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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/09/23 17:49:45 UTC
[GitHub] [tvm] CircleSpin opened a new pull request #9095: Dynamic squeeze
CircleSpin opened a new pull request #9095:
URL: https://github.com/apache/tvm/pull/9095
@AndrewZhaoLuo @anwang2009 @mbrookhart
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[GitHub] [tvm] mbrookhart merged pull request #9095: [ONNX] [Relay] Dynamic squeeze
Posted by GitBox <gi...@apache.org>.
mbrookhart merged pull request #9095:
URL: https://github.com/apache/tvm/pull/9095
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[GitHub] [tvm] AndrewZhaoLuo commented on a change in pull request #9095: Dynamic squeeze
Posted by GitBox <gi...@apache.org>.
AndrewZhaoLuo commented on a change in pull request #9095:
URL: https://github.com/apache/tvm/pull/9095#discussion_r715035739
##########
File path: python/tvm/relay/op/dyn/_transform.py
##########
@@ -258,3 +259,24 @@ def _sparse_to_dense_shape_func(output_shape, ndim):
@_reg.register_shape_func("dyn.sparse_to_dense", True)
def sparse_to_dense_shape_func(attrs, inputs, out_ndims):
return [_sparse_to_dense_shape_func(inputs[3], out_ndims[0])]
+
+
+@script
+def _squeeze_shape_func_input_data(data, axis, ndims):
+ out = output_tensor((ndims,), "int64")
+ out_i = 0
+ for i in const_range(data.shape[0]):
+ not_in_axis = True
+ for j in const_range(axis.shape[0]):
+ if i == axis[j]:
+ not_in_axis = False
+ if not_in_axis:
+ out[out_i] = int64(data[i])
+ out_i += 1
+
+ return out
+
+
+@_reg.register_shape_func("dyn.squeeze", [False, True])
+def dynamic_squeeze_shape_func(attrs, inputs, out_ndims):
+ return [_squeeze_shape_func_input_data(inputs[0], inputs[1], out_ndims[0])]
Review comment:
nit: newline at end of file
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -1482,6 +1482,20 @@ def _impl_v12(cls, inputs, attr, params):
result = _op.expand_dims(result, axis)
return result
+class Squeeze(OnnxOpConverter):
+ """Operator converter for Squeeze."""
+
+ @classmethod
+ def _impl_v1(cls, inputs, attr, params):
+ return _op.squeeze(*inputs, axis=attr["axes"])
Review comment:
You must handle the case where "axes" is not given: https://github.com/onnx/onnx/blob/master/docs/Changelog.md#Squeeze-1
##########
File path: src/relay/op/dyn/tensor/transform.cc
##########
@@ -692,6 +692,66 @@ RELAY_REGISTER_OP("dyn.expand_dims")
.set_attr<FTVMCompute>("FTVMCompute", ExpandDimsCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
-} // namespace dyn
-} // namespace relay
-} // namespace tvm
+bool DynSqueezeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 3);
Review comment:
nit: add a comment about each of the types
e.g. [input tensor, axes tensor, output tensor]
##########
File path: src/relay/op/dyn/tensor/transform.cc
##########
@@ -692,6 +692,66 @@ RELAY_REGISTER_OP("dyn.expand_dims")
.set_attr<FTVMCompute>("FTVMCompute", ExpandDimsCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
-} // namespace dyn
-} // namespace relay
-} // namespace tvm
+bool DynSqueezeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 3);
+ const auto* data = types[0].as<TensorTypeNode>();
+ if (data == nullptr) {
+ return false;
+ }
+ const auto* axes = types[1].as<TensorTypeNode>();
+ if (axes == nullptr) {
+ return false;
+ }
+ ICHECK_EQ(axes->shape.size(), 1);
+ ICHECK(axes->shape[0].as<IntImmNode>());
+ size_t output_rank = data->shape.size() - axes->shape[0].as<IntImmNode>()->value;
+ std::vector<IndexExpr> result_shape(output_rank, Any());
+ reporter->Assign(types[2], TensorType(result_shape, data->dtype));
+ return true;
+}
+
+
+Array<te::Tensor> SqueezeCompute(const Attrs& attrs, const Array<te::Tensor>& inputs,
+ const Type& out_type) {
+ const auto* out_ttype = out_type.as<TensorTypeNode>();
+ ICHECK(out_ttype != nullptr);
+ Array<IndexExpr> newshape;
+ for (auto val : out_ttype->shape) {
+ if (val->IsInstance<tir::AnyNode>()) {
+ newshape.push_back(val.as<tir::AnyNode>()->ToVar());
+ } else {
+ newshape.push_back(val);
+ }
+ }
+ return {topi::reshape(inputs[0], newshape)};
+}
+
+Expr MakeDynSqueeze(Expr data, Expr axes) {
+ auto attrs = make_object<SqueezeAttrs>();
+ static const Op& op = Op::Get("dyn.squeeze");
+ return Call(op, {data, axes}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.dyn._make.squeeze").set_body_typed(MakeDynSqueeze);
+
+RELAY_REGISTER_OP("dyn.squeeze")
+ .describe(R"code(Squeeze the input tensor at the dimensions given by axes
+
+- **data**: The input data to the operator.
