removing the op. However, this is not possible if the op is modifying the dtype of the original tensor, so these instances of the op would be skipped by the partitioner.

This diff adds an implementation dtype conversion, which allows to_dim_order_copy to be lowered.

Differential Revision: [D86340341](https://our.internmc.facebook.com/intern/diff/D86340341/)

Author: pytorchbot

backends/vulkan/_passes/remove_redundant_ops.py

@@ -31,35 +31,37 @@ class RemoveRedundantOpsTransform(ExportPass):
         exir_ops.edge.aten.lift_fresh_copy.default,
         exir_ops.edge.dim_order_ops._to_dim_order_copy.default,
         exir_ops.edge.dim_order_ops._clone_dim_order.default,
+        exir_ops.edge.aten.expand_copy.default,
     }
 
     def __init__(self) -> None:
         super(RemoveRedundantOpsTransform, self).__init__()
 
     def _should_remove(self, node: torch.fx.Node) -> bool:
-        if node.target in self.redundant_ops:
-            return True
-
-        # Only remove to_copy if dtype does not change. Otherwise, memory format changes
-        # will be handled internally by the backend.
-        if (
-            node.target == exir_ops.edge.aten._to_copy.default
-            or node.target == torch.ops.aten._to_copy.default
-        ):
-            src_dtype = node.meta["val"].dtype
-            # pyre-ignore
-            dst_dtype = node.args[0].meta["val"].dtype
-            return src_dtype == dst_dtype
-
-        return False
+        if node.target not in self.redundant_ops:
+            return False
+
+        orig_node = node.args[0]
+        assert isinstance(orig_node, torch.fx.Node)
+
+        src_dtype = orig_node.meta["val"].dtype
+        dst_dtype = node.meta["val"].dtype
+
+        # Do not remove if the op is converting the dtype.
+        if src_dtype != dst_dtype:
+            return False
+
+        src_shape = orig_node.meta["val"].shape
+        dst_shape = node.meta["val"].shape
+
+        return src_shape == dst_shape
 
     def _remove(self, graph_module: torch.fx.GraphModule) -> None:
         for node in graph_module.graph.nodes:
             if not self._should_remove(node):
                 continue
 
-            with graph_module.graph.inserting_after(node):
-                node.replace_all_uses_with(node.args[0])
+            node.replace_all_uses_with(node.args[0])
 
         graph_module.graph.eliminate_dead_code()
 

backends/vulkan/op_registry.py

@@ -7,17 +7,12 @@
 # pyre-unsafe
 
 import operator
-
 from typing import Any, Callable, Dict, List, Optional, Union
 
 import executorch.backends.vulkan.custom_ops_lib  # noqa
-
 import executorch.backends.vulkan.utils as utils
-
 import torch
-
 from executorch.exir.dialects._ops import ops as exir_ops
-
 from executorch.exir.dialects.edge._ops import EdgeOpOverload
 from torch._subclasses.fake_tensor import FakeTensor
 
@@ -129,6 +124,7 @@ def update_features_impl(op: OpKey):
         # Symbolic integer ops
         torch.ops.aten.sym_size.int,
         operator.add,
+        operator.sub,
         operator.lt,
         operator.gt,
         operator.ge,
@@ -297,27 +293,9 @@ def check_to_copy_node(node: torch.fx.Node) -> bool:
 
 @update_features(exir_ops.edge.dim_order_ops._to_dim_order_copy.default)
 def register_to_copy_dim_order_op():
-    # Currently there is no "real" implementation for to_dim_order_copy, but it can be
-    # removed as long as the operator is not changing the dtype, i.e. the operator call
-    # is modifying the dim order only. Therefore, check that the input and output dtypes
-    # are the same, if so the operator is safe to remove.
-    def check_dim_order_copy_node(node: torch.fx.Node) -> bool:
-        in_arg = node.args[0]
-        if not isinstance(in_arg, torch.fx.Node):
-            return False
-
-        in_tensor = in_arg.meta.get("val", None)
-        out_tensor = node.meta.get("val", None)
-
-        if in_tensor.dtype != out_tensor.dtype:
-            return False
-
-        return True
-
     return OpFeatures(
-        inputs_storage=utils.ANY_STORAGE,
+        inputs_storage=utils.ANY_BUFFER,
         supports_resize=True,
-        are_node_inputs_supported_fn=check_dim_order_copy_node,
     )
 
 

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

@@ -18,6 +18,8 @@ ${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", "all_contiguous", "0")}
+
 /*
  * The insight behind the view operation is that the contiguous index of each
  * tensor element in the input and output tensors are the same.
@@ -28,17 +30,20 @@ void main() {
     return;
   }
 
-  TensorIndex outp_tidx;
-  linear_idx_to_tensor_idx(outp, outp_bufi, outp_tidx);
+  uint inp_bufi = outp_bufi;
+  if (all_contiguous == 0) {
+    TensorIndex outp_tidx;
+    linear_idx_to_tensor_idx(outp, outp_bufi, outp_tidx);
 
