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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
from typing import Dict, List, Callable
import time
import gc
import torch
from torch.fx import Graph, GraphModule
try:
import torch.utils._pytree as pytree
import torch._dynamo
from functorch.compile import make_boxed_func
from torch._functorch.aot_autograd import aot_module_simplified
from torch._subclasses.fake_tensor import unset_fake_temporarily
from torch._subclasses.fake_tensor import is_fake
except ImportError:
pass
from deepspeed.accelerator import get_accelerator
from .fx import add_free_activations
from .graph_param import DSGraphParamManager
from .profilers import ProfilingResult
from .profilers.graph_profile import MemoryProfilingInterpreter
from .patch_compiled_func import patch_compiled_func, unpatch_compiled_func, get_backward_inputs
from .util import get_input_nodes, get_activation_node_names, get_index_by_graph_id, get_deepcompile_handle, log_rank0
from .partitioner import get_wrapped_partitioner
from .inductor import register_custom_ops, patch_create_aot_dispatcher_function
remaining_schedule = None
next_pass_step = -1
next_passes = None
current_passes = None
param_manager: Dict[int, DSGraphParamManager] = {}
class GraphOrder:
def __init__(self):
self.ordered_frames = []
self.frames = {}
def add_graph(self, graph_id, frame_id, needs_backward):
if frame_id not in self.ordered_frames:
self.ordered_frames.append(frame_id)
self.frames[frame_id] = (graph_id, needs_backward)
def get_graph_order(self):
return [self.frames[frame_id] for frame_id in self.ordered_frames]
def clear(self):
self.frames.clear()
graph_order_with_frame_id = GraphOrder()
frames_needing_bwd = set()
profiling_results: Dict[int, ProfilingResult] = {}
opt_pass_times = []
opt_passes = {}
fwd_real_inputs = []
def register_compile_pass(name: str, opt_pass_fn):
opt_passes[name] = opt_pass_fn
def init_schedule(schedule):
assert isinstance(schedule, list), f"schedule should be a list, but got {type(schedule)}"
for step, passes in schedule:
assert isinstance(step, int), f"Each step in schedule should be an integer, but got {type(step)}"
assert isinstance(passes, list), f"Passes at a certain step should be a list, but got {type(passes)}"
global remaining_schedule
remaining_schedule = schedule
def launch_compile_passes(global_steps: int):
global next_pass_step, next_passes
if len(remaining_schedule) > 0 and global_steps == remaining_schedule[0][0]:
_, next_passes = remaining_schedule.pop(0)
log_rank0(f"Launching compile passes: global_steps={global_steps} passes={next_passes}", True)
torch._dynamo.reset()
get_deepcompile_handle().reset()
graph_order_with_frame_id.clear()
profiling_results.clear()
param_manager.clear()
def set_time_and_tensor_size(graph_id, graph: Graph, mem, bwd, profiling_results):
node_time = []
tensor_sizes = []
for n in graph.nodes:
node_time.append((n.name, n.meta["device_time"] if "device_time" in n.meta else 0.0,
n.meta["wall_time"] if "wall_time" in n.meta else 0.0))
tensor_sizes.append((n.name, n.meta["tensor_size"] if "tensor_size" in n.meta else 0))
if bwd:
profiling_results[graph_id].bwd_graph = graph
profiling_results[graph_id].bwd_time = node_time
profiling_results[graph_id].bwd_tensor_sizes = tensor_sizes
profiling_results[graph_id].bwd_mem = mem
else:
profiling_results[graph_id].fwd_graph = graph
profiling_results[graph_id].fwd_time = node_time
profiling_results[graph_id].fwd_tensor_sizes = tensor_sizes
