feat(mge/tools): add support of receptive_field stats for NetworkNode

GitOrigin-RevId: 11ef3354689d343883348d4129bc89db784e3fe0

imperative/python/megengine/tools/network_visualize.py

@@ -7,6 +7,7 @@
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 import argparse
+import json
 import logging
 
 import numpy as np
@@ -14,6 +15,7 @@ import numpy as np
 from megengine.core.tensor.dtype import is_quantize
 from megengine.logger import _imperative_rt_logger, get_logger, set_mgb_log_level
 from megengine.utils.module_stats import (
+    get_flops_stats,
     get_param_stats,
     print_flops_stats,
     print_params_stats,
@@ -89,6 +91,7 @@ def visualize(
 
         inp_list = [process_name(var.owner.name) for var in node.inputs]
         if log_path:
+            # detail format see tensorboard/compat/proto/attr_value.proto
             attr = {
                 "_output_shapes": AttrValue(
                     list=AttrValue.ListValue(
@@ -101,24 +104,20 @@ def visualize(
                         ]
                     )
                 ),
+                "params": AttrValue(s=str(node.params).encode(encoding="utf-8")),
+                "dtype": AttrValue(s=str(node_oup.dtype).encode(encoding="utf-8")),
             }
-        if hasattr(node, "calc_flops"):
-            flops_num = node.calc_flops()
+        flops_stats = get_flops_stats(node, node.inputs, node.outputs)
+        if flops_stats is not None:
             # add op flops attr
-            if log_path:
+            if log_path and hasattr(flops_stats, "flops_num"):
                 attr["flops"] = AttrValue(
-                    s=sizeof_fmt(flops_num).encode(encoding="utf-8")
-                )
-            flops_list.append(
-                dict(
-                    name=node.name,
-                    class_name=node.type,
-                    input_shapes=[i.shape for i in node.inputs],
-                    output_shapes=[o.shape for o in node.outputs],
-                    flops_num=flops_num,
-                    flops_cum=0,
+                    s=sizeof_fmt(flops_stats["flops"]).encode(encoding="utf-8")
                 )
-            )
+            flops_stats["name"] = node.name
+            flops_stats["class_name"] = node.type
+            flops_list.append(flops_stats)
+
         if node.type == "ImmutableTensor":
             param_stats = get_param_stats(node.numpy())
             # add tensor size attr
@@ -132,6 +131,7 @@ def visualize(
         # FIXME(MGE-2165): nodes outside network module may lead to unknown display bug
         if not len(node.name.split(".")) > 2 and not node in graph.input_vars:
             continue
+
         if log_path:
             node_list.append(
                 NodeDef(
@@ -141,14 +141,26 @@ def visualize(
                     attr=attr,
                 )
             )
+    # summary
+    extra_info = {
+        "#ops": len(graph.all_oprs),
+        "#params": len(params_list),
+    }
 
-    total_flops, total_params = None, None
+    total_flops, total_param_dims, total_param_size = 0, 0, 0
     if log_params:
         total_param_dims, total_param_size = print_params_stats(
             params_list, bar_length_max
         )
+        extra_info["total_param_dims"] = sizeof_fmt(total_param_dims)
+        extra_info["total_param_size"] = sizeof_fmt(total_param_size)
     if log_flops:
         total_flops = print_flops_stats(flops_list, bar_length_max)
+        extra_info["total_flops"] = sizeof_fmt(total_flops, suffix="OPs")
+    if log_params and log_flops:
+        extra_info["flops/param_size"] = "{:3.3f}".format(
+            total_flops / total_param_size
+        )
 
     if log_path:
         graph_def = GraphDef(node=node_list, versions=VersionDef(producer=22))
@@ -160,21 +172,12 @@ def visualize(
         writer = SummaryWriter(log_path)
         writer._get_file_writer().add_graph((graph_def, stepstats))
 
-    # summary
-    extra_info = {
-        "#ops": len(graph.all_oprs),
-        "#params": len(params_list),
-        "total_param_dims": sizeof_fmt(total_param_dims),
-        "total_param_size": sizeof_fmt(total_param_size),
-        "total_flops": sizeof_fmt(total_flops, suffix="OPs"),
-        "flops/param_size": "{:3.3f}".format(total_flops / total_param_size),
-    }
     print_summary(**extra_info)
 