-  // To map the output to the input, find the input element that has the same
-  // contiguous index as the output element.
-  const uint contig_idx = tensor_idx_to_contiguous_idx(outp, outp_tidx);
+    // To map the output to the input, find the input element that has the same
+    // contiguous index as the output element.
+    const uint contig_idx = tensor_idx_to_contiguous_idx(outp, outp_tidx);
 
-  TensorIndex inp_tidx;
-  contiguous_idx_to_tensor_idx(inp, contig_idx, inp_tidx);
+    TensorIndex inp_tidx;
+    contiguous_idx_to_tensor_idx(inp, contig_idx, inp_tidx);
 
-  const uint inp_bufi = tensor_idx_to_linear_idx(inp, inp_tidx);
+    inp_bufi = tensor_idx_to_linear_idx(inp, inp_tidx);
+  }
 
   t_outp[outp_bufi] = t_inp[inp_bufi];
 }

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

@@ -0,0 +1,54 @@
+#version 450 core
+
+#define PRECISION ${PRECISION}
+
+#define IN_T ${buffer_scalar_type(IN_DTYPE)}
+#define OUT_T ${buffer_scalar_type(OUT_DTYPE)}
+
+${define_required_extensions(IN_DTYPE)}
+${define_required_extensions(OUT_DTYPE)}
+
+layout(std430) buffer;
+
+#include "indexing.glslh"
+
+${layout_declare_buffer(B, "w", "t_outp", OUT_DTYPE)}
+${layout_declare_buffer(B, "r", "t_inp", IN_DTYPE)}
+
+${layout_declare_ubo(B, "BufferMetadata", "outp")}
+${layout_declare_ubo(B, "BufferMetadata", "inp")}
+
+layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in;
+
+${layout_declare_spec_const(C, "int", "all_contiguous", "0")}
+
+/*
+ * The insight behind the view_convert operation is that the contiguous index of each
+ * tensor element in the input and output tensors are the same, but the data types
+ * may be different and need conversion.
+ */
+void main() {
+  const uint outp_bufi = gl_GlobalInvocationID.x;
+  if (outp_bufi >= numel(outp)) {
+    return;
+  }
+
+  uint inp_bufi = outp_bufi;
+
+  if (all_contiguous == 0) {
+    TensorIndex outp_tidx;
+    linear_idx_to_tensor_idx(outp, outp_bufi, outp_tidx);
+
+    // To map the output to the input, find the input element that has the same
+    // contiguous index as the output element.
+    const uint contig_idx = tensor_idx_to_contiguous_idx(outp, outp_tidx);
+
+    TensorIndex inp_tidx;
+    contiguous_idx_to_tensor_idx(inp, contig_idx, inp_tidx);
+
+    inp_bufi = tensor_idx_to_linear_idx(inp, inp_tidx);
+  }
+
+  // Convert data type from input to output
+  t_outp[outp_bufi] = OUT_T(t_inp[inp_bufi]);
+}

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

@@ -0,0 +1,22 @@
+# 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.
+
+view_convert_buffer:
+  parameter_names_with_default_values:
+    IN_DTYPE: float
+    OUT_DTYPE: float
+    STORAGE: buffer
+  generate_variant_forall:
+    combination:
+      parameter_names: [IN_DTYPE, OUT_DTYPE]
+      combos:
+        - parameter_values: [int32, float]
+        - parameter_values: [int32, half]
+        - parameter_values: [uint8, float]
+        - parameter_values: [uint8, half]
+        - parameter_values: [uint8, int32]
+  shader_variants:
+    - NAME: view_convert_buffer

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

@@ -67,6 +67,18 @@ void resize_unsqueeze_node(
 
   std::vector<int64_t> out_sizes = graph->sizes_of(in);
 
+  std::vector<int64_t> unsqueezed_dims;
+
+  if (graph->val_is_int_list(dims_ref)) {
+    const IntListPtr dims = graph->get_int_list(dims_ref);
+    for (int64_t d : *dims) {
+      unsqueezed_dims.push_back(d);
+    }
+  } else {
+    const int64_t dim = graph->extract_scalar<int64_t>(dims_ref);
+    unsqueezed_dims.push_back(dim);
+  }
+
   // Insert singleton dimensions at the specified positions
   for (auto dim : dims_vec) {
     int64_t d = dim;

