Source code for torch._functorch.compilers
import copy
import logging
import os
import pickle
import random
from contextlib import contextmanager
from functools import partial
from typing import Callable, Optional, Tuple, Union
import torch
from torch import SymInt
import torch.fx as fx
import torch.nn as nn
from torch._decomp import get_decompositions
from torch.fx.experimental.symbolic_shapes import bind_symbols
from .aot_autograd import aot_function, aot_module, make_boxed_compiler
from .compile_utils import strip_overloads
from .partitioners import (
default_partition,
draw_graph,
min_cut_rematerialization_partition,
)
import torch.utils._pytree as pytree
log = logging.getLogger(__name__)
# These canonicalizations are needed here (and not decompositions), as the ops
# we're trying to canonicalize to CompositeImplicitAutograd.
def _canonicalize(fx_g):
for node in fx_g.graph.nodes:
if node.target == torch.ops.aten._to_copy:
node.target = torch.ops.aten.to
fx_g.recompile()
return fx_g
@contextmanager
def _disable_jit_autocast():
old_jit_autocast_flag = torch._C._jit_set_autocast_mode(False)
try:
yield
finally:
torch._C._jit_set_autocast_mode(old_jit_autocast_flag)
[docs]@make_boxed_compiler
def ts_compile(fx_g: fx.GraphModule, inps) -> Callable:
"""
Compiles the :attr:`fx_g` with Torchscript compiler.
.. warning::
This API is experimental and likely to change.
Args:
fx_g(fx.GraphModule): The input Fx graph module to be compiled.
Returns:
Torch scripted model.
"""
with _disable_jit_autocast():
strip_overloads(fx_g)
for node in fx_g.graph.nodes:
if (
node.target == torch.ops.aten._to_copy
and len(node.args) == 1
and len(node.kwargs) == 1
and "dtype" in node.kwargs
):
node.target = torch.ops.aten.to
for node in fx_g.graph.nodes:
new_kwargs = {}
for k, v in node.kwargs.items():
if isinstance(v, torch.device):
v = v.type
new_kwargs[k] = v
node.kwargs = new_kwargs
fx_g.graph.lint()
fx_g.recompile()
f = torch.jit.script(fx_g)
torch._C._jit_pass_remove_mutation(f.graph)
f = torch.jit.freeze(f.eval())
f = torch.jit.optimize_for_inference(f)
if not any(isinstance(t, torch._subclasses.FakeTensor) for t in inps):
f(*inps)
return f
def _draw_graph_compile(fx_g, _, name, clear_meta=True):
print(fx_g.code)
draw_graph(fx_g, name, clear_meta=clear_meta)
return fx_g
def draw_graph_compile(name):
return make_boxed_compiler(
partial(_draw_graph_compile, name=name)
)
[docs]@make_boxed_compiler
def nop(fx_g: fx.GraphModule, _) -> Callable:
"""
Returns the :attr:`fx_g` Fx graph module as it is. This is a no-op compiler
and can be used to check accuracy.
.. warning::
This API is experimental and likely to change.
"""
return fx_g
class DebugInterpreter(fx.Interpreter):
def run(self, *args):
self.symbol_mapping = bind_symbols(self.module, *args)
super().run(*args)
def run_node(self, n):
import sympy
def subst_symint(ni):
if not isinstance(ni, SymInt):
return ni
r = sympy.expand(ni.node.expr.xreplace(self.symbol_mapping))
assert len(r.free_symbols) == 0, r
return int(r)
def subst_symint_tuple(nis):
return tuple(subst_symint(ni) for ni in nis)
def check_significant_strides(a, b):
if subst_symint(a.numel()) > 0:
for idx in range(a.ndim):
if subst_symint(a.stride(idx)) != b.stride(idx) and subst_symint(a.size(idx)) > 1:
return False
return True
def check(nv, rv, desc):
assert callable(desc)
assert nv.dtype == rv.dtype, f"{desc()}: {nv.dtype} != {rv.dtype}"
assert subst_symint_tuple(nv.size()) == rv.size(), \
f"{desc()}: {nv.size()} aka {subst_symint_tuple(nv.size())} != {rv.size()}"
same_strides = check_significant_strides(nv, rv)
assert same_strides, f"{desc()}: {nv.stride()} aka {subst_symint_tuple(nv.stride())} != {rv.stride()}"
r = super().run_node(n)
if 'val' in n.meta:
n_vals, n_spec = pytree.tree_flatten(n.meta['val'])
r_vals, r_spec = pytree.tree_flatten(r)
