Source code for fbgemm_gpu.split_table_batched_embeddings_ops_training
#!/usr/bin/env python3
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
# pyre-strict
# pyre-ignore-all-errors[56]
import contextlib
import enum
import functools
import logging
import math
import os
import uuid
from dataclasses import dataclass, field
from itertools import accumulate
from math import log2
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
import torch # usort:skip
from torch import nn, Tensor # usort:skip
import fbgemm_gpu.split_embedding_codegen_lookup_invokers as invokers
# from fbgemm_gpu.config import FeatureGateName
from fbgemm_gpu.runtime_monitor import (
AsyncSeriesTimer,
TBEStatsReporter,
TBEStatsReporterConfig,
)
from fbgemm_gpu.split_embedding_configs import EmbOptimType as OptimType, SparseType
from fbgemm_gpu.split_table_batched_embeddings_ops_common import (
BoundsCheckMode,
CacheAlgorithm,
CacheState,
construct_cache_state,
EmbeddingLocation,
MAX_PREFETCH_DEPTH,
MultiPassPrefetchConfig,
PoolingMode,
RecordCacheMetrics,
SplitState,
)
try:
if torch.version.hip:
torch.ops.load_library(
"//deeplearning/fbgemm/fbgemm_gpu/codegen:embedding_ops_hip_training"
)
else:
torch.ops.load_library(
"//deeplearning/fbgemm/fbgemm_gpu/codegen:embedding_ops_cuda_training"
)
torch.ops.load_library(
"//deeplearning/fbgemm/fbgemm_gpu/codegen:embedding_ops_cpu_training"
)
except Exception:
pass
try:
try:
from torch.compiler import is_compiling
def is_torchdynamo_compiling() -> bool: # type: ignore[misc]
# at least one test fails if we import is_compiling as a different name
return is_compiling()
except Exception:
# torch.compiler.is_compiling is not available in torch 1.10
from torch._dynamo import is_compiling as is_torchdynamo_compiling
except Exception:
def is_torchdynamo_compiling() -> bool: # type: ignore[misc]
return False
DEFAULT_ASSOC = 32 if torch.version.hip is None else 64
INT8_EMB_ROW_DIM_OFFSET = 8
class DoesNotHavePrefix(Exception):
pass
class ComputeDevice(enum.IntEnum):
CPU = 0
CUDA = 1
MTIA = 2
class WeightDecayMode(enum.IntEnum):
NONE = 0
L2 = 1
DECOUPLE = 2
COUNTER = 3
COWCLIP = 4
DECOUPLE_GLOBAL = 5
class CounterWeightDecayMode(enum.IntEnum):
NONE = 0
L2 = 1
DECOUPLE = 2
class LearningRateMode(enum.IntEnum):
EQUAL = -1
TAIL_ID_LR_INCREASE = 0
TAIL_ID_LR_DECREASE = 1
COUNTER_SGD = 2
class GradSumDecay(enum.IntEnum):
NO_DECAY = -1
CTR_DECAY = 0
@dataclass(frozen=True)
class TailIdThreshold:
val: float = 0
is_ratio: bool = False
@dataclass(frozen=True)
class CounterBasedRegularizationDefinition:
counter_weight_decay_mode: CounterWeightDecayMode = CounterWeightDecayMode.NONE
counter_halflife: int = -1
adjustment_iter: int = -1
adjustment_ub: float = 1.0
learning_rate_mode: LearningRateMode = LearningRateMode.EQUAL
grad_sum_decay: GradSumDecay = GradSumDecay.NO_DECAY
tail_id_threshold: TailIdThreshold = field(default_factory=TailIdThreshold)
max_counter_update_freq: int = 1000
@dataclass(frozen=True)
class CowClipDefinition:
counter_weight_decay_mode: CounterWeightDecayMode = CounterWeightDecayMode.NONE
counter_halflife: int = -1
weight_norm_coefficient: float = 0.0
lower_bound: float = 0.0
@dataclass(frozen=True)
class GlobalWeightDecayDefinition:
start_iter: int = 0
lower_bound: float = 0.0
# Keep in sync with fbgemm_gpu/include/fbgemm_gpu/split_embeddings_cache_cuda.cuh
class UVMCacheStatsIndex(enum.IntEnum):
num_calls = 0
num_requested_indices = 1
num_unique_indices = 2
num_unique_misses = 3
num_conflict_unique_misses = 4
num_conflict_misses = 5
def construct_split_state(
embedding_specs: List[Tuple[int, int, EmbeddingLocation, ComputeDevice]],
rowwise: bool,
cacheable: bool,
precision: SparseType = SparseType.FP32,
int8_emb_row_dim_offset: int = INT8_EMB_ROW_DIM_OFFSET,
placement: Optional[EmbeddingLocation] = None,
) -> SplitState:
placements: List[EmbeddingLocation] = []
offsets: List[int] = []
dev_size: int = 0
host_size: int = 0
uvm_size: int = 0
for num_embeddings, embedding_dim, location, _ in embedding_specs:
assert (
embedding_dim % 4 == 0
), f"embedding_dim must be a multiple of 4, but got {embedding_dim}"
if precision == SparseType.INT8:
embedding_dim += int8_emb_row_dim_offset
state_size = num_embeddings * embedding_dim if not rowwise else num_embeddings
location = placement if placement is not None else location
if location == EmbeddingLocation.HOST:
placements.append(EmbeddingLocation.HOST)
offsets.append(host_size)
