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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2022/07/22 12:33:47 UTC
[GitHub] [tvm] echuraev commented on a diff in pull request #11878: [Adreno] Add markup pass of relay tensors for static texture planning
echuraev commented on code in PR #11878:
URL: https://github.com/apache/tvm/pull/11878#discussion_r927596550
##########
tests/python/relay/test_conv2d_nchw_texture.py:
##########
@@ -435,3 +435,641 @@ def test_conv2d_vgg16_winograd_4d():
graph = build_run_compare(mod, params1, {"data": input_shape}, dtype, target)
matches = re.findall("winograd", graph)
assert len(matches) > 0
+
+
+@tvm.testing.requires_opencl
+def test_2conv2d():
Review Comment:
Probably let's rename this test to something more meaningful?
##########
src/relay/transforms/annotate_texture_storage.cc:
##########
@@ -0,0 +1,528 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file annotate_texture_storage.cc
+ * \brief Collection of target specific relay passes which
+ * storage scope related information.
+ *
+ * - CollectStorageInfo returns a mapping from relay expr
+ * to a list of output storage scopes for each output.
+ * These scopes are used during memory planning as well
+ * as downstream when doing codegen and in the graph runtime when doing runtime dataspace
+ * allocations.
+ *
+ * - AnnotateMemoryScope calls *target.CollectStorageInfo for all target been represented
+ * in the graph and rewrites graph modifying or inserting of VirtualDevice with required
+ * memory_scop collected from the CollectStorageInfo
+ */
+
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/transform.h>
+#include <tvm/tir/expr.h>
+
+#include <memory>
+#include <unordered_map>
+
+#include "../transforms/device_aware_visitors.h"
+
+namespace tvm {
+namespace relay {
+namespace {
+
+/**
+ * @brief Analyzes the graph and returns mapping of expressions vs desired memory scope
+ */
+class StorageInfo : private transform::DeviceAwareExprVisitor {
+ public:
+ StorageInfo() : transform::DeviceAwareExprVisitor(Optional<IRModule>()) {}
+
+ static Map<Expr, Array<String>> GetStorageMap(const Expr& expr) {
+ StorageInfo storage_info;
+ storage_info.VisitExpr(expr);
+ storage_info.LegalizeProducerStorage();
+ Map<Expr, Array<String>> storage_map;
+ for (auto& kv : storage_info.storage_scope_) {
+ std::vector<String> storage_scopes;
+ std::copy(kv.second.begin(), kv.second.end(), std::back_inserter(storage_scopes));
+ storage_map.Set(GetRef<Expr>(kv.first), Array<String>{storage_scopes});
+ }
+
+ // Filling the input arguments by "global" scope to handle PlanDevice algo which propagates
+ // virtual devices from outputs to inputs. At the same time outputs must be unconstrained
+ // to avoid useless device_copy
+ for (const auto& cs : storage_info.consumer_storage_scopes_) {
+ // we have record in consumers that mean that potentially consumer
+ // dealt with textures anyhow, it's safe to mark this expr as global scope
+ // even without verification of the consumer's outputs scope
+ if (storage_info.CanConsumeTextures(cs.second) &&
+ storage_map.find(GetRef<Expr>(cs.first)) == storage_map.end()) {
+ storage_map.Set(GetRef<Expr>(cs.first), Array<String>{"global"});
+ }
+ }
+
+ // initial algo assumes mapping of outputs of the expr that is not enough, need to update
+ // VirtualDevice for function variables to get proper codegen. Adding vars to storage_map
+ for (const auto& a : storage_info.args_to_vars_) {
+ if (storage_map.count(a.first)) {
+ for (const auto& v : a.second) {
+ storage_map.Set(v, storage_map[a.first]);
+ }
+ }
+ }
+ return storage_map;
+ }
+
+ private:
+ void Visit(const Expr& expr) {
+ // Pre-order traversal to enable upward propagation
+ // of consumer storage scopes to producers when desirable.
