[export] Temporarily bypass torch_fn in partitioner

[yushangdi]

Summary:
"torch_fn" is not correct for the decomposed add node from batch norm. This is a temporary workaround to bypass torch fn.

For example, for the graph below (test_qat_conv2d_unary graph):

graph():
    %conv_weight : [num_users=1] = get_attr[target=conv.weight]
    %bn_weight : [num_users=1] = get_attr[target=bn.weight]
    %bn_bias : [num_users=1] = get_attr[target=bn.bias]
    %bn_running_mean : [num_users=1] = get_attr[target=bn.running_mean]
    %bn_running_var : [num_users=1] = get_attr[target=bn.running_var]
    %bn_num_batches_tracked : [num_users=1] = get_attr[target=bn.num_batches_tracked]
    %x : [num_users=1] = placeholder[target=x]
    %conv2d : [num_users=1] = call_function[target=torch.ops.aten.conv2d.default](args = (%x, %conv_weight, None, [1, 1], [1, 1]), kwargs = {})
    %add_ : [num_users=0] = call_function[target=torch.ops.aten.add_.Tensor](args = (%bn_num_batches_tracked, 1), kwargs = {})
    %batch_norm : [num_users=1] = call_function[target=torch.ops.aten.batch_norm.default](args = (%conv2d, %bn_weight, %bn_bias, %bn_running_mean, %bn_running_var, True, 0.1, 1e-05, True), kwargs = {})
    %relu : [num_users=1] = call_function[target=torch.ops.aten.relu.default](args = (%batch_norm,), kwargs = {})
    %max_pool2d : [num_users=1] = call_function[target=torch.ops.aten.max_pool2d.default](args = (%relu, [3, 3], [3, 3]), kwargs = {})
    return (max_pool2d,)

the add_ node has 'torch_fn': ('add__1', 'method_descriptor.add_'), in its meta.

If we run the line below in _annotate_qat_conv2d_bn_binary_unary, we'll have a partition without output nodes.

 find_sequential_partitions(
            gm, [torch.nn.Conv2d, torch.nn.BatchNorm2d, operator.add, torch.nn.ReLU]
        )

partition_list
[
SourcePartition(nodes=[conv_weight, conv2d], source=<class 'torch.nn.modules.conv.Conv2d'>, input_nodes=[x], output_nodes=[conv2d], params=[conv_weight]),

SourcePartition(nodes=[bn_weight, bn_bias, bn_running_mean, bn_running_var, bn_num_batches_tracked, add_, batch_norm], source=<class 'torch.nn.modules.batchnorm.BatchNorm2d'>, input_nodes=[conv2d], output_nodes=[batch_norm], params=[bn_num_batches_tracked, bn_running_var, bn_bias, bn_weight, bn_running_mean]),

SourcePartition(nodes=[add_], source='add_', input_nodes=[bn_num_batches_tracked], output_nodes=[], params=[])
]

We should not have the last partition.

Test Plan:

buck2 run 'fbcode//mode/dev-nosan' fbcode//caffe2/test/quantization:test_quantization -- -r test_qat_conv2d

Differential Revision: D61569049

[pytorch-bot]

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✅ You can merge normally! (1 Unrelated Failure)

As of commit c1b4102 with merge base 78d69bf ():

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• trunk / linux-focal-rocm6.1-py3.8 / test (default, 1, 2, linux.rocm.gpu) (gh) (trunk failure)
  inductor/test_torchinductor.py::CpuTests::test_mutable_custom_op_fixed_layout2_cpu

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[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D61569049

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D61569049

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D61569049

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D61569049

[facebook-github-bot]

@pytorchbot merge -f 'Landed internally'

(Initiating merge automatically since Phabricator Diff has merged, using force because this PR might not pass merge_rules.json but landed internally)

[pytorchmergebot]

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