+- **axes**: The axes to squeeze.
+
+)code" TVM_ADD_FILELINE)
+ .set_num_inputs(2)
+ .set_attrs_type<SqueezeAttrs>()
+ .add_argument("data", "Tensor", "The input tensor.")
+ .add_argument("axes", "Tensor", "The axes to squeeze.")
+ .set_support_level(3)
+ .add_type_rel("DynSqueeze", DynSqueezeRel)
+ .set_attr<FTVMCompute>("FTVMCompute", SqueezeCompute)
+ .set_attr<TOpPattern>("TOpPattern", kInjective)
+ .set_attr<TReshapeOp>("TReshapeOp", true);
+
+}
Review comment:
Cap off namespaces
e.g.
} // namespace dyn
} // namespace ...
##########
File path: src/relay/op/dyn/tensor/transform.cc
##########
@@ -692,6 +692,66 @@ RELAY_REGISTER_OP("dyn.expand_dims")
.set_attr<FTVMCompute>("FTVMCompute", ExpandDimsCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
-} // namespace dyn
-} // namespace relay
-} // namespace tvm
+bool DynSqueezeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 3);
+ const auto* data = types[0].as<TensorTypeNode>();
+ if (data == nullptr) {
+ return false;
+ }
+ const auto* axes = types[1].as<TensorTypeNode>();
+ if (axes == nullptr) {
+ return false;
+ }
+ ICHECK_EQ(axes->shape.size(), 1);
Review comment:
nit: add error messages to these ICHECKs
e.g. ICHECK_EQ(...) << "Error message here got " << variable << " expected 1".
##########
File path: src/relay/op/dyn/tensor/transform.cc
##########
@@ -692,6 +692,66 @@ RELAY_REGISTER_OP("dyn.expand_dims")
.set_attr<FTVMCompute>("FTVMCompute", ExpandDimsCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
-} // namespace dyn
-} // namespace relay
-} // namespace tvm
+bool DynSqueezeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 3);
+ const auto* data = types[0].as<TensorTypeNode>();
+ if (data == nullptr) {
+ return false;
+ }
+ const auto* axes = types[1].as<TensorTypeNode>();
+ if (axes == nullptr) {
+ return false;
+ }
+ ICHECK_EQ(axes->shape.size(), 1);
+ ICHECK(axes->shape[0].as<IntImmNode>());
+ size_t output_rank = data->shape.size() - axes->shape[0].as<IntImmNode>()->value;
+ std::vector<IndexExpr> result_shape(output_rank, Any());
+ reporter->Assign(types[2], TensorType(result_shape, data->dtype));
+ return true;
+}
+
+
+Array<te::Tensor> SqueezeCompute(const Attrs& attrs, const Array<te::Tensor>& inputs,
+ const Type& out_type) {
+ const auto* out_ttype = out_type.as<TensorTypeNode>();
+ ICHECK(out_ttype != nullptr);
+ Array<IndexExpr> newshape;
+ for (auto val : out_ttype->shape) {
+ if (val->IsInstance<tir::AnyNode>()) {
+ newshape.push_back(val.as<tir::AnyNode>()->ToVar());
Review comment:
Do we need these IF checks? They are always going to be AnyNodes() right?