+ if (const auto* fn = expr.as<FunctionNode>()) {
+ this->VisitExpr(fn->body);
+ for (const auto& param : fn->params) {
+ this->VisitExpr(param);
+ }
+ } else {
+ this->VisitExpr(expr);
+ }
+ }
+
+ void VisitExpr_(const VarNode* vn) final { ApplyConsumerScopeToInputs(vn); }
+
+ void VisitExpr_(const ConstantNode* cn) final { ApplyConsumerScopeToInputs(cn); }
+
+ void DeviceAwareVisitExpr_(const CallNode* call) final {
+ // Check the contents of this primitive function
+ if (DeviceSupportsTextureStorage(GetRef<Expr>(call))) {
+ if (const auto* fn = call->op.as<FunctionNode>()) {
+ if (fn->HasNonzeroAttr(attr::kPrimitive)) {
+ primitive_supports_texture_ = false;
+ Visit(call->op);
+ if (primitive_supports_texture_) {
+ if (call->checked_type().as<TensorTypeNode>()) {
+ std::string scope = "global.texture";
+ if (const auto* ttype = call->checked_type().as<TensorTypeNode>()) {
+ if (ttype->shape.size() == 5) {
+ scope = Scope(ttype->shape, GetVirtualDevice(GetRef<Expr>(call)));
+ }
+ }
+ storage_scope_[call].push_back(scope);
+ } else {
+ const auto* tuple_type = call->type_as<TupleTypeNode>();
+ ICHECK(tuple_type);
+ // TODO(csullivan): Add support for mixed output storage scope.
+ // In current adreno storage planner all outputs of a
+ // primitive function are assumed to be of the same storage
+ // type. This should be easy to extend in the future.
+ for (size_t i = 0; i < tuple_type->fields.size(); i++) {
+ storage_scope_[call].push_back("global.texture");
+ }
+ }
+ for (size_t i = 0; i < fn->params.size(); i++) {
+ args_to_vars_[call->args[i]].push_back(fn->params[i]);
+ }
+ }
+ // Add consumer storage scope information for call arguments
+ for (auto& arg : call->args) {
+ if (storage_scope_.count(call)) {
+ ICHECK(!HasMixedStorageOutputs(call))
+ << "Mixed output storage scopes are not currently supported";
+ consumer_storage_scopes_[arg.operator->()].push_back("global.texture");
+ } else {
+ consumer_storage_scopes_[arg.operator->()].push_back("global");
+ }
+ }
+ }
+ }
+ }
+
+ primitive_supports_texture_ = SupportsTextureStorage(call);
+
+ for (auto& arg : call->args) {
+ Visit(arg);
+ }
+ // We have all callees filled into storage_scope_ if they support textures
+ // We need to verify if this call expects texture and if it does not, remove from
+ // storage_scope_ since initially storage_scope_ is filled only based on knowledge
+ // that function able to work with textures, but not necessary that this texture is
+ // expected by function callee
+ for (auto& arg : call->args) {
+ if (consumer_storage_scopes_.count(arg.operator->()) &&
+ GetConsumerScope(consumer_storage_scopes_[arg.operator->()]) != "global.texture") {
+ storage_scope_.erase(arg.operator->());
+ if (const auto* cn = arg.as<CallNode>()) {
+ if (const auto* fn = cn->op.as<FunctionNode>()) {
+ storage_scope_.erase(fn->body.operator->());
+ }
+ }
+ }
+ }
+ }
+
+ std::string Scope(Array<PrimExpr> shape, const VirtualDevice& vd) {
+ if (vd != VirtualDevice::FullyUnconstrained()) {
+ std::map<int, std::string> diffs;
+ int limit =