     # FIXME: remove this after resolving "span dist too large" warning
     _imperative_rt_logger.set_log_level(old_level)
 
-    return total_params, total_flops
+    return total_param_size, total_flops
 
 
 def main():

imperative/python/megengine/utils/module_stats.py

@@ -26,61 +26,95 @@ logger = mge.get_logger(__name__)
 logger.setLevel("INFO")
 
 
-CALC_FLOPS = {}
-
-
-def _register_modules(*modules):
+_calc_flops_dict = {}
+_calc_receptive_field_dict = {}
+
+
+def _receptive_field_fallback(module, inputs, outputs):
+    assert not hasattr(module, "_rf")
+    assert not hasattr(module, "_stride")
+    if len(inputs) == 0:
+        # TODO: support other dimension
+        module._rf = (1, 1)
+        module._stride = (1, 1)
+        return module._rf, module._stride
+    rf, stride = preprocess_receptive_field(module, inputs, outputs)
+    module._rf = rf
+    module._stride = stride
+    return rf, stride
+
+
+# key tuple, impl_dict, fallback
+_iter_list = [
+    ("flops_num", _calc_flops_dict, None),
+    (
+        ("receptive_field", "stride"),
+        _calc_receptive_field_dict,
+        _receptive_field_fallback,
+    ),
+]
+
+
+def _register_dict(*modules, dict=None):
     def callback(impl):
         for module in modules:
-            CALC_FLOPS[module] = impl
+            dict[module] = impl
         return impl
 
     return callback
 
 
-@_register_modules(
-    m.Conv2d,
-    m.ConvTranspose2d,
-    m.LocalConv2d,
-    qm.Conv2d,
-    qm.ConvRelu2d,
-    qm.ConvBn2d,
-    qm.ConvBnRelu2d,
-    qatm.Conv2d,
-    qatm.ConvRelu2d,
-    qatm.ConvBn2d,
-    qatm.ConvBnRelu2d,
+def register_flops(*modules):
+    return _register_dict(*modules, dict=_calc_flops_dict)
+
+
+def register_receptive_field(*modules):
+    return _register_dict(*modules, dict=_calc_receptive_field_dict)
+
+
+@register_flops(
+    m.Conv1d, m.Conv2d, m.Conv3d,
 )
-def count_convNd(module, input, output):
+def flops_convNd(module: m.Conv2d, inputs, outputs):
     bias = 1 if module.bias is not None else 0
     group = module.groups
-    ic = input[0].shape[1]
-    oc = output[0].shape[1]
+    ic = inputs[0].shape[1]
+    oc = outputs[0].shape[1]
     goc = oc // group
     gic = ic // group
-    N = output[0].shape[0]
-    HW = np.prod(output[0].shape[2:])
+    N = outputs[0].shape[0]
+    HW = np.prod(outputs[0].shape[2:])
     # N x Cout x H x W x  (Cin x Kw x Kh + bias)
     return N * HW * goc * (gic * np.prod(module.kernel_size) + bias)
 
 
-@_register_modules(m.ConvTranspose2d)
-def count_deconvNd(module, input, output):
-    return np.prod(input[0].shape) * output[0].shape[1] * np.prod(module.kernel_size)
+@register_flops(m.ConvTranspose2d)
+def flops_deconvNd(module: m.ConvTranspose2d, inputs, outputs):
+    return np.prod(inputs[0].shape) * outputs[0].shape[1] * np.prod(module.kernel_size)
+
 
+@register_flops(m.Linear)
+def flops_linear(module: m.Linear, inputs, outputs):
+    bias = 1 if module.bias is not None else 0
+    return np.prod(outputs[0].shape) * module.in_features
 
-@_register_modules(m.Linear, qatm.Linear, qm.Linear)
-def count_linear(module, input, output):
-    return np.prod(output[0].shape) * module.in_features
+
+@register_flops(m.BatchMatMulActivation)
+def flops_batchmatmul(module: m.BatchMatMulActivation, inputs, outputs):
+    bias = 1 if module.bias is not None else 0
+    x = inputs[0]
+    w = module.weight
+    batch_size = x.shape[0]
+    n, p = x.shape[1:]
+    _, m = w.shape[1:]
+    return n * (p + bias) * m * batch_size
 
 
 # does not need import qat and quantized module since they inherit from float module.
 hook_modules = (
-    m.Conv2d,
-    m.ConvTranspose2d,
-    m.LocalConv2d,
-    m.BatchNorm2d,
+    m.conv._ConvNd,
     m.Linear,
+    m.BatchMatMulActivation,
 )
 
