ColossalAI/colossalai/fx/profiler/opcount.py

# adopted from https://github.com/facebookresearch/fvcore/blob/main/fvcore/nn/jit_handles.py
# ideas from https://pastebin.com/AkvAyJBw

from functools import reduce
import operator
from typing import Callable, List, Any
from numbers import Number
import torch

aten = torch.ops.aten


def matmul_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
    """
    Count flops for matmul.
    """
    # Inputs should be a list of length 2.
    # Inputs contains the shapes of two matrices.
    input_shapes = [v.shape for v in inputs]
    assert len(input_shapes) == 2, input_shapes
    assert input_shapes[0][-1] == input_shapes[1][-2], input_shapes
    flops = reduce(operator.mul, input_shapes[0]) * input_shapes[-1][-1]
    return flops


def addmm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
    """
    Count flops for fully connected layers.
    """
    # Count flop for nn.Linear
    # inputs is a list of length 3.
    input_shapes = [v.shape for v in inputs[1:3]]
    # input_shapes[0]: [batch size, input feature dimension]
    # input_shapes[1]: [batch size, output feature dimension]
    assert len(input_shapes[0]) == 2, input_shapes[0]
    assert len(input_shapes[1]) == 2, input_shapes[1]
    batch_size, input_dim = input_shapes[0]
    output_dim = input_shapes[1][1]
    flops = batch_size * input_dim * output_dim
    return flops


def linear_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
    """
    Count flops for the aten::linear operator.
    """
    # Inputs is a list of length 3; unlike aten::addmm, it is the first
    # two elements that are relevant.
    input_shapes = [v.shape for v in inputs[0:2]]
    # input_shapes[0]: [dim0, dim1, ..., input_feature_dim]
    # input_shapes[1]: [output_feature_dim, input_feature_dim]
    assert input_shapes[0][-1] == input_shapes[1][-1]
    flops = reduce(operator.mul, input_shapes[0]) * input_shapes[1][0]
    return flops


def bmm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
    """
    Count flops for the bmm operation.
    """
    # Inputs should be a list of length 2.
    # Inputs contains the shapes of two tensor.
    assert len(inputs) == 2, len(inputs)
    input_shapes = [v.shape for v in inputs]
    n, c, t = input_shapes[0]
    d = input_shapes[-1][-1]
    flops = n * c * t * d
    return flops


def conv_flop_count(
    x_shape: List[int],
    w_shape: List[int],
    out_shape: List[int],
    transposed: bool = False,
) -> Number:
    """
    Count flops for convolution. Note only multiplication is
    counted. Computation for addition and bias is ignored.
    Flops for a transposed convolution are calculated as
    flops = (x_shape[2:] * prod(w_shape) * batch_size).
    Args:
        x_shape (list(int)): The input shape before convolution.
        w_shape (list(int)): The filter shape.
        out_shape (list(int)): The output shape after convolution.
        transposed (bool): is the convolution transposed
    Returns:
        int: the number of flops
    """
    batch_size = x_shape[0]
    conv_shape = (x_shape if transposed else out_shape)[2:]
    flops = batch_size * reduce(operator.mul, w_shape) * reduce(operator.mul, conv_shape)
    return flops


def conv_flop_jit(inputs: List[Any], outputs: List[Any]):
    """
    Count flops for convolution.
    """
    x, w = inputs[:2]
    x_shape, w_shape, out_shape = (x.shape, w.shape, outputs[0].shape)
    transposed = inputs[6]

    return conv_flop_count(x_shape, w_shape, out_shape, transposed=transposed)


def transpose_shape(shape):
    return [shape[1], shape[0]] + list(shape[2:])


def conv_backward_flop_jit(inputs: List[Any], outputs: List[Any]):
    grad_out_shape, x_shape, w_shape = [i.shape for i in inputs[:3]]
    output_mask = inputs[-1]
    fwd_transposed = inputs[7]
    flop_count = 0

    if output_mask[0]:
        grad_input_shape = outputs[0].shape
        flop_count += conv_flop_count(grad_out_shape, w_shape, grad_input_shape, not fwd_transposed)
    if output_mask[1]:
        grad_weight_shape = outputs[1].shape
        flop_count += conv_flop_count(transpose_shape(x_shape), grad_out_shape, grad_weight_shape, fwd_transposed)

    return flop_count


def norm_flop_counter(affine_arg_index: int, input_arg_index: int) -> Callable:
    """
    Args:
        affine_arg_index: index of the affine argument in inputs
    """

    def norm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
        """
        Count flops for norm layers.
        """
        # Inputs[0] contains the shape of the input.
        input_shape = inputs[input_arg_index].shape

        has_affine = inputs[affine_arg_index].shape is not None if hasattr(inputs[affine_arg_index],
                                                                           'shape') else inputs[affine_arg_index]
        assert 2 <= len(input_shape) <= 5, input_shape
        # 5 is just a rough estimate
        flop = reduce(operator.mul, input_shape) * (5 if has_affine else 4)
        return flop

    return norm_flop_jit


def batchnorm_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
    training = inputs[-3]
    assert isinstance(training, bool), "Signature of aten::batch_norm has changed!"
    if training:
        return norm_flop_counter(1, 0)(inputs, outputs)    # pyre-ignore
    has_affine = inputs[1].shape is not None
    input_shape = reduce(operator.mul, inputs[0].shape)
    return input_shape * (2 if has_affine else 1)


