ashutosh-arm commented on a change in pull request #9331: URL: https://github.com/apache/tvm/pull/9331#discussion_r733864218
########## File path: tests/python/contrib/test_cmsisnn/test_conv2d.py ########## @@ -0,0 +1,303 @@ +# 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: Conv2D""" +import itertools +import numpy as np +import pytest +import tvm +from tvm import relay +from tvm.relay.op.contrib import cmsisnn + + +from tests.python.relay.aot.aot_test_utils import ( + AOTTestModel, + AOT_CORSTONE300_RUNNER, + AOT_DEFAULT_RUNNER, + generate_ref_data, + compile_and_run, +) +from utils import ( + skip_if_no_reference_system, + make_module, + count_num_calls, + get_range_for_dtype_str, + get_same_padding, + get_conv2d_qnn_params, + make_qnn_relu, +) + + +def make_model( + shape, + kernel_shape, + input_zp, + input_sc, + kernel_zp, + kernel_sc, + output_zp, + output_sc, + padding, + strides, + dilation, + groups, + dtype, + kernel_dtype, + out_channels, + weight_format, + enable_bias, + relu_type, +): + """Return a model and any parameters it may have""" + h_index = weight_format.index("H") + w_index = weight_format.index("W") + kernel_h = kernel_shape[h_index] + kernel_w = kernel_shape[w_index] + a = relay.var("in0", shape=shape, dtype=dtype) + p = (0, 0, 0, 0) + if padding == "SAME": + p = get_same_padding((shape[1], shape[2]), (kernel_h, kernel_w), dilation, strides) + a = relay.nn.pad( + a, + pad_width=[(0, 0), (p[0], p[2]), (p[1], p[3]), (0, 0)], + pad_value=input_zp, + pad_mode="constant", + ) + shape = (shape[0], shape[1] + p[0] + p[2], shape[2] + p[1] + p[3], shape[3]) + + weight_shape = (kernel_h, kernel_w, shape[3] // groups, out_channels) + w = tvm.nd.array( + np.random.randint( + np.iinfo(kernel_dtype).min, + high=np.iinfo(kernel_dtype).max, + size=weight_shape, + dtype=kernel_dtype, + ) + ) + weights = relay.const(w, kernel_dtype) + conv = relay.qnn.op.conv2d( + a, + weights, + input_zero_point=relay.const(input_zp, "int32"), + kernel_zero_point=relay.const(kernel_zp, "int32"), + input_scale=relay.const(input_sc, "float32"), + kernel_scale=relay.const(kernel_sc, "float32"), + kernel_size=(kernel_h, kernel_w), + data_layout="NHWC", + kernel_layout=weight_format, + dilation=dilation, + strides=strides, + groups=groups, + channels=out_channels, + padding=p, + out_dtype="int32", + ) + b = tvm.nd.array(np.random.randint(0, high=10, size=(out_channels,), dtype="int32")) + bc = relay.const(b, "int32") Review comment: Changed it to bias_const. ########## File path: tests/python/contrib/test_cmsisnn/test_conv2d.py ########## @@ -0,0 +1,303 @@ +# 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: Conv2D""" +import itertools +import numpy as np +import pytest +import tvm +from tvm import relay +from tvm.relay.op.contrib import cmsisnn + + +from tests.python.relay.aot.aot_test_utils import ( + AOTTestModel, + AOT_CORSTONE300_RUNNER, + AOT_DEFAULT_RUNNER, + generate_ref_data, + compile_and_run, +) +from utils import ( + skip_if_no_reference_system, + make_module, + count_num_calls, + get_range_for_dtype_str, + get_same_padding, + get_conv2d_qnn_params, + make_qnn_relu, +) + + +def make_model( + shape, + kernel_shape, + input_zp, + input_sc, + kernel_zp, + kernel_sc, + output_zp, + output_sc, + padding, + strides, + dilation, + groups, + dtype, + kernel_dtype, + out_channels, + weight_format, + enable_bias, + relu_type, +): + """Return a model and any parameters it may have""" + h_index = weight_format.index("H") + w_index = weight_format.index("W") + kernel_h = kernel_shape[h_index] + kernel_w = kernel_shape[w_index] + a = relay.var("in0", shape=shape, dtype=dtype) + p = (0, 0, 0, 0) + if padding == "SAME": + p = get_same_padding((shape[1], shape[2]), (kernel_h, kernel_w), dilation, strides) + a = relay.nn.pad( + a, + pad_width=[(0, 0), (p[0], p[2]), (p[1], p[3]), (0, 0)], + pad_value=input_zp, + pad_mode="constant", + ) + shape = (shape[0], shape[1] + p[0] + p[2], shape[2] + p[1] + p[3], shape[3]) + + weight_shape = (kernel_h, kernel_w, shape[3] // groups, out_channels) + w = tvm.nd.array( + np.random.randint( + np.iinfo(kernel_dtype).min, + high=np.iinfo(kernel_dtype).max, + size=weight_shape, + dtype=kernel_dtype, + ) + ) + weights = relay.const(w, kernel_dtype) + conv = relay.qnn.op.conv2d( + a, + weights, + input_zero_point=relay.const(input_zp, "int32"), + kernel_zero_point=relay.const(kernel_zp, "int32"), + input_scale=relay.const(input_sc, "float32"), + kernel_scale=relay.const(kernel_sc, "float32"), + kernel_size=(kernel_h, kernel_w), + data_layout="NHWC", + kernel_layout=weight_format, + dilation=dilation, + strides=strides, + groups=groups, + channels=out_channels, + padding=p, + out_dtype="int32", + ) + b = tvm.nd.array(np.random.randint(0, high=10, size=(out_channels,), dtype="int32")) + bc = relay.const(b, "int32") + bias = conv + if enable_bias: + bias = relay.nn.bias_add(conv, bc, axis=3) + requant_input_sc = [sc * input_sc for sc in kernel_sc] + req = relay.qnn.op.requantize( + bias, + relay.const(requant_input_sc, "float32"), + relay.const(0, "int32"), + relay.const(output_sc, "float32"), + relay.const(output_zp, "int32"), + out_dtype=dtype, + ) + relu = make_qnn_relu(req, relu_type, output_sc, output_zp, dtype) + params = {"w": w, "b": b} + return relu, params + + +@tvm.testing.requires_cmsisnn +@pytest.mark.parametrize("ifm_shape", [(1, 28, 28, 12), (1, 64, 100, 4)]) +@pytest.mark.parametrize("kernel_size", [(3, 3)]) +@pytest.mark.parametrize("padding", ["SAME", "VALID"]) +@pytest.mark.parametrize("strides, dilation", [((2, 2), (1, 1)), ((1, 1), (1, 1))]) +@pytest.mark.parametrize("enable_bias", [True, False]) +@pytest.mark.parametrize("relu_type", ["NONE", "RELU"]) +@pytest.mark.parametrize( + "in_zp, in_sc, k_sc, out_channels", + [(10, 0.0128, [0.11, 0.22], 2), (-64, 1, [1, 0.0256, 1.37], 3)], +) +def test_op_int8( + ifm_shape, + kernel_size, + padding, + strides, + dilation, + enable_bias, + relu_type, + in_zp, + in_sc, + k_sc, + out_channels, +): + interface_api = "c" + use_unpacked_api = True + test_runner = AOT_CORSTONE300_RUNNER + + k_zp = 0 Review comment: Ah! Reason for them being cryptic is because they have derived from another target, but that's no excuse for not changing them. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: commits-unsubscr...@tvm.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org
