ashutosh-arm commented on code in PR #13522: URL: https://github.com/apache/tvm/pull/13522#discussion_r1040776186
########## tests/python/relay/aot/test_crt_forward_declarations.py: ########## @@ -0,0 +1,325 @@ +# 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. + +"""test forward function declarations codegen by CodegenCHost.""" + +from collections import OrderedDict +import pytest +import numpy as np + +import tvm.testing +from tvm import relay +from tvm.contrib.download import download_testdata +from tvm.relay.op.contrib import cmsisnn +from tvm.testing.aot import AOTTestModel, compile_models, generate_ref_data +from tvm.micro.testing.aot_test_utils import ( + AOT_CORSTONE300_RUNNER, + AOT_USMP_CORSTONE300_RUNNER, + parametrize_aot_options, + AOTTestRunner, +) + + +def skip_if_no_reference_system(func): + return tvm.testing.skip_if_32bit(reason="Reference system unavailable in i386 container")(func) + + +def get_range_for_dtype_str(dtype): + """ + Produces the min,max for a give data type. + + Parameters + ---------- + dtype : str + a type string (e.g., int8) + + Returns + ------- + type_info.min : int + the minimum of the range + type_info.max : int + the maximum of the range + """ + + try: + type_info = np.iinfo(dtype) + except ValueError: + type_info = np.finfo(dtype) + return type_info.min, type_info.max + + +# pylint: disable=import-outside-toplevel +def _convert_to_relay( + tflite_model_buf, + input_data, + input_node, +): + """Converts TFLite model to Relay module and params""" + + def convert_to_list(x): + if not isinstance(x, list): + x = [x] + return x + + # TFLite.Model.Model has changed to TFLite.Model from 1.14 to 2.1 + try: + import tflite.Model + + tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buf, 0) + except AttributeError: + import tflite + + tflite_model = tflite.Model.GetRootAsModel(tflite_model_buf, 0) + except ImportError: + raise ImportError("The tflite package must be installed") + + input_data = convert_to_list(input_data) + input_node = convert_to_list(input_node) + + shape_dict = {} + dtype_dict = {} + for i, name in enumerate(input_node): + shape_dict[name] = input_data[i].shape + dtype_dict[name] = input_data[i].dtype.name + + mod, params = relay.frontend.from_tflite( + tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict + ) + + return mod, params + + +def _change_ndarray_layout(arr, src_layout, dst_layout): + """Makes a copy of an ndarray, reshaping it to a new data layout. + + Parameter + --------- + arr : numpy.ndarray + The ndarray to be reformatted. + + src_layout : str + The current layout of the Relay constant. Must be alphabetic (e.g. NHWC + or OIHW, but not NCHW2c). + + dst_layout : str + The desired layout of new the Relay constant. Must be alphabetic (e.g. NHWC + or OIHW, but not NCHW2c). + + Returns + ------- + dst_shape : numpy.ndarray + A copy of the ndarray with the new layout. + """ + assert src_layout.isalpha() and dst_layout.isalpha() + axis_order = [src_layout.index(c) for c in dst_layout] + return np.transpose(arr, axis_order) + + [email protected]_package("tflite") [email protected]_cmsisnn [email protected]("test_runner", [AOT_CORSTONE300_RUNNER, AOT_USMP_CORSTONE300_RUNNER]) +def test_external_calls(test_runner): + """Download a small network and partition for CMSIS-NN to test forward declarations for external + calls outside of __tvm_main__.""" + # download the model + base_url = ( + "https://github.com/ARM-software/ML-zoo/raw/"; + "48a22ee22325d15d2371a6df24eb7d67e21dcc97" + "/models/keyword_spotting/cnn_small/tflite_int8" + ) + file_to_download = "cnn_s_quantized.tflite" + file_saved = "cnn_s_quantized_15Dec2021.tflite" + model_file = download_testdata("{}/{}".format(base_url, file_to_download), file_saved) + + with open(model_file, "rb") as f: + tflite_model_buf = f.read() + + input_shape = (1, 490) + dtype = "int8" + in_min, in_max = get_range_for_dtype_str(dtype) + rng = np.random.default_rng(12345) + input_data = rng.integers(in_min, high=in_max, size=input_shape, dtype=dtype) + + orig_mod, params = _convert_to_relay(tflite_model_buf, input_data, "input") + cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params) + + # validate CMSIS-NN output against CPU output + interface_api = "c" + use_unpacked_api = True + inputs = {"input": input_data} + params = {} + output_list = generate_ref_data(orig_mod["main"], inputs, params) + compiled_models = compile_models( + AOTTestModel( + module=cmsisnn_mod, + inputs=inputs, + outputs=output_list, + params=None, + output_tolerance=1, + ), + interface_api, + use_unpacked_api, + pass_config=test_runner.pass_config, + ) + + # Forward function declaration increases the number of times a function name appears under + # __tvm__main. Validate this frequency for native, offloaded and allocation functions. + lib_mod = compiled_models[0].executor_factory.lib.imported_modules[0] + main_source = lib_mod.get_source() + assert ( + main_source.count("TVMBackendAllocWorkspace") == 