+ vd->target->GetAttr<Integer>("texture_spatial_limit").value_or(Integer(16384))->value;
+ int a0 = shape[0].as<IntImmNode>()->value;
+ int a1 = shape[1].as<IntImmNode>()->value;
+ int a2 = shape[2].as<IntImmNode>()->value;
+ int a3 = shape[3].as<IntImmNode>()->value;
+
+ int d3l = a0 * a1 * a2;
+ int d3r = a3;
+ int diff3 = d3l > d3r ? d3l - d3r : d3r - d3l;
+ if (d3l < limit && d3r < limit) diffs[diff3] = "";
+
+ int d2l = a0 * a1;
+ int d2r = a2 * a3;
+ int diff2 = d2l > d2r ? d2l - d2r : d2r - d2l;
+ if (d2l < limit && d2r < limit) diffs[diff2] = "nhwc";
+
+ int d1l = a0;
+ int d1r = a1 * a2 * a3;
+ int diff1 = d1l > d1r ? d1l - d1r : d1r - d1l;
+ if (d1l < limit && d1r < limit) diffs[diff1] = "weight";
+ if (!diffs.empty()) {
+ std::string scope = "global.texture";
+ if (!diffs.begin()->second.empty()) {
+ scope += ("-" + diffs.begin()->second);
+ }
+ return scope;
+ }
+ }
+ return "global";
+ }
+
+ void ApplyConsumerScopeToInputs(const ExprNode* expr) {
+ std::string scope;
+ auto consumer_scopes_it = consumer_storage_scopes_.find(expr);
+ if (consumer_scopes_it != consumer_storage_scopes_.end()) {
+ std::string consumer_scope = GetConsumerScope(consumer_scopes_it->second);
+ ICHECK(!storage_scope_.count(expr))
+ << "Already propagated consumer scopes to input: " << GetRef<Expr>(expr);
+
+ bool expr_is_rgba_vectorizable = false;
+ if (const auto* ttype = expr->checked_type().as<TensorTypeNode>()) {
+ if (ttype->shape.size() == 5) {
+ scope = Scope(ttype->shape, GetVirtualDevice(GetRef<Expr>(expr)));
+ if (scope != "global") {
+ auto inner_dim = ttype->shape.back().as<IntImmNode>();
+ if (inner_dim && inner_dim->value == 4) {
+ expr_is_rgba_vectorizable = true;
+ }
+ }
+ }
+ }
+
+ // Only propagate texture scope from consumers to input expr if
+ // the input shape of the input expr is rgba vectorizable.
+ if (consumer_scope.find("global.texture") != std::string::npos) {
+ if (expr_is_rgba_vectorizable) {
+ storage_scope_[expr].push_back(scope);
+ }
+ } else {
+ storage_scope_[expr].push_back(consumer_scope);
+ }
+ }
+ }
+
+ void LegalizeProducerStorage() {
+ for (auto& kv : consumer_storage_scopes_) {
+ const ExprNode* producer = kv.first;
+ std::string legal_scope = GetConsumerScope(kv.second);
+ if (storage_scope_.count(producer)) {
+ ICHECK(!HasMixedStorageOutputs(producer))
+ << "Mixed output storage scopes are not currently supported";
+ if (storage_scope_[producer][0].find(legal_scope) == std::string::npos) {
+ for (size_t i = 0; i < storage_scope_[producer].size(); i++) {
+ // Only support uniform storage scope across all outputs for now
+ storage_scope_[producer][i] = legal_scope;
+ }
+ }
+ }
+ }
+ }
+
+ bool DeviceSupportsTextureStorage(const Expr& expr) {
+ auto vd = GetVirtualDevice(expr);
+ if (vd != VirtualDevice::FullyUnconstrained()) {
+ if (Optional<String> t_device = vd->target->GetAttr<String>("device")) {
+ if (vd->target->kind->device_type == kDLOpenCL && t_device.defined()) {
+ if (t_device.value() == "adreno") {
Review Comment:
Just an idea. Probably we could add a method to `t_device` which will report if the device support textures or not?