##########
File path: src/relay/op/dyn/tensor/transform.cc
##########
@@ -692,6 +692,66 @@ RELAY_REGISTER_OP("dyn.expand_dims")
.set_attr<FTVMCompute>("FTVMCompute", ExpandDimsCompute)
.set_attr<TOpPattern>("TOpPattern", kInjective);
-} // namespace dyn
-} // namespace relay
-} // namespace tvm
+bool DynSqueezeRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
+ const TypeReporter& reporter) {
+ ICHECK_EQ(types.size(), 3);
+ const auto* data = types[0].as<TensorTypeNode>();
+ if (data == nullptr) {
+ return false;
+ }
+ const auto* axes = types[1].as<TensorTypeNode>();
+ if (axes == nullptr) {
+ return false;
+ }
+ ICHECK_EQ(axes->shape.size(), 1);
+ ICHECK(axes->shape[0].as<IntImmNode>());
+ size_t output_rank = data->shape.size() - axes->shape[0].as<IntImmNode>()->value;
+ std::vector<IndexExpr> result_shape(output_rank, Any());
+ reporter->Assign(types[2], TensorType(result_shape, data->dtype));
+ return true;
+}
+
+
+Array<te::Tensor> SqueezeCompute(const Attrs& attrs, const Array<te::Tensor>& inputs,
+ const Type& out_type) {
+ const auto* out_ttype = out_type.as<TensorTypeNode>();
+ ICHECK(out_ttype != nullptr);
+ Array<IndexExpr> newshape;
+ for (auto val : out_ttype->shape) {
+ if (val->IsInstance<tir::AnyNode>()) {
+ newshape.push_back(val.as<tir::AnyNode>()->ToVar());
+ } else {
+ newshape.push_back(val);
+ }
+ }
+ return {topi::reshape(inputs[0], newshape)};
+}
+
+Expr MakeDynSqueeze(Expr data, Expr axes) {
+ auto attrs = make_object<SqueezeAttrs>();
+ static const Op& op = Op::Get("dyn.squeeze");
+ return Call(op, {data, axes}, Attrs(attrs), {});
+}
+
+TVM_REGISTER_GLOBAL("relay.op.dyn._make.squeeze").set_body_typed(MakeDynSqueeze);
+
+RELAY_REGISTER_OP("dyn.squeeze")
+ .describe(R"code(Squeeze the input tensor at the dimensions given by axes
Review comment:
nit: might want to define what it means to "squeeze". E.g. remove axes (with dimension value 1)
##########
File path: tests/python/relay/dyn/test_dynamic_op_level3.py
##########
@@ -92,6 +92,25 @@ def verify_reshape(shape, newshape, oshape):
verify_reshape((4, 7), (2, 7, 2), (2, 7, 2))
+def test_squeeze():
+ def verify_squeeze(shape, dtype, axis):
+ x = relay.var("x", relay.TensorType(shape, dtype))
+ assert axis is not None
+ np_axis = tuple(axis)
+ axis = relay.var("axis", relay.TensorType([len(axis)], "int64"))
+ squeeze = relay.squeeze(x, axis=axis)
+
+ print(squeeze)
Review comment:
Remove prints
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[GitHub] [tvm] mbrookhart commented on a change in pull request #9095: Dynamic squeeze
Posted by GitBox <gi...@apache.org>.
mbrookhart commented on a change in pull request #9095:
URL: https://github.com/apache/tvm/pull/9095#discussion_r715030721
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -2806,7 +2820,9 @@ def _impl_v12(cls, inputs, attr, params):
alpha = _op.const(attr.get("alpha", 1.0), dtype)
zero = _op.const(0, dtype)
one = _op.const(1, dtype)
- return _op.maximum(zero, x) + _op.minimum(zero, alpha * (_op.exp(x / alpha) - one))
+ out = _op.maximum(zero, x) + _op.minimum(zero, alpha * (_op.exp(x / alpha) - one))
+ print(out)
Review comment:
remove the print
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[GitHub] [tvm] mbrookhart merged pull request #9095: [ONNX] [Relay] Dynamic squeeze
Posted by GitBox <gi...@apache.org>.
mbrookhart merged pull request #9095:
URL: https://github.com/apache/tvm/pull/9095
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[GitHub] [tvm] AndrewZhaoLuo commented on pull request #9095: Dynamic squeeze
Posted by GitBox <gi...@apache.org>.
AndrewZhaoLuo commented on pull request #9095:
URL: https://github.com/apache/tvm/pull/9095#issuecomment-926044265
Please also change the title of this PR to something like "[ONNX] [Relay] Dynamic Squeeze"
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