 
@@ -106,28 +140,71 @@ def sizeof_fmt(num, suffix="B"):
     return "{}{:.1f} {}{}".format(sign_str, num, "Yi", suffix)
 
 
+def preprocess_receptive_field(module, inputs, outputs):
+    # TODO: support other dimensions
+    pre_rf = (
+        max(getattr(i.owner, "_rf", (1, 1))[0] for i in inputs),
+        max(i.owner._rf[1] for i in inputs),
+    )
+    pre_stride = (
+        max(getattr(i.owner, "_stride", (1, 1))[0] for i in inputs),
+        max(i.owner._stride[1] for i in inputs),
+    )
+    return pre_rf, pre_stride
+
+
+def get_flops_stats(module, inputs, outputs):
+    rst = {
+        "input_shapes": [i.shape for i in inputs],
+        "output_shapes": [o.shape for o in outputs],
+    }
+    valid_flag = False
+    for key, _dict, fallback in _iter_list:
+        for _type in _dict:
+            if isinstance(module, _type):
+                value = _dict[_type](module, inputs, outputs)
+                valid_flag = True
+                break
+        else:
+            if fallback is not None:
+                value = fallback(module, inputs, outputs)
+            continue
+
+        if isinstance(key, tuple):
+            assert isinstance(value, tuple)
+            for k, v in zip(key, value):
+                rst[k] = v
+        else:
+            rst[key] = value
+
+    if valid_flag:
+        return rst
+    else:
+        return None
+    return
+
+
 def print_flops_stats(flops, bar_length_max=20):
-    flops_list = [i["flops_num"] for i in flops]
-    max_flops_num = max(flops_list + [0])
-    # calc total flops and set flops_cum
+    max_flops_num = max([i["flops_num"] for i in flops] + [0])
     total_flops_num = 0
     for d in flops:
         total_flops_num += int(d["flops_num"])
         d["flops_cum"] = sizeof_fmt(total_flops_num, suffix="OPs")
 
     for d in flops:
-        f = d["flops_num"]
-        d["flops"] = sizeof_fmt(f, suffix="OPs")
-        r = d["ratio"] = f / total_flops_num
-        d["percentage"] = "{:.2f}%".format(r * 100)
-        bar_length = int(f / max_flops_num * bar_length_max)
+        ratio = d["ratio"] = d["flops_num"] / total_flops_num
+        d["percentage"] = "{:.2f}%".format(ratio * 100)
+        bar_length = int(d["flops_num"] / max_flops_num * bar_length_max)
         d["bar"] = "#" * bar_length
+        d["flops"] = sizeof_fmt(d["flops_num"], suffix="OPs")
 
     header = [
         "name",
         "class_name",
         "input_shapes",
         "output_shapes",
+        "receptive_field",
+        "stride",
         "flops",
         "flops_cum",
         "percentage",
@@ -154,8 +231,8 @@ def get_param_stats(param: np.ndarray):
     param_size = param_dim * nbits // 8
     return {
         "shape": shape,
-        "mean": param.mean(),
-        "std": param.std(),
+        "mean": "{:.3g}".format(param.mean()),
+        "std": "{:.3g}".format(param.std()),
         "param_dim": param_dim,
         "nbits": nbits,
         "size": param_size,
@@ -163,21 +240,20 @@ def get_param_stats(param: np.ndarray):
 
 
 def print_params_stats(params, bar_length_max=20):
+    max_size = max([d["size"] for d in params] + [0])
     total_param_dims, total_param_size = 0, 0
     for d in params:
         total_param_dims += int(d["param_dim"])
         total_param_size += int(d["size"])
-        ratio = d["size"] / total_param_size
-        d["size"] = sizeof_fmt(d["size"])
         d["size_cum"] = sizeof_fmt(total_param_size)
-        d["ratio"] = ratio
-        d["percentage"] = "{:.2f}%".format(ratio * 100)
 
-    # construct bar
-    max_ratio = max([d["ratio"] for d in params])
     for d in params:
-        bar_length = int(d["ratio"] / max_ratio * bar_length_max)
+        ratio = d["size"] / total_param_size
+        d["ratio"] = ratio
+        d["percentage"] = "{:.2f}%".format(ratio * 100)
+        bar_length = int(d["size"] / max_size * bar_length_max)
         d["size_bar"] = "#" * bar_length
+        d["size"] = sizeof_fmt(d["size"])
 
     param_size = sizeof_fmt(total_param_size)
     params.append(dict(name="total", param_dim=total_param_dims, size=param_size,))
@@ -225,26 +301,14 @@ def module_stats(
     :param log_flops: whether print and record op flops.
     """
 
