ashutosh-arm commented on a change in pull request #9331: URL: https://github.com/apache/tvm/pull/9331#discussion_r746614175
########## File path: tests/python/contrib/test_cmsisnn/test_extract_constants.py ########## @@ -0,0 +1,175 @@ +# 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. + +"""CMSIS-NN integration tests: extract_constants pass""" +import itertools +import math +import numpy as np +import pytest +import tvm +from tvm import relay + +from utils import ( + make_module, + count_num_calls, + get_range_for_dtype_str, + get_same_padding, + get_conv2d_qnn_params, + make_qnn_relu, +) + +tvm._ffi._init_api("relay.ext.cmsisnn.transform", __name__) + + +class CheckFunctionsForConstants(tvm.relay.ExprVisitor): + def __init__(self): + super().__init__() + self.num_constants_ = 0 + + def visit_call(self, call): + super().visit_call(call) + for arg in call.args: + if isinstance(arg, relay.Constant) and arg.data.numpy().ndim > 0: + self.num_constants_ += 1 + + def visit_function(self, func): + super().visit_function(func) + assert self.num_constants_ == 0, "Functions should not have constant arguments in Calls" + + +def set_external_func_attr(func, compiler, ext_symbol): + func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1)) + func = func.with_attr("Compiler", compiler) + func = func.with_attr("global_symbol", ext_symbol) + return func + + +def test_external_function(): + y0_data = np.random.uniform(0, 1, (8, 8)).astype("float32") + x0 = relay.var("x0", shape=(8, 8)) + y0_const = relay.const(y0_data, "float32") + z0 = x0 + y0_const + ef = relay.Function([x0], z0, relay.TensorType((8, 8), "float32")) + ev = relay.GlobalVar("external_function") + ef = set_external_func_attr(ef, "external_compiler", ev.name_hint) + + x = relay.var("x", shape=(8, 8)) + c = relay.Call(ev, [x]) + mf = relay.Function([x], c, relay.TensorType((8, 8), "float32")) + mv = relay.GlobalVar("main") + + mod = tvm.IRModule() + mod[ev] = ef + mod[mv] = mf + + mod = ExtractConstantsFromPartitionedFunction()(mod) + CheckFunctionsForConstants().visit_function(mod[ev]) + relay.transform.InferType()(mod) + + +def test_nested_function(): + y1_data = np.random.uniform(0, 1, (8, 8)).astype("float32") + x1 = relay.var("x1", shape=(8, 8)) + y1_const = relay.const(y1_data, "float32") + z1 = x1 + y1_const + w1 = z1 * relay.const(5.0, "float32") + lf = relay.Function([x1], w1, relay.TensorType((8, 8), "float32")) + + x0 = relay.var("x0", shape=(8, 8)) + c0 = relay.Call(lf, [x0]) + ef = relay.Function([x0], c0, relay.TensorType((8, 8), "float32")) + + x = relay.var("x", shape=(8, 8)) + ev = relay.GlobalVar("external_function") + ef = set_external_func_attr(ef, "external_compiler", ev.name_hint) + c = relay.Call(ev, [x]) + mf = relay.Function([x], c, relay.TensorType((8, 8), "float32")) + mv = relay.GlobalVar("main") + + mod = tvm.IRModule() + mod[ev] = ef + mod[mv] = mf + + mod = ExtractConstantsFromPartitionedFunction()(mod) + CheckFunctionsForConstants().visit_function(mod[ev]) + relay.transform.InferType()(mod) + + +def test_multiple_functions(): + y20_data = np.random.uniform(0, 1, (8, 8)).astype("float32") + x20 = relay.var("x20", shape=(8, 8)) + y20_const = relay.const(y20_data, "float32") + z20 = x20 + y20_const + f20 = relay.Function([x20], z20, relay.TensorType((8, 8), "float32")) + + y21_data = np.random.uniform(0, 1, (8, 8)).astype("float32") + x21 = relay.var("x21", shape=(8, 8)) + y21_const = relay.const(y21_data, "float32") + z21 = x21 + y21_const + f21 = relay.Function([x21], z21, relay.TensorType((8, 8), "float32")) + + x10 = relay.var("x10", shape=(8, 8)) + c10 = relay.Call(f20, [x10]) + c11 = relay.Call(f21, [c10]) + ef = relay.Function([x10], c11, relay.TensorType((8, 8), "float32")) + + x0 = relay.var("x0", shape=(8, 8)) + ev = relay.GlobalVar("external_function") + ef = set_external_func_attr(ef, "external_compiler", ev.name_hint) + c = relay.Call(ev, [x0]) + mf = relay.Function([x0], c, relay.TensorType((8, 8), "float32")) + mv = relay.GlobalVar("main") + + mod = tvm.IRModule() + mod[ev] = ef + mod[mv] = mf + + mod = ExtractConstantsFromPartitionedFunction()(mod) + CheckFunctionsForConstants().visit_function(mod[ev]) + relay.transform.InferType()(mod) Review comment: some asserts are already part of the Visitor call above in the code: `assert self.num_constants_ == 0, "Functions should not have constant arguments in Calls"` Is that what you were expecting as well? -- This is an automated message from the Apache Git Service. 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