[Auto Scheduler] Mali Support (apache#7132)

* [Auto Scheduler] Mali Support

* Fix doc

* fix lint

* address comments

* fix doc

python/tvm/auto_scheduler/relay_integration.py

@@ -259,9 +259,13 @@ def auto_schedule_topi(outs, has_complex_op):
 
     key = register_workload_tensors(dag.hash_key(), io_tensors)
 
-    # only enable layout rewrite for cpu backend
+    # only enable layout rewrite for cpu / mali backend
     target = tvm.target.Target.current()
-    enable_layout_rewrite = "cpu" in target.keys
+    enable_layout_rewrite_targets = ["cpu", "mali"]
+    enable_layout_rewrite = any(
+        enable_layout_rewrite_target in target.keys
+        for enable_layout_rewrite_target in enable_layout_rewrite_targets
+    )
 
     env = TracingEnvironment.current
     if env is None:

python/tvm/relay/op/strategy/mali.py

@@ -18,6 +18,7 @@
 # pylint: disable=invalid-name,unused-argument,wildcard-import,unused-wildcard-import
 import re
 from tvm import topi
+from tvm.auto_scheduler import is_auto_scheduler_enabled
 from .generic import *
 from .. import op as _op
 
@@ -69,6 +70,38 @@ def conv2d_strategy_mali(attrs, inputs, out_type, target):
                 raise RuntimeError(
                     "Unsupported weight layout {} for conv2d NCHW".format(kernel_layout)
                 )
+        elif layout == "NHWC":
+            assert kernel_layout == "HWIO"
+            if not is_auto_scheduler_enabled():
+                raise RuntimeError(
+                    "conv2d NHWC layout is not enabled for mali without auto_scheduler."
+                )
+            strategy.add_implementation(
+                wrap_compute_conv2d(topi.nn.conv2d_nhwc, need_auto_scheduler_layout=True),
+                naive_schedule,
+                name="conv2d_nhwc.mali",
+            )
+            is_winograd_applicable = False
+            if len(kernel.shape) == 4:
+                kernel_h, kernel_w, _, _ = get_const_tuple(kernel.shape)
+                is_winograd_applicable = (
+                    "float" in data.dtype
+                    and "float" in kernel.dtype
+                    and kernel_h == 3
+                    and kernel_w == 3
+                    and stride_h == 1
+                    and stride_w == 1
+                    and dilation_h == 1
+                    and dilation_w == 1
+                )
+            if is_winograd_applicable:
+                strategy.add_implementation(
+                    wrap_compute_conv2d(topi.nn.conv2d_winograd_nhwc),
+                    naive_schedule,  # this implementation should never be picked by autotvm
+                    name="conv2d_nhwc.winograd",
+                    plevel=15,
+                )
+
         else:
             raise RuntimeError("Unsupported conv2d layout {} for mali".format(layout))
     elif is_depthwise_conv2d(data.shape, layout, kernel.shape, kernel_layout, groups):
@@ -79,6 +112,17 @@ def conv2d_strategy_mali(attrs, inputs, out_type, target):
                 wrap_topi_schedule(topi.mali.schedule_depthwise_conv2d_nchw),
                 name="depthwise_conv2d_nchw.mali",
             )
+        elif layout == "NHWC":
+            assert kernel_layout == "HWOI"
+            if not is_auto_scheduler_enabled():
+                raise RuntimeError(
+                    "depthwise_conv2d NHWC layout is not enabled for mali without auto_scheduler."
+                )
+            strategy.add_implementation(
+                wrap_compute_conv2d(topi.nn.depthwise_conv2d_nhwc),
+                naive_schedule,
+                name="depthwise_conv2d_nhwc.mali",
+            )
         else:
             raise RuntimeError("Unsupported depthwise_conv2d layout {} for mali".format(layout))
     else:  # group_conv2d
@@ -105,6 +149,17 @@ def conv2d_winograd_without_weight_transfrom_strategy_mali(attrs, inputs, out_ty
             wrap_topi_schedule(topi.mali.schedule_conv2d_nchw_winograd),
             name="conv2d_nchw_winograd.mali",
         )
+    elif layout == "NHWC":
+        if not is_auto_scheduler_enabled():
+            raise RuntimeError(
+                "Winograd conv2d NHWC is not enabled for mali without auto_scheduler."
+            )
+        strategy.add_implementation(
+            wrap_compute_conv2d(topi.nn.conv2d_winograd_nhwc_without_weight_transform),
+            naive_schedule,  # this implementation should never be picked by autotvm
+            name="conv2d_nhwc_winograd_without_weight_transform",
+            plevel=15,
+        )
     else:
         raise RuntimeError(
             "Unsupported conv2d_winograd_without_weight_transfrom layout {}".format(layout)

