ashutosh-arm commented on a change in pull request #9331: URL: https://github.com/apache/tvm/pull/9331#discussion_r733864363
########## 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 + groups = 1 + weight_format = "HWIO" + kernel_h = kernel_size[0] + kernel_w = kernel_size[1] + dtype = "int8" + in_min, in_max = get_range_for_dtype_str(dtype) + + weight_shape = None + if weight_format == "HWIO": + weight_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels) + else: + weight_shape = (kernel_h, kernel_w, ifm_shape[3], out_channels) + + out_sc, out_zp = get_conv2d_qnn_params( + weight_shape, in_sc, in_zp, k_sc, k_zp, dtype, dtype, dtype, False + ) + + model, params = make_model( + ifm_shape, + weight_shape, + in_zp, + in_sc, + k_zp, + k_sc, + out_zp, + out_sc, + padding, + strides, + dilation, + groups, + dtype, + dtype, + out_channels, + weight_format, + enable_bias, + relu_type, + ) + orig_mod = make_module(model) + cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params) + + # validate pattern matching + attrs = [ + cmsisnn_mod[var.name_hint].attrs + for var in cmsisnn_mod.get_global_vars() + if cmsisnn_mod[var.name_hint].attrs + ] + assert any(attrs), "At least one function with external attributes was expected." + + compilers = [ + key == "Compiler" and value == "cmsisnn" for attr in attrs for key, value in attr.items() + ] + assert any(compilers), "Module does not contain function for cmsisnn target." + + assert count_num_calls(orig_mod) == count_num_calls( + cmsisnn_mod + ), "Number of calls changed during partitioning" + + # validate the output + np.random.seed(0) Review comment: Thanks! This was new to me. ########## 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 + groups = 1 + weight_format = "HWIO" + kernel_h = kernel_size[0] + kernel_w = kernel_size[1] + dtype = "int8" + in_min, in_max = get_range_for_dtype_str(dtype) + + weight_shape = None + if weight_format == "HWIO": + weight_shape = (kernel_h, kernel_w, ifm_shape[3] // groups, out_channels) + else: + weight_shape = (kernel_h, kernel_w, ifm_shape[3], out_channels) + + out_sc, out_zp = get_conv2d_qnn_params( + weight_shape, in_sc, in_zp, k_sc, k_zp, dtype, dtype, dtype, False + ) + + model, params = make_model( + ifm_shape, + weight_shape, + in_zp, + in_sc, + k_zp, + k_sc, + out_zp, + out_sc, + padding, + strides, + dilation, + groups, + dtype, + dtype, + out_channels, + weight_format, + enable_bias, + relu_type, + ) + orig_mod = make_module(model) + cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params) + + # validate pattern matching + attrs = [ + cmsisnn_mod[var.name_hint].attrs + for var in cmsisnn_mod.get_global_vars() + if cmsisnn_mod[var.name_hint].attrs + ] + assert any(attrs), "At least one function with external attributes was expected." + + compilers = [ + key == "Compiler" and value == "cmsisnn" for attr in attrs for key, value in attr.items() + ] + assert any(compilers), "Module does not contain function for cmsisnn target." + + assert count_num_calls(orig_mod) == count_num_calls( + cmsisnn_mod + ), "Number of calls changed during partitioning" + + # validate the output + np.random.seed(0) + inputs = { + "in0": np.random.randint(in_min, high=in_max, size=ifm_shape, dtype="int8"), + } + output_list = generate_ref_data(orig_mod["main"], inputs, params) + compile_and_run( + AOTTestModel( + module=cmsisnn_mod, + inputs=inputs, + outputs=output_list, + params=params, + output_tolerance=1, + ), + test_runner, + interface_api, + use_unpacked_api, + ) + + +def parameterize_for_invalid_model(test): + in_dtype = ["uint8", "int8"] + kernel_dtype = ["uint8", "int8"] + kernel_zero_point = [-33, 10, 0] + all_combinations = itertools.product(in_dtype, kernel_dtype, kernel_zero_point) + all_combinations = filter( + lambda parameters: not ( + parameters[0] == "int8" and parameters[1] == "int8" and