[CMSIS-NN][Perf] Converted Relay Conv2D into CMSIS-NN Depthwise (apac…

…he#12006)

apps/microtvm/zephyr_cmsisnn/CMakeLists.txt

@@ -53,6 +53,11 @@ set(DATA_FILES
 )
 set(CMSIS_SOURCES
     ${CMSIS_PATH}/CMSIS/NN/Source/SoftmaxFunctions/arm_softmax_s8.c
+    ${CMSIS_PATH}/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_wrapper_s8.c
+    ${CMSIS_PATH}/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8.c
+    ${CMSIS_PATH}/CMSIS/NN/Source/ConvolutionFunctions/arm_depthwise_conv_s8_opt.c
+    ${CMSIS_PATH}/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_s8.c
+    ${CMSIS_PATH}/CMSIS/NN/Source/NNSupportFunctions/arm_nn_depthwise_conv_nt_t_padded_s8.c
     ${CMSIS_PATH}/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_wrapper_s8.c
     ${CMSIS_PATH}/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1_x_n_s8.c
     ${CMSIS_PATH}/CMSIS/NN/Source/ConvolutionFunctions/arm_convolve_1x1_s8_fast.c

src/relay/backend/contrib/cmsisnn/convolutions.cc

@@ -0,0 +1,46 @@
+/*
+ * 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 "convolutions.h"
+
+#include <string>
+
+#include "../../../qnn/utils.h"
+#include "tvm/ir/transform.h"
+#include "tvm/relay/attrs/nn.h"
+
+namespace tvm {
+namespace relay {
+namespace contrib {
+namespace cmsisnn {
+
+bool IsCMSISNNDepthwise(const Conv2DAttrs* conv2d_attrs, const Array<PrimExpr>& input_shape,
+                        const Array<PrimExpr>& kernel_shape) {
+  std::string kernel_layout = conv2d_attrs->kernel_layout.c_str();
+  int kernel_pos_o = kernel_layout.find("O");
+  int kernel_pos_i = kernel_layout.find("I");
+  int kernel_dim_o_val = qnn::get_const_int(kernel_shape[kernel_pos_o]);
+  int kernel_dim_i_val = qnn::get_const_int(kernel_shape[kernel_pos_i]);
+  int64_t out_channels = conv2d_attrs->channels.as<IntImmNode>()->value;
+  return (out_channels == kernel_dim_o_val * kernel_dim_i_val);
+}
+
+}  // namespace cmsisnn
+}  // namespace contrib
+}  // namespace relay
+}  // namespace tvm

src/relay/backend/contrib/cmsisnn/convolutions.h

@@ -0,0 +1,60 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file src/relay/backend/contrib/cmsisnn/convolutions.h
+ * \brief CMSIS-NN utility functions for Convolutions
+ */
+
+#ifndef TVM_RELAY_BACKEND_CONTRIB_CMSISNN_CONVOLUTIONS_H_
+#define TVM_RELAY_BACKEND_CONTRIB_CMSISNN_CONVOLUTIONS_H_
+
+#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 "../../../op/make_op.h"
+#include "../../../qnn/utils.h"
+#include "../../../transforms/pattern_utils.h"
+
+namespace tvm {
+namespace relay {
+namespace contrib {
+namespace cmsisnn {
+/*!
+ * \brief Checks if Relay Conv2D was originally CMSIS-NN compliant Depthwise Convolution
+ * See:
+ * https://github.com/apache/tvm/blob/6ed3ab3e33f8eafa4acaf53b7a671831de7587e9/python/tvm/relay/frontend/tflite.py#L2107
+ *
+ *
+ * \return true if a Conv2D is a Depthwise Convolution based on Conv2D's inputs' shapes and
+ * attributes
+ */
+
+bool IsCMSISNNDepthwise(const Conv2DAttrs* conv2d_attrs, const Array<PrimExpr>& input_shape,
+                        const Array<PrimExpr>& kernel_shape);
+
+}  // namespace cmsisnn
+}  // namespace contrib
+}  // namespace relay
+}  // namespace tvm
+
+#endif  // TVM_RELAY_BACKEND_CONTRIB_CMSISNN_CONVOLUTIONS_H_

src/relay/backend/contrib/cmsisnn/generate_constants.cc

@@ -31,6 +31,7 @@
 #include "../../../op/make_op.h"
 #include "../../../qnn/utils.h"
 #include "../../../transforms/pattern_utils.h"
+#include "convolutions.h"
 
 namespace tvm {
 namespace relay {
@@ -111,11 +112,7 @@ class GenerateConstantsMutator : public MixedModeMutator {
 