-    def module_stats_hook(module, input, output, name=""):
+    def module_stats_hook(module, inputs, outputs, name=""):
         class_name = str(module.__class__).split(".")[-1].split("'")[0]
 
-        flops_fun = CALC_FLOPS.get(type(module))
-        if callable(flops_fun):
-            flops_num = flops_fun(module, input, output)
-
-            if not isinstance(output, (list, tuple)):
-                output = [output]
-
-            flops.append(
-                dict(
-                    name=name,
-                    class_name=class_name,
-                    input_shapes=[i.shape for i in input],
-                    output_shapes=[o.shape for o in output],
-                    flops_num=flops_num,
-                    flops_cum=0,
-                )
-            )
+        flops_stats = get_flops_stats(module, inputs, outputs)
+        if flops_stats is not None:
+            flops_stats["name"] = name
+            flops_stats["class_name"] = class_name
+            flops.append(flops_stats)
 
         if hasattr(module, "weight") and module.weight is not None:
             w = module.weight
@@ -278,19 +342,22 @@ def module_stats(
     for h in hooks:
         h.remove()
 
-    total_flops, total_params = 0, 0
+    extra_info = {
+        "#params": len(params),
+    }
+    total_flops, total_param_dims, total_param_size = 0, 0, 0
     if log_params:
         total_param_dims, total_param_size = print_params_stats(params, bar_length_max)
+        extra_info["total_param_dims"] = sizeof_fmt(total_param_dims)
+        extra_info["total_param_size"] = sizeof_fmt(total_param_size)
     if log_flops:
         total_flops = print_flops_stats(flops, bar_length_max)
+        extra_info["total_flops"] = sizeof_fmt(total_flops, suffix="OPs")
+    if log_params and log_flops:
+        extra_info["flops/param_size"] = "{:3.3f}".format(
+            total_flops / total_param_size
+        )
 
-    extra_info = {
-        "#params": len(params),
-        "total_param_dims": sizeof_fmt(total_param_dims),
-        "total_param_size": sizeof_fmt(total_param_size),
-        "total_flops": sizeof_fmt(total_flops, suffix="OPs"),
-        "flops/param_size": "{:3.3f}".format(total_flops / total_param_size),
-    }
     print_summary(**extra_info)
 
-    return total_params, total_flops
+    return total_param_size, total_flops

imperative/python/megengine/utils/network_node.py

@@ -18,6 +18,11 @@ from ..core.ops import builtin
 from ..core.tensor.megbrain_graph import InputNode
 from ..tensor import Tensor
 from .comp_graph_tools import replace_vars
+from .module_stats import (
+    preprocess_receptive_field,
+    register_flops,
+    register_receptive_field,
+)
 
 
 class NetworkNode:
@@ -225,8 +230,21 @@ class Elemwise(OpNode):
     type = "Elemwise"
     opdef = builtin.Elemwise
 
-    def calc_flops(self):
-        return np.prod(self.outputs[0].shape)
+
+class ElemwiseMultiType(OpNode):
+    type = "ElemwiseMultiType"
+    opdef = builtin.ElemwiseMultiType
+
+    @classmethod
+    def load(cls, opr):
+        obj = super(ElemwiseMultiType, cls).load(opr)
+        obj.params["dtype"] = opr.outputs[0].dtype
+        return obj
+
+
+@register_flops(Elemwise, ElemwiseMultiType)
+def flops_elemwise(opnode: Elemwise, inputs, outputs):
+    return np.prod(outputs[0].shape)
 
 
 class Reduce(OpNode):
@@ -255,20 +273,24 @@ class MatrixMul(OpNode):
     type = "MatrixMul"
     opdef = builtin.MatrixMul
 
-    def calc_flops(self):
-        assert len(self.inputs[0].shape) == 2 and len(self.outputs[0].shape) == 2
-        mid_shape = self.inputs[0].shape[1]
-        return np.prod(self.outputs[0].shape) * mid_shape
+
+@register_flops(MatrixMul)
+def flops_matmul(opnode: MatrixMul, inputs, outputs):
+    assert len(inputs[0].shape) == 2 and len(outputs[0].shape) == 2
+    mid_shape = inputs[0].shape[1]
+    return np.prod(outputs[0].shape) * mid_shape
 
 
 class BatchedMatrixMul(OpNode):
     type = "BatchedMatmul"
     opdef = builtin.BatchedMatrixMul
 