# TODO: There is some sort of problem where we record that an
# operator returned a tuple/list, and then later it turns out the
# real version of the operator returned a list/tuple. Need to
# figure out what's actually going on here, the error itself is
# harmless enough as we only getitem out the outputs.
# assert n_spec == r_spec, f"{n_spec} != {r_spec}"
assert len(n_vals) == len(r_vals), f"{len(n_vals)} != {len(r_vals)}"
for i, nv, rv in zip(range(len(n_vals)), n_vals, r_vals):
if not isinstance(rv, torch.Tensor):
continue
check(nv, rv, lambda: f"output {i} where {self.symbol_mapping}")
return r
@make_boxed_compiler
def debug_nop(fx_g: fx.GraphModule, _) -> Callable:
"""
Returns a (slow) interpreter over the FX graph module that also checks
various debugging properties (e.g., that tracing strides matched real
strides.)
"""
return DebugInterpreter(fx_g).run
@make_boxed_compiler
def simple_ts_compile(fx_g, _):
strip_overloads(fx_g)
f = torch.jit.script(fx_g)
f = torch.jit.freeze(f.eval())
return f
def nnc_jit(f, static_argnums=None):
return aot_function(f, simple_ts_compile, static_argnums=static_argnums)
aten = torch.ops.aten
default_decompositions = {
aten.detach,
aten.gelu_backward,
aten.leaky_relu_backward,
aten.sigmoid_backward,
aten.threshold_backward,
aten.hardtanh_backward,
aten.hardsigmoid_backward,
aten.hardswish_backward,
aten.tanh_backward,
aten.silu_backward,
aten.elu_backward,
aten.cudnn_batch_norm,
aten.cudnn_batch_norm_backward,
aten.masked_fill.Scalar,
aten.masked_fill.Tensor,
aten.elu,
aten.leaky_relu,
aten.hardtanh,
aten.hardswish,
aten.hardsigmoid,
aten.conj_physical,
aten.is_same_size,
}
default_decompositions = get_decompositions(default_decompositions)
@make_boxed_compiler
def print_compile(fx_g, _):
print(fx_g.code)
return fx_g
[docs]def memory_efficient_fusion(
fn: Union[Callable, nn.Module],
static_argnums: Optional[Tuple[int]] = None,
**kwargs,
):
"""
Wrapper function over :func:`aot_function` and :func:`aot_module` to perform
memory efficient fusion. It uses the
:func:`min_cut_rematerialization_partition` partitioner to perform efficient
recomputation. It uses NVFuser to compile the generated forward and backward
graphs.
.. warning::
This API is experimental and likely to change.
Args:
fn (Union[Callable, nn.Module]): A Python function or a ``nn.Module``
that takes one ore more arguments. Must return one or more Tensors.
static_argnums (Optional[Tuple[Int]]): An option tuple of ints to mark
the arguments of the function as static.
**kwargs: Any other overrides you want to make to the settings
Returns:
Returns a ``Callable`` or ``nn.Module`` that retains the eager behavior
of the original :attr:`fn`, but whose forward and backward graphs have
gone through recomputation optimizations, and the graphs have been
compiled with nvfuser.
"""
config = {
"fw_compiler": ts_compile,
"bw_compiler": ts_compile,
"partition_fn": min_cut_rematerialization_partition,
"decompositions": default_decompositions,
"static_argnums": static_argnums,
}
config.update(kwargs)
if isinstance(fn, torch.nn.Module):
return aot_module(fn, **config)
else:
return aot_function(fn, **config)
def debug_compile(fx_g, inps):
fx_g.to_folder("foo")
print(
f"""
##############################################################
# To minimize FX graph, copy and paste the below and run it #
##############################################################
import torch
import torch.fx as fx
from functorch.compile import minifier, check_nvfuser_subprocess, check_nvfuser_correctness_subprocess
inps = {[(i.shape, i.dtype) for i in inps]}
inps = [torch.ones(shape, dtype=dtype, device='cuda') for (shape, dtype) in inps]
from foo import FxModule
mod = FxModule().cuda()
with torch.jit.fuser("fuser2"):
# check_nvfuser_subprocess can be replaced with check_nvfuser_correctness_subprocess
minifier(fx.symbolic_trace(mod), inps, check_nvfuser_subprocess)
"""
)
from foo import FxModule
FxModule().cuda()(*inps)
return ts_compile(fx_g, inps)
graph_index = 0
def get_inputs(input_data_path):
"""
Return a random input for the given inputs meta generated from _save_fx_default.