     Array<PrimExpr> input_shape = conv2d_call->args[0]->type_as<TensorTypeNode>()->shape;
     Array<PrimExpr> kernel_shape = conv2d_call->args[1]->type_as<TensorTypeNode>()->shape;
-    std::string kernel_layout = conv2d_attrs->kernel_layout.c_str();
-    int kernel_pos_o = kernel_layout.find("O");
-    int groups = conv2d_attrs->groups;
-    if (groups != qnn::get_const_int(input_shape[3]) ||
-        groups != qnn::get_const_int(kernel_shape[kernel_pos_o])) {
+    if (!IsCMSISNNDepthwise(conv2d_attrs, input_shape, kernel_shape)) {
       // Transpose weights: HWIO -> OHWI for Conv2D
       conv2d_kernel = ConvertKernelLayout(conv2d_call->args[1], conv2d_attrs, &new_conv2d_attrs);
     }

src/relay/backend/contrib/cmsisnn/relay_to_tir.cc

@@ -1,4 +1,3 @@
-
 /*
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
@@ -31,6 +30,7 @@
 #include "../../../transforms/pattern_utils.h"
 #include "buffer_size.h"
 #include "compiler_attrs.h"
+#include "convolutions.h"
 
 namespace tvm {
 namespace relay {
@@ -173,7 +173,6 @@ class RelayToTIRVisitor : public MixedModeMutator {
     int32_t dilation_w = qnn::get_const_int(conv2d_attrs->dilation[1]);
     int32_t dilation_h = qnn::get_const_int(conv2d_attrs->dilation[0]);
     int32_t out_channels = qnn::get_const_int(conv2d_attrs->channels);
-    int32_t groups = conv2d_attrs->groups;
     std::string kernel_layout = conv2d_attrs->kernel_layout.c_str();
     int32_t clip_min = std::numeric_limits<int8_t>::min();
     int32_t clip_max = std::numeric_limits<int8_t>::max();
@@ -207,11 +206,13 @@ class RelayToTIRVisitor : public MixedModeMutator {
     int32_t output_c = qnn::get_const_int(output_shape[3]);
 
     int32_t depth_multiplier = -1;
-    int kernel_pos_o = kernel_layout.find("O");
-    if (groups == qnn::get_const_int(input_shape[3]) &&
-        groups == qnn::get_const_int(filter_shape[kernel_pos_o])) {
+    if (IsCMSISNNDepthwise(conv2d_attrs, input_shape, filter_shape)) {
+      // Refer to TVM frontend to know how depth multiplier and out_channels are related
+      // https://github.com/apache/tvm/blob/6ed3ab3e33f8eafa4acaf53b7a671831de7587e9/python/tvm/relay/frontend/tflite.py#L2129
       int kernel_pos_i = kernel_layout.find("I");
-      depth_multiplier = qnn::get_const_int(filter_shape[kernel_pos_i]);
+      int kernel_pos_o = kernel_layout.find("O");
+      int kernel_pos_dm = input_c == 1 ? kernel_pos_o : kernel_pos_i;
+      depth_multiplier = qnn::get_const_int(filter_shape[kernel_pos_dm]);
     }
     scalar_args.push_back(ToArg(depth_multiplier));
 

tests/python/contrib/test_cmsisnn/test_conv2d.py

@@ -23,8 +23,13 @@
 from tvm import relay
 from tvm.relay.op.contrib import cmsisnn
 
-from tvm.testing.aot import generate_ref_data, AOTTestModel, compile_models, compile_and_run
-
+from tvm.testing.aot import (
+    generate_ref_data,
+    AOTTestModel,
+    compile_models,
+    compile_and_run,
+    run_and_check,
+)
 from tvm.micro.testing.aot_test_utils import AOT_USMP_CORSTONE300_RUNNER
 from .utils import (
     make_module,
@@ -84,13 +89,14 @@ def make_model(
         )
     )
     weight_const = relay.const(weight, kernel_dtype)
+    conv2d_kernel_sc = kernel_scale[0] if out_channels == 1 else kernel_scale
     conv = relay.qnn.op.conv2d(
         invar,
         weight_const,
         input_zero_point=relay.const(input_zero_point, "int32"),
         kernel_zero_point=relay.const(kernel_zero_point, "int32"),
         input_scale=relay.const(input_scale, "float32"),
-        kernel_scale=relay.const(kernel_scale, "float32"),
+        kernel_scale=relay.const(conv2d_kernel_sc, "float32"),
         kernel_size=(kernel_h, kernel_w),
         data_layout="NHWC",
         kernel_layout=weight_format,
@@ -105,6 +111,7 @@ def make_model(
     bias_const = relay.const(bias, "int32")
     last_op = relay.nn.bias_add(conv, bias_const, axis=3) if enable_bias else conv
     requant_input_sc = [sc * input_scale for sc in kernel_scale]
+    requant_input_sc = requant_input_sc[0] if out_channels == 1 else requant_input_sc
     last_op = relay.qnn.op.requantize(
         last_op,
         relay.const(requant_input_sc, "float32"),
@@ -209,7 +216,7 @@ def test_conv2d_number_primfunc_args(
     cmsisnn_func = cmsisnn_tir_mod["tvmgen_default_cmsis_nn_main_0"]
     assert (
         len(cmsisnn_func.params) == expected_num_params
-    ), "Generated unexpected number of function arguments"
+    ), "Generated unexpected number of function arguments."
 
