frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-21 21:49:24 +08:00
Code Issues Packages Projects Releases Wiki Activity
Files
abf91562bd23af88b5c0849b46239135ad253295
pytorch/functorch/_src/compilers.py
Edward Z. Yang abf91562bd Change aot_module_simplified to take take arguments directly (#89669) 
This is extracted from voz's #89392

Previously, the implementation did some half-assed caching where it
returned a callable, that when invoked for the first time, actually
performed the compilation.  Delaying the compilation like this...
seems totally unnecessary?  To make matters worse, this has cost
(we have to check if we hit the cache) and unsound (because the
compiled function may not be valid for other arguments.)

So instead, we ask user to provide arguments, and compile everything
immediately.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/89669
Approved by: https://github.com/voznesenskym, https://github.com/Chillee
2022-11-28 18:39:15 +00:00

407 lines
13 KiB
Python
Raw Blame History

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
import torch.fx as fx
import torch.nn as nn
from torch._decomp import get_decompositions
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
# 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)
@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
@make_boxed_compiler
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 partial(_draw_graph_compile, name=name)
@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_node(self, n):
# TODO: This will fail once we start caching in AOTAutograd
# again, because we need to remap SymInts to their new values
# in the presence of dynamism
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)
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
assert nv.size() == rv.size(), f"output {i}: {nv.size()} != {rv.size()}"
assert nv.dtype == rv.dtype, f"output {i}: {nv.dtype} != {rv.dtype}"
assert torch._prims_common.check_significant_strides(nv, rv), f"output {i}: {nv.stride()} != {rv.stride()}"
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
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:
logging.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)
Reference in New Issue View Git Blame Copy Permalink