profiling_results[graph_id].fwd_mem = mem
def evaluate_symint_from_shape_env(sym_int_v):
assert isinstance(sym_int_v, torch.SymInt)
# shape_env = sym_int_v.node.shape_env
# v = shape_env.evaluate_sym_node(sym_int_v.node)
return sym_int_v.node.hint
def set_example_values_to_symints(real_inputs):
real_inputs_ret = []
for v in real_inputs:
if isinstance(v, torch.Tensor):
if is_fake(v):
shape = []
for fs in v.shape:
if isinstance(fs, torch.SymInt):
shape.append(evaluate_symint_from_shape_env(fs))
else:
shape.append(fs)
stride = []
for fs in v.stride():
if isinstance(fs, torch.SymInt):
stride.append(evaluate_symint_from_shape_env(fs))
else:
stride.append(fs)
with unset_fake_temporarily():
dummy_v = torch.ones(shape,
dtype=v.dtype,
layout=v.layout,
device=v.device,
requires_grad=v.requires_grad).as_strided(shape, stride)
real_inputs_ret.append(dummy_v)
else:
real_inputs_ret.append(v)
else:
if isinstance(v, torch.SymInt):
real_inputs_ret.append(evaluate_symint_from_shape_env(v))
else:
real_inputs_ret.append(v)
return tuple(real_inputs_ret)
def run_opt_passes(opt_passes: List[Callable],
gm: GraphModule,
graph_id: int,
graph_order: List[int],
profiling_results,
create_inputs_fn,
mem_budget: float,
param_manager,
bwd: bool,
debug_log=False) -> None:
with unset_fake_temporarily():
get_accelerator().synchronize()
gc.collect()
get_accelerator().empty_cache()
for i, opt_pass_fn in enumerate(opt_passes):
log_rank0(f"Running opt pass {i} for graph {graph_id}. bwd={bwd}", enable=debug_log)
gm_new = opt_pass_fn(gm, graph_id, graph_order, profiling_results, create_inputs_fn, mem_budget, param_manager,
bwd)
if gm_new is not None:
gm = gm_new
gm.graph.lint()
gm.recompile()
mem_prof = MemoryProfilingInterpreter(gm, debug_log=debug_log)
mem_prof.run(*create_inputs_fn())
mem = [(name, current_alloc, delta, peak) for name, current_alloc, delta, peak in mem_prof.mem_record]
set_time_and_tensor_size(graph_id, gm.graph, mem, bwd, profiling_results)
with unset_fake_temporarily():
get_accelerator().synchronize()
gc.collect()
get_accelerator().empty_cache()
def make_backend(backend, compile_kwargs={}, free_activation=False, debug_log=False):
register_custom_ops()
def backend_fn(gm: GraphModule, real_inputs):
graph_id = id(gm.graph)
needs_backward = pytree.tree_any(lambda x: x.requires_grad if torch.is_tensor(x) else False, real_inputs)
frame_id = gm.meta["dynamo_compile_id"].frame_id
graph_order_with_frame_id.add_graph(graph_id, frame_id, needs_backward)
if needs_backward:
if len(frames_needing_bwd) == 0:
patch_compiled_func()
frames_needing_bwd.add(frame_id)
graph_order = graph_order_with_frame_id.get_graph_order()
z3_partition = any(hasattr(v, "ds_id") for v in real_inputs)
if z3_partition:
param_indices = [(i, input_val.ds_id, input_val.ds_shape) for i, input_val in enumerate(real_inputs)
if isinstance(input_val, torch.nn.Parameter)]
else:
assert all(hasattr(v, "param_id") for v in real_inputs
if isinstance(v, torch.nn.Parameter)), "All param inputs should have param_id"
param_indices = [(i, input_val.param_id, input_val.shape) for i, input_val in enumerate(real_inputs)
if isinstance(input_val, torch.nn.Parameter)]
global fwd_real_inputs
fwd_real_inputs.append(real_inputs)
global profiling_results
if graph_id not in profiling_results:
profiling_results[graph_id] = ProfilingResult()
profiling_results[graph_id].param_indices = param_indices
profiling_results[graph_id].needs_backward = needs_backward
def make_fw_graph(gm, sample_inputs):