##########
tests/python/relay/test_conv2d_nchw_texture.py:
##########
@@ -435,3 +435,641 @@ def test_conv2d_vgg16_winograd_4d():
graph = build_run_compare(mod, params1, {"data": input_shape}, dtype, target)
matches = re.findall("winograd", graph)
assert len(matches) > 0
+
+
+@tvm.testing.requires_opencl
+def test_2conv2d():
+ target = "opencl --device=adreno"
+ dtype = "float16"
+
+ input_shape = (1, 32, 40, 40)
+ filter_shape1 = (96, 32, 2, 2)
+ filter_shape2 = (32, 96, 2, 2)
+ bias_shape1 = (1, 96, 1, 1)
+ bias_shape2 = (1, 32, 1, 1)
+ A = relay.var("data", shape=input_shape, dtype=dtype)
+ W1 = relay.var("weight1", shape=filter_shape1, dtype=dtype)
+ B1 = relay.var("bias1", shape=bias_shape1, dtype=dtype)
+ W2 = relay.var("weight2", shape=filter_shape2, dtype=dtype)
+ B2 = relay.var("bias2", shape=bias_shape2, dtype=dtype)
+
+ # C = relay.nn.relu(A)
+ conv1 = relay.nn.conv2d(
+ A,
+ W1,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ padding=[0, 0, 0, 0],
+ strides=[2, 2],
+ out_dtype=dtype,
+ channels=96,
+ kernel_size=(2, 2),
+ )
+ D = relay.op.add(conv1, B1)
+ D = relay.op.nn.relu(D)
+
+ conv2 = relay.nn.conv2d(
+ D,
+ W2,
+ data_layout="NCHW",
+ kernel_layout="OIHW",
+ padding=[0, 0, 0, 0],
+ strides=[2, 2],
+ out_dtype=dtype,
+ channels=32,
+ kernel_size=(2, 2),
+ )
+ D = relay.op.add(conv2, B2)
+ D = relay.op.nn.relu(D)
+
+ mod = relay.Function([A, W1, B1, W2, B2], D)
+ np.random.seed(0)
+ initializer = relay.testing.init.Xavier()
+ filter_data1 = np.zeros(filter_shape1).astype(dtype)
+ bias_data1 = np.zeros(bias_shape1).astype(dtype)
+ initializer("weight", filter_data1)
+ initializer("bias", bias_data1)
+ filter_data2 = np.zeros(filter_shape2).astype(dtype)
+ bias_data2 = np.zeros(bias_shape2).astype(dtype)
+ initializer("weight", filter_data2)
+ initializer("bias", bias_data2)
+ params1 = {
+ "weight1": tvm.nd.array(filter_data1),
+ "bias1": tvm.nd.array(bias_data1),
+ "weight2": tvm.nd.array(filter_data2),
+ "bias2": tvm.nd.array(bias_data2),
+ }
+
+ static_memory_scope = [
+ "",
+ "global",
+ "global.texture-weight",
+ "global.texture-weight",
+ "global.texture-nhwc",
+ "global.texture-weight",
+ "global.texture-weight",
+ "",
+ "",
+ ]
+
+ build_run_compare(mod, params1, {"data": input_shape}, dtype, target, static_memory_scope)
+
+
+@tvm.testing.requires_opencl
+def test_residual_block():
+ target = "opencl --device=adreno"
+ dtype = "float16"
+
+ input_shape = (1, 32, 40, 40)
+ filter_shape1 = (32, 32, 2, 2)
+ filter_shape2 = (32, 32, 1, 1)
+ filter_shape3 = (32, 32, 2, 2)
+ bias_shape1 = (1, 32, 1, 1)
+ # bias_shape2 = (1, 32, 1, 1)
Review Comment:
Please, remove commented code
##########
src/relay/transforms/annotate_texture_storage.cc:
##########
@@ -0,0 +1,528 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements. See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership. The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied. See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+/*!
