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[ShapeFunc] Handle weights in shape func #6912

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Nov 14, 2020
Merged

[ShapeFunc] Handle weights in shape func #6912

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[ShapeFunc] Handle weights in shape func
anijain2305 Nov 13, 2020
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anijain2305 Nov 14, 2020
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73 changes: 47 additions & 26 deletions src/relay/backend/compile_engine.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -420,38 +420,59 @@ class MakeShapeFunc : public backend::MemoizedExprTranslator<Array<te::Tensor>>
Array<te::Tensor> VisitExpr_(const ConstantNode* op) final {
using tir::make_const;
ICHECK(data_dependants_.size());
ICHECK(op->is_scalar());
bool data_dependant = data_dependants_.back();
if (data_dependant) {
void* data = op->data->data;
DataType dtype = DataType(op->data->dtype);
auto value = tvm::te::compute(
{},
[&](const Array<tvm::tir::Var>&) {
if (dtype == DataType::Int(32)) {
return make_const(dtype, static_cast<const int32_t*>(data)[0]);
} else if (dtype == DataType::Int(64)) {
return make_const(dtype, static_cast<const int64_t*>(data)[0]);
} else if (dtype == DataType::Float(32)) {
return make_const(dtype, static_cast<const float*>(data)[0]);
} else if (dtype == DataType::Float(64)) {
return make_const(dtype, static_cast<const double*>(data)[0]);
} else if (dtype == DataType::Bool()) {
return make_const(dtype, static_cast<const uint8_t*>(data)[0]);
} else {
LOG(FATAL) << "not handled";
return tvm::PrimExpr();
if (!op->is_scalar()) {
// This is a constant weight, extract the shape of the weight tensor.
// This can not be data dependent.
CHECK(!data_dependant);
auto ttype = op->checked_type().as<TensorTypeNode>();
int ndim = static_cast<int>(ttype->shape.size());
Array<PrimExpr> out_shape{ndim};
te::Tensor value = tvm::te::compute(
out_shape,
[&](const Array<tvm::tir::Var>& indices) {
auto idx = indices[0];
PrimExpr ret = make_const(DataType::Int(64), 0);
for (int i = 0; i < ndim; i++) {
ret = tvm::if_then_else(idx == i, ttype->shape[i], ret);
}
return ret;
},
"data_const", topi::kBroadcast);
scalars_.push_back(value);
return {value};
} else {
auto value = tvm::te::compute(
{}, [&](const Array<tvm::tir::Var>&) { return tir::make_const(DataType::Int(64), 0); },
"shape_const", topi::kBroadcast);
scalars_.push_back(value);
return {value};
} else {
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No need for else since you already return.

if (data_dependant) {
void* data = op->data->data;
DataType dtype = DataType(op->data->dtype);
auto value = tvm::te::compute(
{},
[&](const Array<tvm::tir::Var>&) {
if (dtype == DataType::Int(32)) {
return make_const(dtype, static_cast<const int32_t*>(data)[0]);
} else if (dtype == DataType::Int(64)) {
return make_const(dtype, static_cast<const int64_t*>(data)[0]);
} else if (dtype == DataType::Float(32)) {
return make_const(dtype, static_cast<const float*>(data)[0]);
} else if (dtype == DataType::Float(64)) {
return make_const(dtype, static_cast<const double*>(data)[0]);
} else if (dtype == DataType::Bool()) {
return make_const(dtype, static_cast<const uint8_t*>(data)[0]);
} else {
LOG(FATAL) << "not handled";
return tvm::PrimExpr();
}
},
"data_const", topi::kBroadcast);
scalars_.push_back(value);
return {value};
} else {
auto value = tvm::te::compute(
{}, [&](const Array<tvm::tir::Var>&) { return tir::make_const(DataType::Int(64), 0); },
"shape_const", topi::kBroadcast);
scalars_.push_back(value);
return {value};
}
}
}

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25 changes: 25 additions & 0 deletions tests/python/relay/test_vm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -770,5 +770,30 @@ def test_vm_reshape_tuple(x_shape=(1, 4, 2), y_shape=(1, 2, 10)):
tvm.testing.assert_allclose(res.asnumpy(), np.reshape(x_data, (1, -1)))


def test_constant_shape_with_external_codegen():
mod = tvm.IRModule()
shape = (relay.Any(), 25)
dtype = "float32"

# external function
x = relay.var("x", shape=shape, dtype=dtype)
weight = relay.const(np.random.rand(5, 25).astype("float32"), dtype="float32")
out = relay.nn.dense(x, weight)
f1 = relay.Function([x], out)
f1 = f1.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
f1 = f1.with_attr("Inline", tvm.tir.IntImm("int32", 1))
f1 = f1.with_attr("Compiler", "a")
glb_f1 = relay.GlobalVar("f1")
mod[glb_f1] = f1
mod = relay.transform.InferType()(mod)

# Main function
x = relay.var("x", shape=shape, dtype=dtype)
mod["main"] = relay.Function([x], glb_f1(x))
comp = relay.vm.VMCompiler()
opt_mod, _ = comp.optimize(mod, target="llvm")
assert "shape_func" in opt_mod.astext(False)


if __name__ == "__main__":
pytest.main([__file__])