3 + or main_source.count("TVMBackendAllocWorkspace") == 0 + ) + assert main_source.count("tvmgen_default_fused_reshape") == 2 + assert main_source.count("tvmgen_default_cmsis_nn_main") == 12 + cmsisnn_source = lib_mod.imported_modules[0].get_source() + assert cmsisnn_source.count("arm_convolve_wrapper") == 1 + assert cmsisnn_source.count("arm_fully_connected") == 3 + assert cmsisnn_source.count("arm_softmax") == 1 + + +@parametrize_aot_options +def test_internal_calls(interface_api, use_unpacked_api, test_runner): + """Test for all internal function calls. No forward declarations are expected here.""" + dtype = "float32" + groups = 32 + weight_shape = 1 + ishape = (1, 32, 14, 14) + wshape = (32, weight_shape, 3, 3) + pass_config = {"tir.usmp.enable": True} + test_runner = AOTTestRunner( + makefile=test_runner.makefile, + prologue=test_runner.prologue, + epilogue=test_runner.epilogue, + includes=test_runner.includes, + parameters=test_runner.parameters, + pass_config=pass_config, + ) + + data0 = relay.var("data", shape=ishape, dtype=dtype) + weight0 = relay.var("weight", shape=wshape, dtype=dtype) + out = relay.nn.conv2d(data0, weight0, kernel_size=(3, 3), padding=(1, 1), groups=groups) + main_f = relay.Function([data0, weight0], out) + mod = tvm.IRModule() + mod["main"] = main_f + mod = tvm.relay.transform.InferType()(mod) + + i_data = np.random.uniform(0, 1, ishape).astype(dtype) + w1_data = np.random.uniform(0, 1, wshape).astype(dtype) + + inputs = OrderedDict([("data", i_data), ("weight", w1_data)]) + + output_list = generate_ref_data(mod, inputs) + compiled_models = compile_models( + models=AOTTestModel(module=mod, inputs=inputs, outputs=output_list), + interface_api=interface_api, + use_unpacked_api=use_unpacked_api, + pass_config=test_runner.pass_config, + ) + + lib_mod = compiled_models[0].executor_factory.lib.imported_modules[0] + main_source = lib_mod.get_source() + assert main_source.count("tvmgen_default_fused_nn_contrib_depthwise_conv2d_NCHWc") == 2 + assert main_source.count("tvmgen_default_fused_layout_transform") == 6 + + +@skip_if_no_reference_system [email protected]_cmsisnn +def test_tensorized_calls(): + """Test a subgraph with a mix of internal and tensorized calls.""" + data_shape, kernel_size, num_filter, groups, strides, padding, dilation = ( + (1, 32, 32, 16), + (3, 3), + 16, + 1, + 1, + (0, 2, 2, 0), + 1, + ) + in_dtype = "int8" + data_layout = "NHWC" + kernel_layout = "HWOI" + ref_kernel_layout = "HWIO" + out_layout = "NHWC" + schedule_name = "conv2d_nhwc_dsp.arm_cpu" + + ref_input_data = np.random.randint(low=-128, high=127, size=data_shape, dtype=in_dtype) + ref_input_var = relay.var("input", relay.TensorType(data_shape, in_dtype)) # NHWC layout + kernel_shape = (*kernel_size, data_shape[-1] // groups, num_filter) # HWIO layout + ref_kernel_data = np.random.randint(low=-10, high=10, size=kernel_shape, dtype=in_dtype) + + ref_relay_op = relay.op.nn.conv2d( + ref_input_var, + relay.const(_change_ndarray_layout(ref_kernel_data, "HWIO", ref_kernel_layout)), + kernel_size=kernel_size, + strides=strides, + padding=padding, + groups=groups, + dilation=(dilation, dilation), + data_layout="NHWC", + kernel_layout=ref_kernel_layout, + out_dtype="int32", + out_layout="NHWC", + ) + ref_module = tvm.IRModule.from_expr(relay.Function([ref_input_var], ref_relay_op)) + ref_outputs = generate_ref_data(ref_module, {"input": ref_input_data}) + + # Reshape output dictionary to match out_layout + assert len(ref_outputs) == 1 + output_tensor_name, output_tensor = next(iter(ref_outputs.items())) + ref_outputs[output_tensor_name] = _change_ndarray_layout(output_tensor, "NHWC", out_layout) + + test_input_data = _change_ndarray_layout(ref_input_data, "NHWC", data_layout) + test_input_var = relay.var("input", relay.TensorType(test_input_data.shape, in_dtype)) + test_kernel_data = _change_ndarray_layout(ref_kernel_data, "HWIO", kernel_layout) + + test_relay_op = relay.op.nn.conv2d( + test_input_var, + relay.const(test_kernel_data), + kernel_size=kernel_size, + strides=strides, + padding=padding, + groups=groups, + dilation=(dilation, dilation), + data_layout=data_layout, + kernel_layout=kernel_layout, + out_dtype="int32", + out_layout=out_layout, + ) + test_function = relay.Function([test_input_var], test_relay_op) + test_model = AOTTestModel( + module=tvm.IRModule.from_expr(test_function), + inputs={"input": test_input_data}, + outputs=ref_outputs, + ) + compiled_models = compile_models( + test_model, + interface_api="c", + use_unpacked_api=True, + pass_config=AOT_CORSTONE300_RUNNER.pass_config, + target=f"c -keys=arm_cpu -mcpu=cortex-m7", + schedule_name=schedule_name, + ) + + lib_mod = compiled_models[0].executor_factory.lib.imported_modules[0] + main_source = lib_mod.get_source() + assert main_source.count("tvmgen_default_fused_nn_conv2d") == 2 + assert main_source.count("gemm_") == 13 Review Comment: Noted. 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