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

@@ -60,6 +60,16 @@ void resize_view_node(
   }
 }
 
+void resize_to_dim_order_copy_node(
+    ComputeGraph* graph,
+    const std::vector<ArgGroup>& args,
+    const std::vector<ValueRef>& extra_args) {
+  const ValueRef out = args.at(0).refs.at(0);
+  const ValueRef in = args.at(1).refs.at(0);
+  const std::vector<int64_t> in_sizes = graph->sizes_of(in);
+  graph->virtual_resize(out, in_sizes);
+}
+
 void add_view_node(
     ComputeGraph& graph,
     ValueRef in,
@@ -98,6 +108,11 @@ void add_view_copy_buffer_node(
   std::string kernel_name = "view_buffer";
   add_dtype_suffix(kernel_name, graph.dtype_of(out));
 
+  bool all_contiguous = graph.is_contiguous_buffer_tensor(in) &&
+      graph.is_contiguous_buffer_tensor(out);
+
+  int32_t all_contiguous_int = all_contiguous ? 1 : 0;
+
   graph.execute_nodes().emplace_back(new DynamicDispatchNode(
       graph,
       VK_KERNEL_FROM_STR(kernel_name),
@@ -110,7 +125,41 @@ void add_view_copy_buffer_node(
       // Push Constants
       {},
       // Specialization Constants
+      {all_contiguous_int},
+      // Resize Args
+      resize_args,
+      // Resizing Logic
+      resize_fn));
+}
+
+void add_view_copy_convert_buffer_node(
+    ComputeGraph& graph,
+    ValueRef in,
+    ValueRef out,
+    const std::vector<ValueRef>& resize_args,
+    const ExecuteNode::ResizeFunction& resize_fn) {
+  std::string kernel_name = "view_convert_buffer";
+  add_dtype_suffix(kernel_name, graph.dtype_of(in));
+  add_dtype_suffix(kernel_name, graph.dtype_of(out));
+
+  bool all_contiguous = graph.is_contiguous_buffer_tensor(in) &&
+      graph.is_contiguous_buffer_tensor(out);
+
+  int32_t all_contiguous_int = all_contiguous ? 1 : 0;
+
+  graph.execute_nodes().emplace_back(new DynamicDispatchNode(
+      graph,
+      VK_KERNEL_FROM_STR(kernel_name),
+      default_pick_global_wg_size,
+      default_pick_local_wg_size,
+      // Inputs and Outputs
+      {{out, vkapi::kWrite}, {in, vkapi::kRead}},
+      // Parameter Buffers
+      {graph.buffer_meta_ubo(out), graph.buffer_meta_ubo(in)},
+      // Push Constants
       {},
+      // Specialization Constants
+      {all_contiguous_int},
       // Resize Args
       resize_args,
       // Resizing Logic
@@ -132,8 +181,38 @@ void view(ComputeGraph& graph, const std::vector<ValueRef>& args) {
   return add_view_node(graph, in, sizes, out);
 }
 
+void to_dim_order_copy(ComputeGraph& graph, const std::vector<ValueRef>& args) {
+  int args_idx = 0;
+  const ValueRef in = args.at(args_idx++);
+  const ValueRef dtype = args.at(args_idx++);
+  (void)dtype;
+  const ValueRef layout = args.at(args_idx++);
+  (void)layout;
+  const ValueRef device = args.at(args_idx++);
+  (void)device;
+  const ValueRef pin_memory = args.at(args_idx++);
+  (void)pin_memory;
+  const ValueRef non_blocking = args.at(args_idx++);
+  (void)non_blocking;
+  const ValueRef dim_order = args.at(args_idx++);
+  (void)dim_order;
+
+  const ValueRef out = args.at(args_idx++);
+
+  VK_CHECK_COND(graph.is_buffer_storage(in) && graph.is_buffer_storage(out));
+
+  if (graph.dtype_of(in) == graph.dtype_of(out)) {
+    return add_view_copy_buffer_node(
+        graph, in, out, {}, resize_to_dim_order_copy_node);
+  }
+
+  return add_view_copy_convert_buffer_node(
+      graph, in, out, {}, resize_to_dim_order_copy_node);
+}
+
 REGISTER_OPERATORS {
   VK_REGISTER_OP(aten.view_copy.default, view);
+  VK_REGISTER_OP(dim_order_ops._to_dim_order_copy.default, to_dim_order_copy);
 }
 
 } // namespace vkcompute

backends/vulkan/runtime/graph/ops/impl/View.h

@@ -24,6 +24,19 @@ void add_view_copy_buffer_node(
     const std::vector<ValueRef>& resize_args,
     const ExecuteNode::ResizeFunction& resize_fn);
 
+/*
+ * Dispatches the view_convert_buffer compute shader. This can be used to
+ * implement ops that preserve the "contiguous" indexes of elements between the
+ * input and output while converting between different data types such as
+ * view_copy with dtype conversion.
+ */
+void add_view_copy_convert_buffer_node(
+    ComputeGraph& graph,
+    ValueRef in,
+    ValueRef out,
+    const std::vector<ValueRef>& resize_args,
+    const ExecuteNode::ResizeFunction& resize_fn);
+
 void add_view_node(
     ComputeGraph& graph,
     ValueRef in,