def elementwise_flop_counter(input_scale: float = 1, output_scale: float = 0) -> Callable:
    """
    Count flops by
        input_tensor.numel() * input_scale + output_tensor.numel() * output_scale
    Args:
        input_scale: scale of the input tensor (first argument)
        output_scale: scale of the output tensor (first element in outputs)
    """

    def elementwise_flop(inputs: List[Any], outputs: List[Any]) -> Number:
        ret = 0
        if input_scale != 0:
            shape = inputs[0].shape
            ret += input_scale * reduce(operator.mul, shape) if shape else 0
        if output_scale != 0:
            shape = outputs[0].shape
            ret += output_scale * reduce(operator.mul, shape) if shape else 0
        return ret

    return elementwise_flop


def zero_flop_jit(*args):
    """
        Count flops for zero flop layers.
    """
    return 0


flop_mapping = {
    # gemm
    aten.mm.default: matmul_flop_jit,
    aten.matmul.default: matmul_flop_jit,
    aten.addmm.default: addmm_flop_jit,
    aten.bmm.default: bmm_flop_jit,

    # convolution
    aten.convolution.default: conv_flop_jit,
    aten._convolution.default: conv_flop_jit,
    aten.convolution_backward.default: conv_backward_flop_jit,

    # normalization
    aten.native_batch_norm.default: batchnorm_flop_jit,
    aten.native_batch_norm_backward.default: batchnorm_flop_jit,
    aten.native_layer_norm.default: norm_flop_counter(2, 0),
    aten.native_layer_norm_backward.default: norm_flop_counter(2, 0),

    # pooling
    aten.avg_pool1d.default: elementwise_flop_counter(1, 0),
    aten.avg_pool2d.default: elementwise_flop_counter(1, 0),
    aten.avg_pool2d_backward.default: elementwise_flop_counter(0, 1),
    aten.avg_pool3d.default: elementwise_flop_counter(1, 0),
    aten.avg_pool3d_backward.default: elementwise_flop_counter(0, 1),
    aten.max_pool1d.default: elementwise_flop_counter(1, 0),
    aten.max_pool2d.default: elementwise_flop_counter(1, 0),
    aten.max_pool3d.default: elementwise_flop_counter(1, 0),
    aten.max_pool1d_with_indices.default: elementwise_flop_counter(1, 0),
    aten.max_pool2d_with_indices.default: elementwise_flop_counter(1, 0),
    aten.max_pool2d_with_indices_backward.default: elementwise_flop_counter(0, 1),
    aten.max_pool3d_with_indices.default: elementwise_flop_counter(1, 0),
    aten.max_pool3d_with_indices_backward.default: elementwise_flop_counter(0, 1),
    aten._adaptive_avg_pool2d.default: elementwise_flop_counter(1, 0),
    aten._adaptive_avg_pool2d_backward.default: elementwise_flop_counter(0, 1),
    aten._adaptive_avg_pool3d.default: elementwise_flop_counter(1, 0),
    aten._adaptive_avg_pool3d_backward.default: elementwise_flop_counter(0, 1),
}

elementwise_flop_aten = [
    # basic op
    aten.add.Tensor,
    aten.add_.Tensor,
    aten.div.Tensor,
    aten.div_.Tensor,
    aten.div.Scalar,
    aten.div_.Scalar,
    aten.mul.Tensor,
    aten.mul.Scalar,
    aten.mul_.Tensor,
    aten.neg.default,
    aten.pow.Tensor_Scalar,
    aten.rsub.Scalar,
    aten.sum.default,
    aten.sum.dim_IntList,
    aten.mean.dim,

    # activation op
    aten.hardswish.default,
    aten.hardswish_.default,
    aten.hardswish_backward.default,
    aten.hardtanh_.default,
    aten.hardtanh_backward.default,
    aten.hardsigmoid_backward.default,
    aten.hardsigmoid.default,
    aten.gelu.default,
    aten.gelu_backward.default,
    aten.silu_.default,
    aten.silu_backward.default,
    aten.sigmoid.default,
    aten.sigmoid_backward.default,
    aten._softmax.default,
    aten._softmax_backward_data.default,
    aten.relu_.default,
    aten.relu.default,
    aten.tanh.default,
    aten.tanh_backward.default,
    aten.threshold_backward.default,
]

for op in elementwise_flop_aten:
    flop_mapping[op] = elementwise_flop_counter(1, 0)

# TODO: this will be removed in future
zero_flop_aten = [
    aten.as_strided.default,
    aten.as_strided_.default,
    aten.bernoulli_.float,
    aten.cat.default,
    aten.clone.default,
    aten.copy_.default,
    aten.detach.default,
    aten.expand.default,
    aten.empty_like.default,
    aten.new_empty.default,
    aten.new_empty_strided.default,
    aten.ones_like.default,
    aten._reshape_alias.default,
    aten.select.int,
    aten.select_backward.default,
    aten.squeeze.dim,
    aten.slice.Tensor,
    aten.slice_backward.default,
    aten.split.Tensor,
    aten.permute.default,
    aten.t.default,
    aten.transpose.int,
    aten._to_copy.default,
    aten.unsqueeze.default,
    aten._unsafe_view.default,
    aten.view.default,
    aten.where.self,
    aten.zero_.default,
]

for op in zero_flop_aten:
    flop_mapping[op] = zero_flop_jit