python/tvm/topi/mali/conv2d.py

@@ -16,6 +16,7 @@
 # under the License.
 # pylint: disable=invalid-name,unused-variable,unused-argument,no-else-return
 """conv2d schedule on ARM Mali GPU"""
+import logging
 import tvm
 from tvm import te
 from tvm import relay
@@ -25,10 +26,13 @@
 from ..utils import traverse_inline, get_const_int, get_const_tuple
 from .. import nn
 from ..nn.winograd_util import winograd_transform_matrices
+from ..nn.conv2d import conv2d_winograd_nhwc, _conv2d_winograd_nhwc_impl
 
 # reuse some compute declarations from ARM CPU
 from ..arm_cpu.conv2d_spatial_pack import conv2d_spatial_pack_nchw
 
+logger = logging.getLogger("topi")
+
 
 @autotvm.register_topi_compute("conv2d_nchw_spatial_pack.mali")
 def conv2d_nchw_spatial_pack(cfg, data, kernel, strides, padding, dilation, out_dtype):
@@ -188,8 +192,12 @@ def _schedule_spatial_pack(cfg, s, output, conv, data_vec, kernel_vec):
 
 
 ##### WINOGRAD TEMPLATE #####
-def _pick_tile_size(data, kernel):
-    N, CI, H, W = get_const_tuple(data.shape)
+def _pick_tile_size(data, kernel, layout="NCHW"):
+    if layout == "NCHW":
+        N, CI, H, W = get_const_tuple(data.shape)
+    else:
+        assert layout == "NHWC"
+        N, H, W, CI = get_const_tuple(data.shape)
 
     if H % 4 == 0:
         return 4
@@ -467,30 +475,54 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
     target = tvm.target.Target.current(allow_none=False)
     dispatch_ctx = autotvm.task.DispatchContext.current
 
-    _, outs = relay.backend.compile_engine.select_implementation(
+    new_attrs = {k: attrs[k] for k in attrs.keys()}
+
+    strides = attrs.get_int_tuple("strides")
+    padding = attrs.get_int_tuple("padding")
+    dilation = attrs.get_int_tuple("dilation")
+    data_layout = attrs["data_layout"]
+    kernel_layout = attrs["kernel_layout"]
+    data, kernel = tinfos
+    out_dtype = out_type.dtype
+
+    impl, outs = relay.backend.compile_engine.select_implementation(
         relay.op.get("nn.conv2d"), attrs, tinfos, out_type, target
     )
     workload = autotvm.task.get_workload(outs)
     if workload is None:
-        # The best implementation is not an AutoTVM template,
-        # we then assume it's not necessary to alter this op.
+        # The best implementation is not an AutoTVM template.
+        # It may be from the auto-scheduler
+        if impl.name.find("winograd") != -1:
+            if dilation != (1, 1):
+                logger.warning("Does not support weight pre-transform for dilated convolution.")
+                return None
+
+            assert data_layout == "NHWC" and kernel_layout == "HWIO"
+            N, H, W, CI = get_const_tuple(data.shape)
+            KH, KW, _, CO = get_const_tuple(kernel.shape)
+
+            # Pre-compute weight transformation in winograd
+            tile_size = _pick_tile_size(tinfos[0], tinfos[1], layout="NHWC")
+
+            # HWIO -> OIHW
+            kernel_transform = relay.transpose(inputs[1], axes=[3, 2, 0, 1])
+            # alpha, alpha, CO, CI
+            weight = relay.nn.contrib_conv2d_winograd_weight_transform(
+                kernel_transform, tile_size=tile_size
+            )
+            new_attrs["tile_size"] = tile_size
+            new_attrs["channels"] = CO
+            return relay.nn.contrib_conv2d_winograd_without_weight_transform(
+                inputs[0], weight, **new_attrs
+            )
+
         return None
     cfg = dispatch_ctx.query(target, workload)
     if cfg.is_fallback:  # if is fallback, clear query cache and return None
         autotvm.task.clear_fallback_cache(target, workload)
         return None
 
     topi_tmpl = workload[0]
-    new_attrs = {k: attrs[k] for k in attrs.keys()}
-
-    strides = attrs.get_int_tuple("strides")
-    padding = attrs.get_int_tuple("padding")
-    dilation = attrs.get_int_tuple("dilation")
-    data_layout = attrs["data_layout"]
-    kernel_layout = attrs["kernel_layout"]
-    data, kernel = tinfos
-    out_dtype = out_type.dtype
-
     idxd = tvm.tir.indexdiv
 
     if topi_tmpl == "conv2d_nchw_spatial_pack.mali":
@@ -545,6 +577,19 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         return None
 