parameters[2] == 0 + ), + all_combinations, + ) + return pytest.mark.parametrize( + ["in_dtype", "kernel_dtype", "kernel_zero_point"], + all_combinations, + )(test) + + +@parameterize_for_invalid_model +def test_invalid_parameters( + in_dtype, + kernel_dtype, + kernel_zero_point, +): + ifm_shape = (1, 28, 28, 12) + out_channels = 2 + in_sc = 1 + in_zp = 24 + k_sc = [0.11, 0.0237] + in_min, in_max = get_range_for_dtype_str(in_dtype) + + kernel_layout = "HWIO" + kernel_shape = [3, 3, ifm_shape[3], out_channels] + out_sc, out_zp = get_conv2d_qnn_params( + kernel_shape, in_sc, in_zp, k_sc, kernel_zero_point, in_dtype, kernel_dtype, in_dtype, False + ) + model, params = make_model( + shape=ifm_shape, + kernel_shape=kernel_shape, + input_zp=in_zp, + input_sc=in_sc, + kernel_zp=kernel_zero_point, + kernel_sc=k_sc, + output_zp=out_zp, + output_sc=out_sc, + padding="SAME", + strides=(1, 1), + dilation=(1, 1), + groups=1, + dtype=in_dtype, + kernel_dtype=kernel_dtype, + out_channels=out_channels, + weight_format=kernel_layout, + enable_bias=True, + relu_type="NONE", + ) + orig_mod = make_module(model) + cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params) + + # print(cmsisnn_mod.astext(False)) Review comment: Removed it. ########## File path: tests/python/contrib/test_cmsisnn/test_networks.py ########## @@ -92,7 +92,6 @@ def test_cnn_small(): orig_mod, params = convert_to_relay(tflite_model_buf, input_data, "input") cmsisnn_mod = cmsisnn.partition_for_cmsisnn(orig_mod, params) - Review comment: No. Added back in. ########## File path: src/relay/backend/contrib/cmsisnn/extract_constants.cc ########## @@ -0,0 +1,158 @@ + +/* + * 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. + */ +#include <tvm/relay/attrs/nn.h> +#include <tvm/relay/expr_functor.h> +#include <tvm/relay/transform.h> +#include <tvm/runtime/ndarray.h> + +#include "../../../qnn/utils.h" +#include "../../../transforms/pattern_utils.h" + +namespace tvm { +namespace relay { +namespace contrib { +namespace cmsisnn { + +class ExtractConstantsMutator : public MixedModeMutator { + public: + explicit ExtractConstantsMutator(IRModule& mod) : mod_(mod) {} + + private: + String gen_var_name() { return "tvm_var_extract_const_" + std::to_string(var_count_++); } + + Expr VisitExpr_(const FunctionNode* func) final { + Function final_func = GetRef<Function>(func); + ++func_nesting_level_; + auto new_body = VisitExpr(func->body); + --func_nesting_level_; + if (!new_body.same_as(func->body)) { + final_func = Function(FreeVars(new_body), new_body, func->ret_type, + FreeTypeVars(new_body, mod_), func->attrs); + function_to_constants_.Set(GetRef<Function>(func), constants_within_function_); + constants_within_function_.clear(); + } + return final_func; + } + + Expr Rewrite_(const CallNode* call, const Expr& post) final { + Expr final_call = post; + auto* post_call = post.as<CallNode>(); + if (post_call == nullptr) { + return final_call; + } + + // Replace Constant arguments with Vars for ML Operators + // Perform this for non-main Call Nodes only + if (func_nesting_level_ && call->op.as<OpNode>()) { + Array<Expr> new_args; + for (auto& arg : post_call->args) { + auto* const_arg = arg.as<ConstantNode>(); + if (const_arg && !const_arg->is_scalar()) { + Var var_arg = Var(gen_var_name(), const_arg->tensor_type()); + new_args.push_back(var_arg); + constants_within_function_.push_back(GetRef<Constant>(const_arg)); + } else { + new_args.push_back(arg); + } + } + final_call = Call(call->op, new_args, call->attrs, {}); + } + + // Since the constants are kicked out of partitioned functions + // a new call to global function is needed + if (auto* glob_var_node = post_call->op.as<GlobalVarNode>()) { + auto glob_var = GetRef<GlobalVar>(glob_var_node); + auto glob_func = Downcast<Function>(mod_->Lookup(glob_var)); + auto new_glob_func = VisitExpr(glob_func); + if (!new_glob_func.same_as(glob_func)) { + mod_->Update(glob_var, Downcast<Function>(new_glob_func)); + Array<Expr> new_args = post_call->args; + ICHECK(function_to_constants_.find(glob_func) != function_to_constants_.end()); + for (auto constant : function_to_constants_.at(glob_func)) { + new_args.push_back(constant); + } + final_call = Call(glob_var, new_args); + } + } + + // Since the constants are kicked out of the local partitioned functions + // a new call to local function is needed + if (auto* func_node = call->op.as<FunctionNode>()) { + Function func = GetRef<Function>(func_node); + auto new_func = VisitExpr(func); + if (!new_func.same_as(func)) { + Array<Expr> new_args = post_call->args; + ICHECK(function_to_constants_.find(func) != function_to_constants_.end()); + for (auto constant : function_to_constants_.at(func)) { + constants_within_function_.push_back(constant); + Var var_arg = Var(gen_var_name(), constant->tensor_type()); + new_args.push_back(var_arg); + } + final_call = Call(new_func, new_args); + } + } + + return final_call; + } + + private: + /* \brief Updated module where all calls have replaced constants with new variables */ + IRModule mod_; + /* \brief Maintains mapping of original function to the replaced constants */ + Map<Function, Array<Constant>> function_to_constants_; + /* \brief Constants being kicked out of a function during the function visit */ + Array<Constant> constants_within_function_; + /* \brief Keeps track of variables being created */ + int var_count_ = 0; + /* \brief Keeps track of function scope */ + int func_nesting_level_ = 0; +}; + +/*! * \brief Kicks out all constants out of the partitioned function into main() */ +IRModule ExtractConstants(IRModule mod) { + String func_name; + Function func; + + auto extract_constants = ExtractConstantsMutator(mod); + Function main_func = Downcast<Function>(mod->Lookup("main")); + auto new_main_body = extract_constants.VisitExpr(main_func->body); + if (!new_main_body.same_as(main_func->body)) { + auto main_var = mod->GetGlobalVar("main"); + auto new_main_func = Function(main_func->params, new_main_body, main_func->ret_type, + main_func->type_params, main_func->attrs); + mod->Update(main_var, new_main_func); + } + return mod; +} + +transform::Pass ExtractConstantsFromPartitionedFunction() { + runtime::TypedPackedFunc<IRModule(IRModule, transform::PassContext)> pass_func = + [=](IRModule m, transform::PassContext pc) { return ExtractConstants(m); }; + return tvm::transform::CreateModulePass(pass_func, 0, "ExtractConstantsFromPartitionedFunction", + {}); +} + +TVM_REGISTER_GLOBAL("relay.ext.cmsisnn.transform.ExtractConstantsFromPartitionedFunction") + .set_body_typed([]() { return ExtractConstantsFromPartitionedFunction(); }); Review comment: ACK ########## File path: src/relay/backend/contrib/cmsisnn/generate_constants.cc ########## @@ -0,0 +1,230 @@ + +/* + * 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. + */ +#include <tvm/relay/attrs/nn.h> +#include <tvm/relay/attrs/transform.h> +#include <tvm/relay/expr_functor.h> +#include <tvm/relay/transform.h> +#include <tvm/runtime/ndarray.h> + +#include "../../../qnn/utils.h" +#include "../../../transforms/pattern_utils.h" + +namespace tvm { +namespace relay { +Expr MakeTranspose(Expr data, Array<Integer> axes); +namespace contrib { +namespace cmsisnn { + +class GenerateConstantsMutator : public MixedModeMutator { + public: + explicit GenerateConstantsMutator(IRModule& mod) : mod_(mod) {} + + private: + /*! * \brief Converts Kernel layout from HWIO to OHWI to align to CMSIS-NN requirements */ + Expr ConvertKernelLayout(Expr kernel_expr, const Conv2DAttrs* conv2d_attrs, Attrs* new_attrs) { + auto attrs = make_object<Conv2DAttrs>(); + attrs->strides = std::move(conv2d_attrs->strides); + attrs->padding = std::move(conv2d_attrs->padding); + attrs->dilation = std::move(conv2d_attrs->dilation); + attrs->groups = conv2d_attrs->groups; + attrs->channels = std::move(conv2d_attrs->channels); + attrs->kernel_size = std::move(conv2d_attrs->kernel_size); + attrs->data_layout = std::move(conv2d_attrs->data_layout); + attrs->kernel_layout = runtime::String("OHWI"); + attrs->out_layout = std::move(conv2d_attrs->out_layout); + attrs->out_dtype = std::move(conv2d_attrs->out_dtype); + *new_attrs = tvm::Attrs{attrs}; + + IRModule kernel_module; + auto func_body = MakeTranspose(kernel_expr, {Integer(3), Integer(0), Integer(1), Integer(2)}); + auto kernel_func = + Function(FreeVars(func_body), func_body, Type(), FreeTypeVars(func_body, kernel_module)); + GlobalVar kernel_var("main"); + kernel_module->Add(kernel_var, kernel_func); + kernel_module = relay::transform::FoldConstant()(kernel_module); + kernel_func = Downcast<Function>(kernel_module->Lookup("main")); + return kernel_func->body; + } + + /*! * \brief Performs weight transpose and substitutes existing constants in the composite + * function for Conv2D with CMSIS-NN Requantize constants */ + Expr GenerateConv2dRequantConstants(const Expr& expr) { + const CallNode* clip_call = nullptr; + const CallNode* requantize_call = nullptr; + const CallNode* bias_add_call = nullptr; + const CallNode* conv2d_call = nullptr; + auto* final_call = expr.as<CallNode>(); + auto* final_op = final_call->op.as<OpNode>(); + if (final_op->name == "clip") { + clip_call = final_call; + requantize_call = clip_call->args[0].as<CallNode>(); + } else { + requantize_call = final_call; + } + auto* requantize_input = requantize_call->args[0].as<CallNode>(); + auto* requantize_input_op = requantize_input->op.as<OpNode>(); + if (requantize_input_op->name == "nn.bias_add") { + bias_add_call = requantize_input; + conv2d_call = bias_add_call->args[0].as<CallNode>(); + } else { + conv2d_call = requantize_input; + } + + // Transpose weights: HWIO -> OHWI + auto* conv2d_attrs = conv2d_call->attrs.as<Conv2DAttrs>(); + tvm::Attrs new_conv2d_attrs; + Expr transposed_kernel = + ConvertKernelLayout(conv2d_call->args[1], conv2d_attrs, &new_conv2d_attrs); + + // Obtain input and output scales from Relay's Requantization + int64_t out_channels = conv2d_attrs->channels.as<IntImmNode>()->value; + float output_scale = GetScalarFromConstant<float>(requantize_call->args[3]); + auto input_scales = tvm::relay::qnn::GetFloatVectorFromConstant(requantize_call->args[1]); + ICHECK(input_scales.size() == static_cast<size_t>(out_channels)); + + // Calculate requantization multiplier and shift + Device dev{DLDeviceType::kDLCPU, 0}; + runtime::NDArray multiplier_nda = + runtime::NDArray::Empty({out_channels}, DataType::Int(32), dev); + runtime::NDArray shift_nda = runtime::NDArray::Empty({out_channels}, DataType::Int(32), dev); + int32_t* multiplier = static_cast<int32_t*>(multiplier_nda->data); + int32_t* shift = static_cast<int32_t*>(shift_nda->data); + for (int i = 0; i < out_channels; ++i) { + double effective_output_scale = + static_cast<double>(input_scales[i]) / static_cast<double>(output_scale); + std::tie(*(multiplier + i), *(shift + i)) = + tvm::relay::qnn::GetFixedPointMultiplierShift(effective_output_scale); + } + + // Create constants from requantization multiplier and shift + Constant multiplier_const(multiplier_nda); + Constant shift_const(shift_nda); + + // Convert scale scalars into Constants + // Scales are expected as Constants by following passes + Expr weight_scale = conv2d_call->args[5]; + Expr req_inp_scale = requantize_call->args[1]; + if (out_channels == 1) { + runtime::NDArray weight_scale_nda = + runtime::NDArray::Empty({out_channels}, DataType::Float(32), dev); + float* weight_scale_p = static_cast<float*>(weight_scale_nda->data); + *weight_scale_p = GetScalarFromConstant<float>(weight_scale); + weight_scale = Constant(weight_scale_nda); + + runtime::NDArray req_inp_scale_nda = + runtime::NDArray::Empty({out_channels}, DataType::Float(32), dev); + float* req_inp_scale_p = static_cast<float*>(req_inp_scale_nda->data); + *req_inp_scale_p = GetScalarFromConstant<float>(req_inp_scale); + req_inp_scale = Constant(req_inp_scale_nda); + } + + // Replace existing weights (HWIO) with the transposed ones (OHWI) + // Substitute Conv2D weight_zero_point with the CMSIS-NN multiplier + // Substitute Requantize input_zero_point with CMSIS-NN shift + // Conv2D arguments: data, weight, input_zp, weight_zp, input_sc, weight_sc + Array<Expr> conv2d_args = {conv2d_call->args[0], transposed_kernel, conv2d_call->args[2], + multiplier_const, conv2d_call->args[4], weight_scale}; + Call ret_call = Call(conv2d_call->op, conv2d_args, new_conv2d_attrs, {}); + if (bias_add_call) { + ret_call = + Call(bias_add_call->op, {ret_call, bias_add_call->args[1]}, bias_add_call->attrs, {}); + } + Array<Expr> requantize_args = {ret_call, req_inp_scale, shift_const, requantize_call->args[3], + requantize_call->args[4]}; + ret_call = Call(requantize_call->op, requantize_args, requantize_call->attrs, {}); + if (clip_call) { + ret_call = Call(clip_call->op, {ret_call}, clip_call->attrs, {}); + } + return ret_call; + } + + Expr Rewrite_(const CallNode* call, const Expr& post) final { + Expr final_call = post; + auto* post_call = post.as<CallNode>(); + if (post_call == nullptr) { + return final_call; + } + + auto* global_var = call->op.as<GlobalVarNode>(); + if (global_var) { + // Update to global function call needed because the body changes while + // generating new constants + Function func = Downcast<Function>(mod_->Lookup(global_var->name_hint)); + Expr new_body = VisitExpr(func->body); + if (!new_body.same_as(func->body)) { + Function new_func = Function(FreeVars(new_body), new_body, func->ret_type, + FreeTypeVars(new_body, mod_), func->attrs); + mod_->Update(GetRef<GlobalVar>(global_var), new_func); + final_call = Call(GetRef<GlobalVar>(global_var), post_call->args); + } + } + + // Recreate composite function and corresponding call + // Updated composite function contains CMSIS-NN quantized multiplier and shift constants + if (call->op.as<FunctionNode>()) { + auto* func = call->op.as<FunctionNode>(); + auto func_name = func->GetAttr<String>(attr::kComposite); + if (func_name.defined() && func_name == "cmsisnn.qnn_conv2d") { + Expr new_body = GenerateConv2dRequantConstants(func->body); + Function new_func = Function(FreeVars(new_body), new_body, func->ret_type, + FreeTypeVars(new_body, mod_), func->attrs); + final_call = Call(new_func, post_call->args); + } + } + + return final_call; + } + + private: + IRModule mod_; +}; + +IRModule GenerateConstants(IRModule mod) { + String func_name; + Function func; + + // Introduces CMSIS-NN constants before the call to the external Relay function + auto generate_constants = GenerateConstantsMutator(mod); + Function main_func = Downcast<Function>(mod->Lookup("main")); + auto new_main_body = generate_constants.VisitExpr(main_func->body); + if (!new_main_body.same_as(main_func->body)) { + auto main_var = mod->GetGlobalVar("main"); + auto new_main_func = Function(main_func->params, new_main_body, main_func->ret_type, + main_func->type_params, main_func->attrs); + mod->Update(main_var, new_main_func); + } + + return mod; +} + +transform::Pass GenerateCMSISNNConstants() { + runtime::TypedPackedFunc<IRModule(IRModule, transform::PassContext)> pass_func = + [=](IRModule m, transform::PassContext pc) { return GenerateConstants(m); }; + return tvm::transform::CreateModulePass(pass_func, 0, "GenerateCMSISNNConstants", {}); +} + +TVM_REGISTER_GLOBAL("relay.ext.cmsisnn.transform.GenerateCMSISNNConstants").set_body_typed([]() { + return GenerateCMSISNNConstants(); 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