+ * \file annotate_texture_storage.cc
+ * \brief Collection of target specific relay passes which
+ * storage scope related information.
+ *
+ * - CollectStorageInfo returns a mapping from relay expr
+ * to a list of output storage scopes for each output.
+ * These scopes are used during memory planning as well
+ * as downstream when doing codegen and in the graph runtime when doing runtime dataspace
+ * allocations.
+ *
+ * - AnnotateMemoryScope calls *target.CollectStorageInfo for all target been represented
+ * in the graph and rewrites graph modifying or inserting of VirtualDevice with required
+ * memory_scop collected from the CollectStorageInfo
+ */
+
+#include <tvm/relay/attrs/nn.h>
+#include <tvm/relay/expr.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/transform.h>
+#include <tvm/tir/expr.h>
+
+#include <memory>
+#include <unordered_map>
+
+#include "../transforms/device_aware_visitors.h"
+
+namespace tvm {
+namespace relay {
+namespace {
+
+/**
+ * @brief Analyzes the graph and returns mapping of expressions vs desired memory scope
+ */
+class StorageInfo : private transform::DeviceAwareExprVisitor {
+ public:
+ StorageInfo() : transform::DeviceAwareExprVisitor(Optional<IRModule>()) {}
+
+ static Map<Expr, Array<String>> GetStorageMap(const Expr& expr) {
+ StorageInfo storage_info;
+ storage_info.VisitExpr(expr);
+ storage_info.LegalizeProducerStorage();
+ Map<Expr, Array<String>> storage_map;
+ for (auto& kv : storage_info.storage_scope_) {
+ std::vector<String> storage_scopes;
+ std::copy(kv.second.begin(), kv.second.end(), std::back_inserter(storage_scopes));
+ storage_map.Set(GetRef<Expr>(kv.first), Array<String>{storage_scopes});
+ }
+
+ // Filling the input arguments by "global" scope to handle PlanDevice algo which propagates
+ // virtual devices from outputs to inputs. At the same time outputs must be unconstrained
+ // to avoid useless device_copy
+ for (const auto& cs : storage_info.consumer_storage_scopes_) {
+ // we have record in consumers that mean that potentially consumer
+ // dealt with textures anyhow, it's safe to mark this expr as global scope
+ // even without verification of the consumer's outputs scope
+ if (storage_info.CanConsumeTextures(cs.second) &&
+ storage_map.find(GetRef<Expr>(cs.first)) == storage_map.end()) {
+ storage_map.Set(GetRef<Expr>(cs.first), Array<String>{"global"});
+ }
+ }
+
+ // initial algo assumes mapping of outputs of the expr that is not enough, need to update
+ // VirtualDevice for function variables to get proper codegen. Adding vars to storage_map
+ for (const auto& a : storage_info.args_to_vars_) {
+ if (storage_map.count(a.first)) {
+ for (const auto& v : a.second) {
+ storage_map.Set(v, storage_map[a.first]);
+ }
+ }
+ }
+ return storage_map;
+ }
+
+ private:
+ void Visit(const Expr& expr) {
+ // Pre-order traversal to enable upward propagation
+ // of consumer storage scopes to producers when desirable.
