Merge branch 'main' of https://github.com/apache/tvm into vm_te_migra…

…tion

Jenkinsfile

@@ -45,12 +45,12 @@
 
 // NOTE: these lines are scanned by docker/dev_common.sh. Please update the regex as needed. -->
 ci_lint = "tlcpack/ci-lint:v0.66"
-ci_gpu = "tlcpack/ci-gpu:v0.75"
-ci_cpu = "tlcpack/ci-cpu:v0.74"
+ci_gpu = "tlcpack/ci-gpu:v0.76"
+ci_cpu = "tlcpack/ci-cpu:v0.75"
 ci_wasm = "tlcpack/ci-wasm:v0.71"
 ci_i386 = "tlcpack/ci-i386:v0.73"
-ci_qemu = "tlcpack/ci-qemu:v0.05"
-ci_arm = "tlcpack/ci-arm:v0.05"
+ci_qemu = "tlcpack/ci-qemu:v0.06"
+ci_arm = "tlcpack/ci-arm:v0.06"
 // <--- End of regex-scanned config.
 
 // tvm libraries

python/tvm/relay/frontend/onnx.py

@@ -585,6 +585,70 @@ def _impl_v1(cls, inputs, attr, params):
             out = _op.nn.bias_add(out, inputs[2])
         return out
 
+    @classmethod
+    def _impl_v11(cls, inputs, attr, params):
+        # get number of channels
+        out_type = infer_type(inputs[1])
+        out_shapes = [get_const_tuple(out_type.checked_type.shape)]
+        channels = out_shapes[0][1]
+        attr["channels"] = channels
+        groups = attr.get("group", 1)
+
+        if "kernel_shape" not in attr:
+            attr["kernel_shape"] = out_shapes[0][2:]
+
+        attr["groups"] = groups
+        # infer pads for auto_pad
+        data = inputs[0]
+        input_shape = infer_shape(data)
+        ndim = len(input_shape)
+        if "auto_pad" in attr:
+            attr["auto_pad"] = attr["auto_pad"].decode("utf-8")
+            if attr["auto_pad"] in ("SAME_UPPER", "SAME_LOWER"):
+                # Warning: Convolution does not yet support dynamic shapes,
+                # one will need to run dynamic_to_static on this model after import
+                kernel_shape = attr["kernel_shape"]
+                kndim = len(kernel_shape)
+                dilations = attr.get("dilations", [1] * kndim)
+                output_padding = attr.get("output_padding", [0] * kndim)
+                strides = attr["strides"]
+                total_pad = [0] * kndim
+                for i in range(kndim):
+                    total_pad[i] = (
+                        output_padding[i] + ((kernel_shape[i] - 1) * dilations[i] + 1) - strides[i]
+                    )
+                left = [p // 2 for p in total_pad]
+                right = [total_pad[i] - left[i] for i in range(kndim)]
+                if "LOWER" in attr["auto_pad"]:
+                    pad = left + right
+                else:
+                    pad = right + left
+                attr["pads"] = pad
+            elif attr["auto_pad"] == "VALID":
+                attr["pads"] = tuple([0 for i in range(ndim - 2)])
+            elif attr["auto_pad"] == "NOTSET":
+                pass
+            else:
+                msg = 'Value {} in attribute "auto_pad" of operator Conv is invalid.'
+                raise tvm.error.OpAttributeInvalid(msg.format(attr["auto_pad"]))
+            attr.pop("auto_pad")
+
+        out = AttrCvt(
+            op_name=dimension_picker("conv", "_transpose"),
+            transforms={
+                "kernel_shape": "kernel_size",
+                "dilations": ("dilation", 1),
+                "pads": ("padding", 0),
+                "group": ("groups", 1),
+            },
+            disables=["output_shape"],
+            custom_check=dimension_constraint(),
+        )([data, inputs[1]], attr, params)
+        use_bias = len(inputs) == 3
+        if use_bias:
+            out = _op.nn.bias_add(out, inputs[2])
+        return out
+
 
 class GlobalAveragePool(OnnxOpConverter):
     """Operator converter for GlobalAveragePool"""

src/relay/backend/vm/compiler.cc

@@ -255,7 +255,6 @@ class VMFunctionCompiler : ExprFunctor<void(const Expr& expr)> {
                      ExprDeviceMap expr_device_map)
       : last_register_(0),
         registers_num_(0),
-        // engine_(CompileEngine::Global()), //TODO: replace with TE 
         context_(context),
         target_host_(target_host),
         expr_device_map_(std::move(expr_device_map)) {
@@ -467,7 +466,7 @@ class VMFunctionCompiler : ExprFunctor<void(const Expr& expr)> {
   void EmitShapeFunc(Function func, Array<Expr> inputs, Array<Expr> outputs) {
     // Lower shape function
     CCacheKey key(func, target_host_);
-    auto cfunc = compiler_->LowerShapeFunc(key); //TODO: replace with TE 
+    auto cfunc = compiler_->LowerShapeFunc(key);
     int op_index = -1;
     // pick the only function inside the context
     ICHECK_EQ(cfunc->funcs->functions.size(), 1);
@@ -553,7 +552,7 @@ class VMFunctionCompiler : ExprFunctor<void(const Expr& expr)> {
 
     CCacheKey key(func, target);
     auto mangle_fn = [](String name) { return name; };
-    auto cfunc = compiler_->Lower(key, mangle_fn); //TODO: replace with TE 
+    auto cfunc = compiler_->Lower(key, mangle_fn);
 
     auto op_index = -1;
     if (func->GetAttr<String>(attr::kCompiler).defined()) {
@@ -1189,8 +1188,6 @@ void VMCompiler::Codegen() {
   TECompiler compiler;
   auto ext_mods = compiler->LowerExternalFunctions();
 
-  //targets = target
-
   runtime::Module lib;
   if (funcs.size() > 0) {
     lib = tvm::build(funcs, target_host_);

src/runtime/module.cc

@@ -113,7 +113,10 @@ const PackedFunc* ModuleNode::GetFuncFromEnv(const std::string& name) {
   if (pf == nullptr) {
     const PackedFunc* f = Registry::Get(name);
     ICHECK(f != nullptr) << "Cannot find function " << name
-                         << " in the imported modules or global registry";
+                         << " in the imported modules or global registry."
+                         << " If this involves ops from a contrib library like"
+                         << " cuDNN, ensure TVM was built with the relevant"
+                         << " library.";
     return f;
   } else {
     import_cache_.insert(std::make_pair(name, std::make_shared<PackedFunc>(pf)));