ashutosh-arm commented on code in PR #12353: URL: https://github.com/apache/tvm/pull/12353#discussion_r944012860
########## tests/python/contrib/test_cmsisnn/test_fuse_pads.py: ########## @@ -0,0 +1,279 @@ +# 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: fuse_pads pass""" +import numpy as np +import pytest +import tvm +import tvm.testing +from tvm import relay + +tvm._ffi._init_api("relay.ext.cmsisnn.transform", __name__) + + +class CheckForPadsWithinCompositeFunc(tvm.relay.ExprVisitor): + """Provides method to test number of pads present inside the function being visited.""" + + def __init__(self): + super().__init__() + self.num_pads_ = 0 + + def visit_call(self, call): + super().visit_call(call) + if ( + isinstance(call, tvm.relay.Call) + and isinstance(call.op, tvm.ir.op.Op) + and call.op.name == "nn.pad" + ): + self.num_pads_ += 1 + + def check_num_pads(self): + assert self.num_pads_ == 0, "CMSIS-NN composite function should not have pads" + + +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 set_composite_func_attr(func, name): + func = func.with_attr("Composite", name) + return func + + [email protected]( + "ifm_shape, pad_width, conv2d_padding, ofm_shape", + [ + [(1, 25, 25, 12), ((0, 0), (0, 2), (1, 2), (0, 0)), (1, 1, 1, 1), (1, 26, 28, 2)], + [(1, 64, 100, 4), ((0, 0), (1, 3), (1, 1), (0, 0)), (0, 0, 0, 0), (1, 64, 100, 2)], + [(1, 55, 55, 3), ((0, 0), (2, 1), (3, 5), (0, 0)), (0, 0, 1, 1), (1, 57, 59, 2)], + ], +) +def test_invalid_padding_for_fusion(ifm_shape, pad_width, conv2d_padding, ofm_shape): + """Negative tests for pads preceding Conv2D that cannot be fused.""" + dtype = "int8" + kernel_size = (3, 3) + ofm_channels = 2 + local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype) + pad = relay.nn.pad( + local_input, + pad_width=pad_width, # ((), (top, bottom), (left, right), ()) + pad_value=10, + pad_mode="constant", + ) + rng = np.random.default_rng(12321) + local_weight = tvm.nd.array( + rng.integers( + np.iinfo(dtype).min, + high=np.iinfo(dtype).max, + size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]), + dtype=dtype, + ) + ) + local_weight = relay.const(local_weight, dtype) + conv2d = relay.qnn.op.conv2d( + pad, + local_weight, + relay.const(1, "int32"), + relay.const(1, "int32"), + relay.const(1, "float32"), + relay.const(1, "float32"), + data_layout="NHWC", + kernel_layout="OHWI", + channels=ofm_channels, + kernel_size=(3, 3), + padding=conv2d_padding, + out_dtype="int32", + ) + requantize = relay.qnn.op.requantize( + conv2d, + relay.const(1, "float32"), + relay.const(1, "int32"), + relay.const(1, "float32"), + relay.const(1, "int32"), + axis=0, + out_dtype=dtype, + ) + local_func = relay.Function(relay.analysis.free_vars(requantize), requantize) + local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_conv2d") + + mod = tvm.IRModule() + ext_input = relay.var("ext_input", shape=ifm_shape, dtype=dtype) + call_local_func = relay.Call(local_func, [ext_input]) + extern_func = relay.Function(relay.analysis.free_vars(call_local_func), call_local_func) + extern_var = relay.GlobalVar("external_function") + extern_func = set_external_func_attr(extern_func, "cmsis-nn", extern_var.name_hint) + mod[extern_var] = extern_func + + main_input = relay.var("main_input", shape=ifm_shape, dtype=dtype) + call_extern_func = relay.Call(extern_var, [main_input]) + main_func = relay.Function([main_input], call_extern_func, relay.TensorType(ofm_shape, dtype)) + main_var = relay.GlobalVar("main") + mod[main_var] = main_func + + mod = relay.transform.InferType()(mod) + + error_regex = r"Difference on each side of a dimension should be either 0 or 1" + + with pytest.raises(tvm.TVMError, match=error_regex): + mod = CMSISNNFusePads()(mod) + + [email protected]( + "ifm_shape, pad_width, conv2d_padding, ofm_shape", + [ + [(1, 25, 25, 12), ((0, 0), (0, 1), (1, 2), (0, 0)), (1, 1, 1, 1), (1, 26, 28, 2)], + [(1, 64, 100, 4), ((0, 0), (1, 1), (1, 1), (0, 0)), (0, 0, 0, 0), (1, 64, 100, 2)], + [(1, 55, 55, 3), ((0, 0), (2, 1), (3, 2), (0, 0)), (0, 0, 1, 1), (1, 57, 59, 2)], + ], +) +def test_pad_conv2d_fusion(ifm_shape, pad_width, conv2d_padding, ofm_shape): + """Tests the pads and conv2d fusion.""" + dtype = "int8" + kernel_size = (3, 3) + ofm_channels = 2 + local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype) + pad = relay.nn.pad( + local_input, + pad_width=pad_width, # ((), (top, bottom), (left, right), ()) + pad_value=10, + pad_mode="constant", + ) + rng = np.random.default_rng(12321) + local_weight = tvm.nd.array( + rng.integers( + np.iinfo(dtype).min, + high=np.iinfo(dtype).max, + size=(ofm_channels, kernel_size[0], kernel_size[1], ifm_shape[3]), + dtype=dtype, + ) + ) + local_weight = relay.const(local_weight, dtype) + conv2d = relay.qnn.op.conv2d( + pad, + local_weight, + relay.const(1, "int32"), + relay.const(1, "int32"), + relay.const(1, "float32"), + relay.const(1, "float32"), + data_layout="NHWC", + kernel_layout="OHWI", + channels=ofm_channels, + kernel_size=(3, 3), + padding=conv2d_padding, + out_dtype="int32", + ) + requantize = relay.qnn.op.requantize( + conv2d, + relay.const(1, "float32"), + relay.const(1, "int32"), + relay.const(1, "float32"), + relay.const(1, "int32"), + axis=0, + out_dtype=dtype, + ) + local_func = relay.Function(relay.analysis.free_vars(requantize), requantize) + local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_conv2d") + + mod = tvm.IRModule() + ext_input = relay.var("ext_input", shape=ifm_shape, dtype=dtype) + call_local_func = relay.Call(local_func, [ext_input]) + extern_func = relay.Function(relay.analysis.free_vars(call_local_func), call_local_func) + extern_var = relay.GlobalVar("external_function") + extern_func = set_external_func_attr(extern_func, "cmsis-nn", extern_var.name_hint) + mod[extern_var] = extern_func + + main_input = relay.var("main_input", shape=ifm_shape, dtype=dtype) + call_extern_func = relay.Call(extern_var, [main_input]) + main_func = relay.Function([main_input], call_extern_func, relay.TensorType(ofm_shape, dtype)) + main_var = relay.GlobalVar("main") + mod[main_var] = main_func + + mod = relay.transform.InferType()(mod) + + mod = CMSISNNFusePads()(mod) + pad_verifier = CheckForPadsWithinCompositeFunc() + pad_verifier.visit_function(mod[extern_var]) + pad_verifier.check_num_pads() + + +def test_without_preceding_pad(): + """Tests the pass FusePads when padding is not present before qnn.conv2d.""" + dtype = "int8" + ifm_shape = (1, 56, 56, 64) + ofm_shape = (1, 56, 56, 64) + local_input = relay.var("local_input", shape=ifm_shape, dtype=dtype) + rng = np.random.default_rng(12321) + local_weight = tvm.nd.array( + rng.integers( + np.iinfo(dtype).min, + high=np.iinfo(dtype).max, + size=(64, 3, 3, 64), + dtype=dtype, + ) + ) + local_weight = relay.const(local_weight, dtype) + conv2d = relay.qnn.op.conv2d( + local_input, + local_weight, + relay.const(1, "int32"), + relay.const(1, "int32"), + relay.const(1, "float32"), + relay.const(1, "float32"), + data_layout="NHWC", + kernel_layout="OHWI", + channels=64, + kernel_size=(3, 3), + padding=(1, 1, 1, 1), + out_dtype="int32", + ) + requantize = relay.qnn.op.requantize( + conv2d, + relay.const(1, "float32"), + relay.const(1, "int32"), + relay.const(1, "float32"), + relay.const(1, "int32"), + axis=0, + out_dtype=dtype, + ) + relu = relay.nn.relu(requantize) + local_func = relay.Function(relay.analysis.free_vars(relu), relu) + local_func = set_composite_func_attr(local_func, "cmsis-nn.qnn_conv2d") + + mod = tvm.IRModule() + ext_input = relay.var("ext_input", shape=ifm_shape, dtype=dtype) + call_local_func = relay.Call(local_func, [ext_input]) + extern_func = relay.Function(relay.analysis.free_vars(call_local_func), call_local_func) + extern_var = relay.GlobalVar("external_function") + extern_func = set_external_func_attr(extern_func, "cmsis-nn", extern_var.name_hint) + mod[extern_var] = extern_func + + main_input = relay.var("main_input", shape=ifm_shape, dtype=dtype) + call_extern_func = relay.Call(extern_var, [main_input]) + main_func = relay.Function(relay.analysis.free_vars(call_extern_func), call_extern_func) + main_func = relay.Function([main_input], call_extern_func, relay.TensorType(ofm_shape, dtype)) + main_var = relay.GlobalVar("main") + mod[main_var] = main_func + + mod = relay.transform.InferType()(mod) + + mod = CMSISNNFusePads()(mod) + pad_verifier = CheckForPadsWithinCompositeFunc() + pad_verifier.visit_function(mod[extern_var]) + pad_verifier.check_num_pads() Review Comment: Done -- This is an automated message from the Apache Git Service. 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