+ if (const auto* fn = expr.as<FunctionNode>()) {
+ this->VisitExpr(fn->body);
+ for (const auto& param : fn->params) {
+ this->VisitExpr(param);
+ }
+ } else {
+ this->VisitExpr(expr);
+ }
+ }
+
+ void VisitExpr_(const VarNode* vn) final { ApplyConsumerScopeToInputs(vn); }
+
+ void VisitExpr_(const ConstantNode* cn) final { ApplyConsumerScopeToInputs(cn); }
+
+ void DeviceAwareVisitExpr_(const CallNode* call) final {
+ // Check the contents of this primitive function
+ if (DeviceSupportsTextureStorage(GetRef<Expr>(call))) {
+ if (const auto* fn = call->op.as<FunctionNode>()) {
+ if (fn->HasNonzeroAttr(attr::kPrimitive)) {
+ primitive_supports_texture_ = false;
+ Visit(call->op);
+ if (primitive_supports_texture_) {
+ if (call->checked_type().as<TensorTypeNode>()) {
+ std::string scope = "global.texture";
+ if (const auto* ttype = call->checked_type().as<TensorTypeNode>()) {
+ if (ttype->shape.size() == 5) {
+ scope = Scope(ttype->shape, GetVirtualDevice(GetRef<Expr>(call)));
+ }
+ }
+ storage_scope_[call].push_back(scope);
+ } else {
+ const auto* tuple_type = call->type_as<TupleTypeNode>();
+ ICHECK(tuple_type);
+ // TODO(csullivan): Add support for mixed output storage scope.
+ // In current adreno storage planner all outputs of a
+ // primitive function are assumed to be of the same storage
+ // type. This should be easy to extend in the future.
+ for (size_t i = 0; i < tuple_type->fields.size(); i++) {
+ storage_scope_[call].push_back("global.texture");
+ }
+ }
+ for (size_t i = 0; i < fn->params.size(); i++) {
+ args_to_vars_[call->args[i]].push_back(fn->params[i]);
+ }
+ }
+ // Add consumer storage scope information for call arguments
+ for (auto& arg : call->args) {
+ if (storage_scope_.count(call)) {
+ ICHECK(!HasMixedStorageOutputs(call))
+ << "Mixed output storage scopes are not currently supported";
+ consumer_storage_scopes_[arg.operator->()].push_back("global.texture");
+ } else {
+ consumer_storage_scopes_[arg.operator->()].push_back("global");
+ }
+ }
+ }
+ }
+ }
+
+ primitive_supports_texture_ = SupportsTextureStorage(call);
+
+ for (auto& arg : call->args) {
+ Visit(arg);
+ }
+ // We have all callees filled into storage_scope_ if they support textures
+ // We need to verify if this call expects texture and if it does not, remove from
+ // storage_scope_ since initially storage_scope_ is filled only based on knowledge
+ // that function able to work with textures, but not necessary that this texture is
+ // expected by function callee
+ for (auto& arg : call->args) {
+ if (consumer_storage_scopes_.count(arg.operator->()) &&
+ GetConsumerScope(consumer_storage_scopes_[arg.operator->()]) != "global.texture") {
+ storage_scope_.erase(arg.operator->());
+ if (const auto* cn = arg.as<CallNode>()) {
+ if (const auto* fn = cn->op.as<FunctionNode>()) {
+ storage_scope_.erase(fn->body.operator->());
+ }
+ }
+ }
+ }
+ }
+
+ std::string Scope(Array<PrimExpr> shape, const VirtualDevice& vd) {
+ if (vd != VirtualDevice::FullyUnconstrained()) {
+ std::map<int, std::string> diffs;
+ int limit =
+ vd->target->GetAttr<Integer>("texture_spatial_limit").value_or(Integer(16384))->value;
+ int a0 = shape[0].as<IntImmNode>()->value;
+ int a1 = shape[1].as<IntImmNode>()->value;
+ int a2 = shape[2].as<IntImmNode>()->value;
+ int a3 = shape[3].as<IntImmNode>()->value;
+
+ int d3l = a0 * a1 * a2;
+ int d3r = a3;
+ int diff3 = d3l > d3r ? d3l - d3r : d3r - d3l;