 
+@conv2d_winograd_nhwc.register(["mali"])
+def conv2d_winograd_nhwc_mali(
+    data, weight, strides, padding, dilation, out_dtype, pre_computed=False
+):
+    """Conv2D Winograd in NHWC layout.
+    This is a clean version to be used by the auto-scheduler for mali.
+    """
+    tile_size = _pick_tile_size(data, weight, layout="NHWC")
+    return _conv2d_winograd_nhwc_impl(
+        data, weight, strides, padding, dilation, out_dtype, tile_size, pre_computed
+    )
+
+
 ##### SCHECULE UTILITIES #####
 def tile_and_bind(s, tensor, y, x, y_factor, x_factor=None):
     """ tile and bind to GPU threads """

python/tvm/topi/nn/conv2d.py

@@ -382,7 +382,18 @@ def conv2d_nhwc(
     if auto_scheduler_rewritten_layout:
         # Infer shape for the rewritten layout
         # todo(merrymercy): wrap this with a more general interface.
-        if len(Filter.shape) >= 10:
+        if len(Filter.shape) == 17:
+            # For mali.
+            # GPU tile structure is SSSRRSRS
+            # You could refer function comment of DoMultiLevelTiling
+            # in the utils.h to see more detail explanation.
+            kernel_h = Filter.shape[6] * Filter.shape[9] * Filter.shape[13]
+            kernel_w = Filter.shape[7] * Filter.shape[10] * Filter.shape[14]
+            channel = Filter.shape[8] * Filter.shape[11] * Filter.shape[15]
+            num_filter = Filter.shape[12] * Filter.shape[16]
+            for i in range(6):
+                num_filter *= Filter.shape[i]
+        elif len(Filter.shape) >= 10:
             # For cpu tile structure SSRSRS
             base = len(Filter.shape) - 10
             kernel_h = Filter.shape[2 + base] * Filter.shape[6 + base]

src/auto_scheduler/search_policy/sketch_policy.cc

@@ -115,21 +115,35 @@ SketchPolicy::SketchPolicy(SearchTask task, CostModel program_cost_model,
     node->mutation_rules.push_back(std::make_shared<MutateParallel>(0.01));
   } else if (IsGPUTask(node->search_task)) {
     // Sketch Generation Rules
-    node->sketch_rules.push_back(&rule_add_cache_read_stage);
-    node->sketch_rules.push_back(&rule_special_compute_location_gpu);
-    node->sketch_rules.push_back(&rule_always_inline);
-    node->sketch_rules.push_back(&rule_simplify_compute_with_const_tensor);
-    node->sketch_rules.push_back(&rule_cross_thread_reduction);
-    node->sketch_rules.push_back(&rule_add_cache_write_stage);
-    node->sketch_rules.push_back(&rule_multi_level_tiling_with_fusion);
-    node->sketch_rules.push_back(&rule_multi_level_tiling);
-    node->sketch_rules.push_back(&rule_skip_stage);
+    if (node->search_task->target->GetAttr<String>("device", "") == "mali") {
+      node->sketch_rules.push_back(&rule_always_inline);
+      node->sketch_rules.push_back(&rule_simplify_compute_with_const_tensor);
+      node->sketch_rules.push_back(&rule_add_rfactor);
+      node->sketch_rules.push_back(&rule_add_cache_write_stage);
+      node->sketch_rules.push_back(&rule_multi_level_tiling_with_fusion);
+      node->sketch_rules.push_back(&rule_multi_level_tiling);
+      node->sketch_rules.push_back(&rule_skip_stage);
+    } else {
+      node->sketch_rules.push_back(&rule_add_cache_read_stage);
+      node->sketch_rules.push_back(&rule_special_compute_location_gpu);
+      node->sketch_rules.push_back(&rule_always_inline);
+      node->sketch_rules.push_back(&rule_simplify_compute_with_const_tensor);
+      node->sketch_rules.push_back(&rule_cross_thread_reduction);
+      node->sketch_rules.push_back(&rule_add_cache_write_stage);
+      node->sketch_rules.push_back(&rule_multi_level_tiling_with_fusion);
+      node->sketch_rules.push_back(&rule_multi_level_tiling);
+      node->sketch_rules.push_back(&rule_skip_stage);
+    }
 