-    def calc_flops(self):
-        assert len(self.inputs[0].shape) == 3 and len(self.outputs[0].shape) == 3
-        mid_shape = self.inputs[0].shape[2]
-        return np.prod(self.outputs[0].shape) * mid_shape
+
+@register_flops(BatchedMatrixMul)
+def flops_batchmatmul(opnode: BatchedMatrixMul, inputs, outputs):
+    assert len(inputs[0].shape) == 3 and len(outputs[0].shape) == 3
+    mid_shape = inputs[0].shape[2]
+    return np.prod(outputs[0].shape) * mid_shape
 
 
 class Dot(OpNode):
@@ -285,18 +307,6 @@ class ConvolutionForward(OpNode):
     type = "Convolution"
     opdef = builtin.Convolution
 
-    def calc_flops(self):
-        param_W_shape = self.inputs[1].shape
-        kh = param_W_shape[-2]
-        kw = param_W_shape[-1]
-        if len(param_W_shape) == 5:
-            num_input = param_W_shape[2]
-        else:
-            num_input = param_W_shape[1]
-        NCHW = np.prod(self.outputs[0].shape)
-        # N x Cout x H x W x  (Cin x Kw x Kh)
-        return NCHW * (num_input * kw * kh)
-
 
 class ConvolutionBackwardData(OpNode):
     type = "ConvTranspose"
@@ -343,17 +353,41 @@ class ConvBiasForward(OpNode):
         obj.params["dtype"] = opr.outputs[0].dtype
         return obj
 
-    def calc_flops(self):
-        param_W_shape = self.inputs[1].shape
-        kh = param_W_shape[-2]
-        kw = param_W_shape[-1]
-        if len(param_W_shape) == 5:
-            num_input = param_W_shape[2]
-        else:
-            num_input = param_W_shape[1]
-        NCHW = np.prod(self.outputs[0].shape)
-        # N x Cout x H x W x  (Cin x Kw x Kh + bias)
-        return NCHW * (num_input * kw * kh + 1)
+
+@register_flops(
+    ConvolutionForward, ConvBiasForward,
+)
+def flops_conv(opnode: ConvolutionForward, inputs, outputs):
+    param_W_shape = inputs[1].shape
+    kh = param_W_shape[-2]
+    kw = param_W_shape[-1]
+    if len(param_W_shape) == 5:
+        num_input = param_W_shape[2]
+    else:
+        num_input = param_W_shape[1]
+    NCHW = np.prod(outputs[0].shape)
+    bias = 1 if isinstance(opnode, ConvBiasForward) else 0
+    # N x Cout x H x W x  (Cin x Kw x Kh)
+    return NCHW * (num_input * kw * kh + bias)
+
+
+@register_receptive_field(ConvolutionForward, ConvBiasForward)
+def receptive_field(opnode: ConvolutionForward, inputs, outputs):
+    pre_rf, pre_stride = preprocess_receptive_field(opnode, inputs, outputs)
+    param_W_shape = inputs[1].shape
+    kh = param_W_shape[-2]
+    kw = param_W_shape[-1]
+    rf = (
+        kh * pre_stride[0] + pre_rf[0] - pre_stride[0],
+        kw * pre_stride[1] + pre_rf[1] - pre_stride[1],
+    )
+    stride = (
+        opnode.params["stride_h"] * pre_stride[0],
+        opnode.params["stride_w"] * pre_stride[1],
+    )
+    opnode._rf = rf
+    opnode._stride = stride
+    return rf, stride
 
 
 class BatchConvBiasForward(OpNode):
@@ -652,20 +686,6 @@ class AssertEqual(OpNode):
     opdef = builtin.AssertEqual
 
 
-class ElemwiseMultiType(OpNode):
-    type = "ElemwiseMultiType"
-    opdef = builtin.ElemwiseMultiType
-
-    @classmethod
-    def load(cls, opr):
-        obj = super(ElemwiseMultiType, cls).load(opr)
-        obj.params["dtype"] = opr.outputs[0].dtype
-        return obj
-
-    def calc_flops(self):
-        return np.prod(self.outputs[0].shape)
-
-
 class CvtColorForward(OpNode):
     type = "CvtColor"
     opdef = builtin.CvtColor