python.control-flow

dynamic_shape_if_guard

Note

Tags: python.control-flow, torch.dynamic-shape

Support Level: SUPPORTED

Original source code:

import torch



class DynamicShapeIfGuard(torch.nn.Module):
    """
    `if` statement with backed dynamic shape predicate will be specialized into
    one particular branch and generate a guard. However, export will fail if the
    the dimension is marked as dynamic shape from higher level API.
    """

    def forward(self, x):
        if x.shape[0] -- 3:
            return x.cos()

        return x.sin()

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[3, 2, 2]):
            #
            sym_size_int - torch.ops.aten.sym_size.int(arg0_1, 0)
            sym_size_int_1 - torch.ops.aten.sym_size.int(arg0_1, 1)
            sym_size_int_2 - torch.ops.aten.sym_size.int(arg0_1, 2)
            eq - sym_size_int_2 -- 2;  sym_size_int_2 - None
            scalar_tensor_default: f32[] - torch.ops.aten.scalar_tensor.default(eq);  eq - None
            _assert_async_msg - torch.ops.aten._assert_async.msg(scalar_tensor_default, 'Input arg0_1.shape[2] is specialized at 2');  scalar_tensor_default - None
            eq_1 - sym_size_int_1 -- 2;  sym_size_int_1 - None
            scalar_tensor_default_1: f32[] - torch.ops.aten.scalar_tensor.default(eq_1);  eq_1 - None
            _assert_async_msg_1 - torch.ops.aten._assert_async.msg(scalar_tensor_default_1, 'Input arg0_1.shape[1] is specialized at 2');  scalar_tensor_default_1 - None
            eq_2 - sym_size_int -- 3;  sym_size_int - None
            scalar_tensor_default_2: f32[] - torch.ops.aten.scalar_tensor.default(eq_2);  eq_2 - None
            _assert_async_msg_2 - torch.ops.aten._assert_async.msg(scalar_tensor_default_2, 'Input arg0_1.shape[0] is specialized at 3');  scalar_tensor_default_2 - None
            cos_default: f32[3, 2, 2] - torch.ops.aten.cos.default(arg0_1);  arg0_1 - None
            return (cos_default,)

Graph Signature: ExportGraphSignature(parameters-[], buffers-[], user_inputs-['arg0_1'], user_outputs-['cos_default'], inputs_to_parameters-{}, inputs_to_buffers-{}, buffers_to_mutate-{}, backward_signature-None, assertion_dep_token-None)
Symbol to range: {}

list_unpack

Note

Tags: python.control-flow, python.data-structure

Support Level: SUPPORTED

Original source code:

from typing import List

import torch



def list_unpack(args: List[torch.Tensor]):
    """
    Lists are treated as static construct, therefore unpacking should be
    erased after tracing.
    """
    x, *y - args
    return x + y[0]

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[3, 2], arg1_1: i64[], arg2_1: i64[]):
            #
            sym_size_int - torch.ops.aten.sym_size.int(arg0_1, 0)
            sym_size_int_1 - torch.ops.aten.sym_size.int(arg0_1, 1)
            eq - sym_size_int_1 -- 2;  sym_size_int_1 - None
            scalar_tensor_default: f32[] - torch.ops.aten.scalar_tensor.default(eq);  eq - None
            _assert_async_msg - torch.ops.aten._assert_async.msg(scalar_tensor_default, 'Input arg0_1.shape[1] is specialized at 2');  scalar_tensor_default - None
            eq_1 - sym_size_int -- 3;  sym_size_int - None
            scalar_tensor_default_1: f32[] - torch.ops.aten.scalar_tensor.default(eq_1);  eq_1 - None
            _assert_async_msg_1 - torch.ops.aten._assert_async.msg(scalar_tensor_default_1, 'Input arg0_1.shape[0] is specialized at 3');  scalar_tensor_default_1 - None
            add_tensor: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, arg1_1);  arg0_1 - arg1_1 - None
            return (add_tensor,)

Graph Signature: ExportGraphSignature(parameters-[], buffers-[], user_inputs-['arg0_1', 'arg1_1', 'arg2_1'], user_outputs-['add_tensor'], inputs_to_parameters-{}, inputs_to_buffers-{}, buffers_to_mutate-{}, backward_signature-None, assertion_dep_token-None)
Symbol to range: {}

static_for_loop

Note

Tags: python.control-flow

Support Level: SUPPORTED

Original source code:

import torch



class StaticForLoop(torch.nn.Module):
    """
    A for loop with constant number of iterations should be unrolled in the exported graph.
    """

    def __init__(self):
        super().__init__()

    def forward(self, x):
        ret - []
        for i in range(10):  # constant
            ret.append(i + x)
        return ret