 
 @tvm.testing.requires_cmsisnn
@@ -540,6 +547,135 @@ def test_depthwise_int8(
     )
 
 
+@tvm.testing.requires_cmsisnn
+@pytest.mark.parametrize("padding", ["SAME", "VALID"])
+@pytest.mark.parametrize("strides, dilation", [((1, 1), (1, 1))])
+@pytest.mark.parametrize("relu_type", ["RELU", "NONE"])
+@pytest.mark.parametrize("depth_multiplier", [1, 3])
+@pytest.mark.parametrize(
+    "input_zero_point, input_scale, kernel_scale",
+    [
+        (
+            10,
+            0.0128,
+            [0.11, 0.22],
+        ),
+        (
+            -64,
+            1,
+            [1, 0.0256, 1.37],
+        ),
+    ],
+)
+def test_relay_conv2d_cmsisnn_depthwise_int8(
+    padding,
+    strides,
+    dilation,
+    relu_type,
+    input_zero_point,
+    input_scale,
+    kernel_scale,
+    depth_multiplier,
+):
+    """Tests QNN Depthwise int8 op via CMSIS-NN"""
+    interface_api = "c"
+    use_unpacked_api = True
+    test_runner = AOT_USMP_CORSTONE300_RUNNER
+
+    dtype = "int8"
+    in_min, in_max = get_range_for_dtype_str(dtype)
+
+    ifm_shape = (1, 24, 24, 1)
+    groups = ifm_shape[3]
+    weight_format = "HWIO"
+    (kernel_h, kernel_w) = (3, 3)
+    kernel_shape = (kernel_h, kernel_w, ifm_shape[3], depth_multiplier)
+    out_channels = ifm_shape[3] * depth_multiplier
+    enable_bias = True
+    ks_len = len(kernel_scale)
+    kernel_zero_point = 0
+    kernel_scale = [kernel_scale[i % ks_len] for i in range(out_channels)]
+
+    output_scale, output_zero_point = get_conv2d_qnn_params(
+        kernel_shape,
+        input_scale,
+        input_zero_point,
+        kernel_scale,
+        kernel_zero_point,
+        dtype,
+        dtype,
+        dtype,
+        True,
+    )
+
+    model, params = make_model(
+        ifm_shape,
+        kernel_shape,
+        input_zero_point,
+        input_scale,
+        kernel_zero_point,
+        kernel_scale,
+        output_zero_point,
+        output_scale,
+        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
+    assert_partitioned_function(orig_mod, cmsisnn_mod)
+
+    # generate reference output
+    rng = np.random.default_rng(12345)
+    inputs = {"input": rng.integers(in_min, high=in_max, size=ifm_shape, dtype=dtype)}
+    output_list = generate_ref_data(orig_mod["main"], inputs, params)
+
+    # validate presence of depthwise convolution
+    compiled_models = compile_models(
+        AOTTestModel(
+            module=cmsisnn_mod,
+            inputs=inputs,
+            outputs=output_list,
+            params=params,
+            output_tolerance=1,
+        ),
+        interface_api,
+        use_unpacked_api,
+        pass_config=test_runner.pass_config,
+    )
+
+    cmsisnn_tir_mod = None
+    for target, mod in compiled_models[0].executor_factory.lowered_ir_mods.items():
+        if target.kind.name == "cmsis-nn":
+            cmsisnn_tir_mod = mod
+
+    cmsisnn_func = cmsisnn_tir_mod["tvmgen_default_cmsis_nn_main_0"]
+    call_extern = None
+    if isinstance(cmsisnn_func.body, tvm.tir.stmt.Evaluate):
+        call_extern = cmsisnn_func.body.value
+    else:
+        call_extern = cmsisnn_func.body.body.value
+    assert (
+        call_extern.args[0].value == "arm_depthwise_conv_wrapper_s8"
+    ), "Relay Conv2D should be mapped to CMSIS-NN Depthwise Convolution."
+
+    # validate the output
+    run_and_check(
+        models=compiled_models,
+        runner=test_runner,
+        interface_api=interface_api,
+    )
+
+
 def parameterize_for_invalid_model(test):
     """Generates non int8 inputs"""
     in_dtype = ["uint8", "int8"]