"""
inputs = []
with (open(input_data_path, "rb")) as f:
inputs_meta = pickle.load(f)
inputs = []
for meta in inputs_meta:
if len(meta) == 1:
type = meta
input = type(random.rand())
else:
type, shape, stride, dtype, device = meta
if dtype in {
torch.int,
torch.int32,
torch.int64,
torch.bool,
torch.int,
torch.uint8,
int,
float,
}:
input = torch.randint(0, 1, shape, dtype=dtype, device=device)
else:
input = torch.rand(shape, dtype=dtype, device=device)
inputs.append(input)
return inputs
def _save_fx_default(current_name, folder_name, dump_example_input, gm, example_inputs):
"""
The forward, backward, and joint computation graph will be stored in
{folder_name}/{current_name}/{current_name}_forward_{graph_index},
{folder_name}/{current_name}/{current_name}_backward_{graph_index}, and
{folder_name}/{current_name}/{current_name}_joint_{graph_index} respectively.
The input shape of the graphs will be stored in the .input files.
These files can be loaded with pickle,
and is a list of format (type, shape, stride, dtype, device).
In the case of type = int or float, it is just (type,).
For joint graph input, it is a nested list [[],[]]
where the two inner lists have the same format.
If dump_example_input is True, example_inputs will be stored in .pt file.
Since each function might produce multiple graphs,
the graph_index is used to distinguish difference graphs
"""
from functorch.compile import aot_module_simplified
def get_input_meta(args):
input_meta = []
if len(args) > 0 and isinstance(args[0], tuple): # joint input
input_meta += get_input_meta(args[0])
input_meta += get_input_meta(args[1])
return input_meta
for arg in args:
if type(arg) == int or type(arg) == float:
input_meta.append((type(arg),))
else:
input_meta.append(
(type(arg), arg.shape, arg.stride(), arg.dtype, arg.device)
)
return input_meta
def graph_saver_helper(gm_to_save, args, type_name):
global graph_index
if len(gm_to_save.graph.nodes) == 0:
log.log(
logging.WARNING,
f"No nodes in graph {current_name}_{type_name}_{graph_index}.",
)
return
gm = copy.deepcopy(gm_to_save)
gm.graph.set_codegen(torch.fx.graph.CodeGen()) # remove codegen
gm.recompile()
input_meta = get_input_meta(args)
isExist = os.path.exists(f"{folder_name}/{current_name}")
if not isExist:
os.makedirs(f"{folder_name}/{current_name}")
gm.to_folder(
f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}"
)
pickle.dump(
input_meta,
open(
f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}/{current_name}_{type_name}_{graph_index}.input", # noqa: B950
"wb",
),
) # noqa: E501
if dump_example_input:
torch.save(
args,
f"{folder_name}/{current_name}/{current_name}_{type_name}_{graph_index}/{current_name}_{type_name}_{graph_index}.pt", # noqa: B950
) # noqa: E501
def graph_saver_forward(gm, fw_args):
graph_saver_helper(gm, fw_args, "forward")
return gm
def graph_saver_backward(gm, bw_args):
graph_saver_helper(gm, bw_args, "backward")
global graph_index
graph_index += 1
return gm
def graph_saver_joint(gm, joint_args):
graph_saver_helper(gm, joint_args, "joint")
return default_partition(gm, joint_args)
return aot_module_simplified(
gm,
example_inputs,
fw_compiler=graph_saver_forward,
bw_compiler=graph_saver_backward,
partition_fn=graph_saver_joint,
decompositions=default_decompositions,
)
# WARNING: This isn't tested anywhere!!
def graph_dumper_aot(current_name, folder_name, dump_example_input=False):
"""
Dump the forward, backward, and joint computation graph.
Example Usage:
save_fx_func = graph_dumper_aot(current_name, folder_name, dump_example_input = False)
optimize_ctx = torchdynamo.optimize(
save_fx_func
)
with torch.enable_grad():
with optimize_ctx:
result = forward_and_backward_pass(model, example_inputs)
"""
global graph_index
graph_index = 0
return partial(_save_fx_default, current_name, folder_name, dump_example_input)