time_start = time.time()
graph_index = len(graph_order) - 1
real_inputs = fwd_real_inputs.pop(0)
real_inputs = set_example_values_to_symints(real_inputs)
param_manager[graph_id] = DSGraphParamManager(gm.graph, real_inputs, param_indices)
real_inputs_with_rng = real_inputs + tuple(sample_inputs[len(real_inputs):])
run_opt_passes(
opt_passes=next_passes,
gm=gm,
graph_id=graph_id,
graph_order=graph_order,
profiling_results=profiling_results,
create_inputs_fn=lambda: real_inputs_with_rng,
mem_budget=.0, # unused
param_manager=param_manager,
bwd=False,
debug_log=debug_log)
opt_pass_times.append(("fwd", graph_index, graph_id, time.time() - time_start))
log_rank0(f"Fwd end {graph_index} graph_id={graph_id} alloc_mem={get_accelerator().memory_allocated()}",
enable=debug_log)
return gm.graph
def make_bw_graph(gm, sample_inputs):
time_start = time.time()
graph_index = get_index_by_graph_id(graph_order, graph_id)
log_rank0(
f"Bwd start {graph_index} graph_id={graph_id} alloc_mem={get_accelerator().memory_allocated()} graph={gm.graph}",
enable=debug_log)
bwd_inputs_stack = get_backward_inputs()
if len(bwd_inputs_stack) == 0:
# dynamo calls bw compiler ahead of time when symints are saved for backward. See the details for aot_dispatch_autograd in jit_compile_runtime_wrappers.
# As we currently use actually bwd input values in bw compiler, we make dummy data for profiling.
bwd_real_inputs = set_example_values_to_symints(sample_inputs)
else:
bwd_real_inputs = bwd_inputs_stack.pop()
run_opt_passes(
opt_passes=next_passes,
gm=gm,
graph_id=graph_id,
graph_order=graph_order,
profiling_results=profiling_results,
create_inputs_fn=lambda: tuple(bwd_real_inputs),
mem_budget=.0, # unused
param_manager=param_manager,
bwd=True,
debug_log=debug_log)
# assert graph_id in param_manager, f"Graph {graph_id} not found in param_manager"
if free_activation:
param_nodes_bw, _ = param_manager[graph_id].get_bwd_mapping(gm.graph)
param_names = [n.name for n in param_nodes_bw]
non_param_input_names = [n.name for n in get_input_nodes(gm.graph) if n.name not in param_names]
add_free_activations(graph_id, gm.graph,
get_activation_node_names(gm.graph, param_nodes_bw, non_param_input_names))
frames_needing_bwd.remove(frame_id)
if len(frames_needing_bwd) == 0:
unpatch_compiled_func()
log_rank0(
f"Bwd end {graph_index} graph_id={graph_id} alloc_mem={get_accelerator().memory_allocated()} graph={gm.graph}",
enable=debug_log)
opt_pass_times.append(("bwd", graph_index, graph_id, time.time() - time_start))
return gm.graph
if backend == "eager":
def make_compiler_fn(make_graph_fn):
def compiler_fn(gm, sample_inputs):
return None if make_graph_fn(gm, sample_inputs) is None else make_boxed_func(gm.forward)
return compiler_fn
aot_mod = aot_module_simplified(gm,
real_inputs,
fw_compiler=make_compiler_fn(make_fw_graph),
bw_compiler=make_compiler_fn(make_bw_graph),
partition_fn=get_wrapped_partitioner(param_indices))
return torch._dynamo.optimize(**compile_kwargs)(aot_mod)
elif backend == "inductor":
patch_create_aot_dispatcher_function(graph_id, z3_partition, make_fw_graph, make_bw_graph, real_inputs,
param_indices, param_manager)
from .partitioner import get_wrapped_choose_saved_values_set
torch._functorch.partitioners.choose_saved_values_set = get_wrapped_choose_saved_values_set(param_indices)
return torch._inductor.compile(gm, real_inputs)
raise ValueError(f"Unsupported backend {backend}")
return backend_fn
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