     // Initial Population Generation Rules
     node->init_rules.push_back(&init_fill_tile_size);
     node->init_rules.push_back(&init_thread_bind);
     node->init_rules.push_back(&init_unroll);
 
+    if (node->search_task->target->GetAttr<String>("device", "") == "mali") {
+      node->init_rules.push_back(&init_vectorization);
+    }
+
     // Mutation Rules for Evolutionary Search
     node->mutation_rules.push_back(std::make_shared<MutateTileSize>(0.90));
     node->mutation_rules.push_back(std::make_shared<MutateAutoUnroll>(0.10));
@@ -389,23 +403,22 @@ Array<State> SketchPolicyNode::SampleInitPopulation(const Array<State>& sketches
     std::vector<State> temp_states(population);
 
     // Sample a batch of states randomly
-    support::parallel_for(0, population,
-                          [this, &temp_states, &sketches, &rand_gens](int index) {
-                            // Randomly choose a sketch
-                            State tmp_s = sketches[(rand_gens[index])() % sketches.size()];
-                            // Apply random annotation rules one by one
-                            bool valid = true;
-                            for (const auto& rule : init_rules) {
-                              if (rule->Apply(this, &tmp_s, &rand_gens[index]) ==
-                                  PopulationGenerationRule::ResultKind::kInvalid) {
-                                valid = false;
-                                break;
-                              }
-                            }
-                            if (valid) {
-                              temp_states[index] = std::move(tmp_s);
-                            }
-                          });
+    support::parallel_for(0, population, [this, &temp_states, &sketches, &rand_gens](int index) {
+      // Randomly choose a sketch
+      State tmp_s = sketches[(rand_gens[index])() % sketches.size()];
+      // Apply random annotation rules one by one
+      bool valid = true;
+      for (const auto& rule : init_rules) {
+        if (rule->Apply(this, &tmp_s, &rand_gens[index]) ==
+            PopulationGenerationRule::ResultKind::kInvalid) {
+          valid = false;
+          break;
+        }
+      }
+      if (valid) {
+        temp_states[index] = std::move(tmp_s);
+      }
+    });
 
     // Filter out the states that were failed to apply initial rules
     Array<State> cand_states;

src/auto_scheduler/search_task.cc

@@ -90,6 +90,22 @@ HardwareParams HardwareParamsNode::GetDefaultHardwareParams(const Target& target
     int max_vthread_extent = warp_size / 4;
     return HardwareParams(-1, 16, 64, max_shared_memory_per_block, max_local_memory_per_block,
                           max_threads_per_block, max_vthread_extent, warp_size);
+  } else if (target->kind->device_type == kDLOpenCL) {
+    if (target->GetAttr<String>("device", "") == "mali") {
+      // We cannot use device API to get hardware attributes like CUDA,
+      // because like Mali target is normally on the remote machine.
+      int max_shared_memory_per_block = 32768;
+      int max_local_memory_per_block = INT32_MAX;  // skip the check on local memory
+      int max_threads_per_block = 256;
+      int warp_size = 1;
+      int max_vthread_extent = 1;
+      return HardwareParams(-1, 16, 64, max_shared_memory_per_block, max_local_memory_per_block,
+                            max_threads_per_block, max_vthread_extent, warp_size);
+    } else {
+      // add other opencl target
+      auto target_device = target->GetAttr<String>("device", "");
+      LOG(FATAL) << "No default hardware parameters for opencl target device: " << target_device;
+    }
   } else {
     LOG(FATAL) << "No default hardware parameters for target: " << target;
   }

src/relay/backend/build_module.cc

@@ -343,8 +343,9 @@ class RelayBuildModule : public runtime::ModuleNode {
     if (backend::IsAutoSchedulerEnabled() && targets.size() == 1) {
       const auto& target = (*targets.begin()).second;
       Pass major_pass = transform::AutoSchedulerLayoutRewrite();
-
-      if (target->kind->device_type == kDLCPU && pass_ctx.PassEnabled(major_pass->Info())) {
+      bool enable_layout_rewrite_targets =
+          target->kind->device_type == kDLCPU || target->GetAttr<String>("device", "") == "mali";
+      if (enable_layout_rewrite_targets && pass_ctx.PassEnabled(major_pass->Info())) {
         With<Target> tctx(target);
         relay_module = major_pass(relay_module);
         // Defuse ops to fold constants, then fuse them again