+ if (d3l < limit && d3r < limit) diffs[diff3] = "";
+
+ int d2l = a0 * a1;
+ int d2r = a2 * a3;
+ int diff2 = d2l > d2r ? d2l - d2r : d2r - d2l;
+ if (d2l < limit && d2r < limit) diffs[diff2] = "nhwc";
+
+ int d1l = a0;
+ int d1r = a1 * a2 * a3;
+ int diff1 = d1l > d1r ? d1l - d1r : d1r - d1l;
+ if (d1l < limit && d1r < limit) diffs[diff1] = "weight";
+ if (!diffs.empty()) {
+ std::string scope = "global.texture";
+ if (!diffs.begin()->second.empty()) {
+ scope += ("-" + diffs.begin()->second);
+ }
+ return scope;
+ }
+ }
+ return "global";
+ }
+
+ void ApplyConsumerScopeToInputs(const ExprNode* expr) {
+ std::string scope;
+ auto consumer_scopes_it = consumer_storage_scopes_.find(expr);
+ if (consumer_scopes_it != consumer_storage_scopes_.end()) {
+ std::string consumer_scope = GetConsumerScope(consumer_scopes_it->second);
+ ICHECK(!storage_scope_.count(expr))
+ << "Already propagated consumer scopes to input: " << GetRef<Expr>(expr);
+
+ bool expr_is_rgba_vectorizable = false;
+ if (const auto* ttype = expr->checked_type().as<TensorTypeNode>()) {
+ if (ttype->shape.size() == 5) {
+ scope = Scope(ttype->shape, GetVirtualDevice(GetRef<Expr>(expr)));
+ if (scope != "global") {
+ auto inner_dim = ttype->shape.back().as<IntImmNode>();
+ if (inner_dim && inner_dim->value == 4) {
+ expr_is_rgba_vectorizable = true;
+ }
+ }
+ }
+ }
+
+ // Only propagate texture scope from consumers to input expr if
+ // the input shape of the input expr is rgba vectorizable.
+ if (consumer_scope.find("global.texture") != std::string::npos) {
+ if (expr_is_rgba_vectorizable) {
+ storage_scope_[expr].push_back(scope);
+ }
+ } else {
+ storage_scope_[expr].push_back(consumer_scope);
+ }
+ }
+ }
+
+ void LegalizeProducerStorage() {
+ for (auto& kv : consumer_storage_scopes_) {
+ const ExprNode* producer = kv.first;
+ std::string legal_scope = GetConsumerScope(kv.second);
+ if (storage_scope_.count(producer)) {
+ ICHECK(!HasMixedStorageOutputs(producer))
+ << "Mixed output storage scopes are not currently supported";
+ if (storage_scope_[producer][0].find(legal_scope) == std::string::npos) {
+ for (size_t i = 0; i < storage_scope_[producer].size(); i++) {
+ // Only support uniform storage scope across all outputs for now
+ storage_scope_[producer][i] = legal_scope;
+ }
+ }
+ }
+ }
+ }
+
+ bool DeviceSupportsTextureStorage(const Expr& expr) {
+ auto vd = GetVirtualDevice(expr);
+ if (vd != VirtualDevice::FullyUnconstrained()) {
+ if (Optional<String> t_device = vd->target->GetAttr<String>("device")) {
+ if (vd->target->kind->device_type == kDLOpenCL && t_device.defined()) {
+ if (t_device.value() == "adreno") {
+ return true;
+ }
+ }
+ }
+ }
+ return false;
+ }
+
+ std::string GetConsumerScope(const std::vector<std::string>& consumer_scopes) const {
+ if (!consumer_scopes.size()) {
+ return "global";
+ }
+ std::string texture_tag = "global.texture";
+ for (auto& consumer_scope : consumer_scopes) {
+ if (consumer_scope.find(texture_tag) == std::string::npos) {
+ return "global";
+ }
+ }
+ return texture_tag;
+ }
+
+ bool CanConsumeTextures(const std::vector<std::string>& consumer_scopes) const {
+ if (!consumer_scopes.size()) {
+ return false;
+ }
Review Comment:
We can remove it? Because anyway we won't go to the loop and the `false` will be returned. Same comment for `GetConsumerScope`.
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