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[3, 2]):
            #
            sym_size_int - torch.ops.aten.sym_size.int(arg0_1, 0)
            sym_size_int_1 - torch.ops.aten.sym_size.int(arg0_1, 1)
            eq - sym_size_int_1 -- 2;  sym_size_int_1 - None
            scalar_tensor_default: f32[] - torch.ops.aten.scalar_tensor.default(eq);  eq - None
            _assert_async_msg - torch.ops.aten._assert_async.msg(scalar_tensor_default, 'Input arg0_1.shape[1] is specialized at 2');  scalar_tensor_default - None
            eq_1 - sym_size_int -- 3;  sym_size_int - None
            scalar_tensor_default_1: f32[] - torch.ops.aten.scalar_tensor.default(eq_1);  eq_1 - None
            _assert_async_msg_1 - torch.ops.aten._assert_async.msg(scalar_tensor_default_1, 'Input arg0_1.shape[0] is specialized at 3');  scalar_tensor_default_1 - None
            add_tensor: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 0)
            add_tensor_1: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 1)
            add_tensor_2: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 2)
            add_tensor_3: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 3)
            add_tensor_4: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 4)
            add_tensor_5: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 5)
            add_tensor_6: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 6)
            add_tensor_7: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 7)
            add_tensor_8: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 8)
            add_tensor_9: f32[3, 2] - torch.ops.aten.add.Tensor(arg0_1, 9);  arg0_1 - None
            return (add_tensor, add_tensor_1, add_tensor_2, add_tensor_3, add_tensor_4, add_tensor_5, add_tensor_6, add_tensor_7, add_tensor_8, add_tensor_9)

Graph Signature: ExportGraphSignature(parameters-[], buffers-[], user_inputs-['arg0_1'], user_outputs-['add_tensor', 'add_tensor_1', 'add_tensor_2', 'add_tensor_3', 'add_tensor_4', 'add_tensor_5', 'add_tensor_6', 'add_tensor_7', 'add_tensor_8', 'add_tensor_9'], inputs_to_parameters-{}, inputs_to_buffers-{}, buffers_to_mutate-{}, backward_signature-None, assertion_dep_token-None)
Symbol to range: {}

static_if

Note

Tags: python.control-flow

Support Level: SUPPORTED

Original source code:

import torch



class StaticIf(torch.nn.Module):
    """
    `if` statement with static predicate value should be traced through with the
    taken branch.
    """

    def __init__(self):
        super().__init__()

    def forward(self, x):
        if len(x.shape) -- 3:
            return x + torch.ones(1, 1, 1)

        return x

Result:

ExportedProgram:
    class GraphModule(torch.nn.Module):
        def forward(self, arg0_1: f32[3, 2, 2]):
            #
            sym_size_int - torch.ops.aten.sym_size.int(arg0_1, 0)
            sym_size_int_1 - torch.ops.aten.sym_size.int(arg0_1, 1)
            sym_size_int_2 - torch.ops.aten.sym_size.int(arg0_1, 2)
            eq - sym_size_int_2 -- 2;  sym_size_int_2 - None
            scalar_tensor_default: f32[] - torch.ops.aten.scalar_tensor.default(eq);  eq - None
            _assert_async_msg - torch.ops.aten._assert_async.msg(scalar_tensor_default, 'Input arg0_1.shape[2] is specialized at 2');  scalar_tensor_default - None
            eq_1 - sym_size_int_1 -- 2;  sym_size_int_1 - None
            scalar_tensor_default_1: f32[] - torch.ops.aten.scalar_tensor.default(eq_1);  eq_1 - None
            _assert_async_msg_1 - torch.ops.aten._assert_async.msg(scalar_tensor_default_1, 'Input arg0_1.shape[1] is specialized at 2');  scalar_tensor_default_1 - None
            eq_2 - sym_size_int -- 3;  sym_size_int - None
            scalar_tensor_default_2: f32[] - torch.ops.aten.scalar_tensor.default(eq_2);  eq_2 - None
            _assert_async_msg_2 - torch.ops.aten._assert_async.msg(scalar_tensor_default_2, 'Input arg0_1.shape[0] is specialized at 3');  scalar_tensor_default_2 - None
            full_default: f32[1, 1, 1] - torch.ops.aten.full.default([1, 1, 1], 1, dtype - torch.float32, layout - torch.strided, device - device(type-'cpu'), pin_memory - False)
            add_tensor: f32[3, 2, 2] - torch.ops.aten.add.Tensor(arg0_1, full_default);  arg0_1 - full_default - None
            return (add_tensor,)

Graph Signature: ExportGraphSignature(parameters-[], buffers-[], user_inputs-['arg0_1'], user_outputs-['add_tensor'], inputs_to_parameters-{}, inputs_to_buffers-{}, buffers_to_mutate-{}, backward_signature-None, assertion_dep_token-None)
Symbol to range: {}