host_size += state_size
# If table is on device, then opimtizer is on device.
# If table is managed, then if optimizer state is rowwise, optimizer is on device, otherwise optimizer is managed.
elif location == EmbeddingLocation.DEVICE or rowwise:
placements.append(EmbeddingLocation.DEVICE)
offsets.append(dev_size)
dev_size += state_size
else:
if cacheable and location == EmbeddingLocation.MANAGED_CACHING:
placements.append(EmbeddingLocation.MANAGED_CACHING)
else:
placements.append(EmbeddingLocation.MANAGED)
offsets.append(uvm_size)
uvm_size += state_size
assert len(placements) == len(offsets)
return SplitState(
dev_size=dev_size,
host_size=host_size,
uvm_size=uvm_size,
placements=placements,
offsets=offsets,
)
def apply_split_helper(
persistent_state_fn: Callable[[str, Tensor], None],
set_attr_fn: Callable[
[str, Union[Tensor, List[int], List[EmbeddingLocation]]], None
],
current_device: torch.device,
use_cpu: bool,
feature_table_map: List[int],
split: SplitState,
prefix: str,
dtype: Type[torch.dtype],
enforce_hbm: bool = False,
make_dev_param: bool = False,
dev_reshape: Optional[Tuple[int, ...]] = None,
uvm_tensors_log: Optional[List[str]] = None,
) -> None:
set_attr_fn(f"{prefix}_physical_placements", split.placements)
set_attr_fn(f"{prefix}_physical_offsets", split.offsets)
offsets = [split.offsets[t] for t in feature_table_map]
placements = [split.placements[t] for t in feature_table_map]
persistent_state_fn(
f"{prefix}_offsets",
torch.tensor(offsets, device=current_device, dtype=torch.int64),
)
persistent_state_fn(
f"{prefix}_placements",
torch.tensor(placements, device=current_device, dtype=torch.int32),
)
if split.dev_size > 0:
dev_buffer = torch.zeros(
split.dev_size,
device=current_device,
# pyre-fixme[6]
dtype=dtype,
)
dev_buffer = (
dev_buffer.view(*dev_reshape) if dev_reshape is not None else dev_buffer
)
else:
# pyre-fixme[6]
dev_buffer = torch.empty(0, device=current_device, dtype=dtype)
if make_dev_param:
set_attr_fn(f"{prefix}_dev", nn.Parameter(dev_buffer))
else:
persistent_state_fn(f"{prefix}_dev", dev_buffer)
if split.host_size > 0:
if dtype == torch.uint8:
persistent_state_fn(
f"{prefix}_host",
torch.zeros(
split.host_size,
device=current_device,
# pyre-fixme[6]: Expected `Optional[Type[torch._dtype]]` for
# 3rd param but got `Type[Type[torch._dtype]]`.
dtype=dtype,
),
)
else:
set_attr_fn(
f"{prefix}_host",
nn.Parameter(
torch.zeros(
split.host_size,
device=current_device,
# pyre-fixme[6]: Expected `Optional[Type[torch._dtype]]`
# for 3rd param but got `Type[Type[torch._dtype]]`.
dtype=dtype,
)
),
)
if uvm_tensors_log is not None:
uvm_tensors_log.append(f"{prefix}_host")
else:
persistent_state_fn(
f"{prefix}_host",
# pyre-fixme[6]: For 3rd param expected `dtype` but got `Type[dtype]`.
torch.empty(0, device=current_device, dtype=dtype),
)
if split.uvm_size > 0:
assert not use_cpu
if enforce_hbm:
logging.info("Enforce hbm for the cache location")
persistent_state_fn(
f"{prefix}_uvm",
torch.zeros(
split.uvm_size,
device=current_device,
# pyre-fixme[6]: Expected `Optional[Type[torch._dtype]]` for
# 3rd param but got `Type[Type[torch._dtype]]`.
dtype=dtype,
),
)
else:
persistent_state_fn(
f"{prefix}_uvm",
torch.zeros(
split.uvm_size,
out=torch.ops.fbgemm.new_managed_tensor(
# pyre-fixme[6]: Expected `Optional[Type[torch._dtype]]`
# for 3rd param but got `Type[Type[torch._dtype]]`.
torch.zeros(1, device=current_device, dtype=dtype),
[split.uvm_size],
),
),
)
if uvm_tensors_log is not None:
uvm_tensors_log.append(f"{prefix}_uvm")
else:
persistent_state_fn(
f"{prefix}_uvm",
# pyre-fixme[6]: For 3rd param expected `dtype` but got `Type[dtype]`.
torch.empty(0, device=current_device, dtype=dtype),
)
def generate_vbe_metadata(
offsets: Tensor,
batch_size_per_feature_per_rank: Optional[List[List[int]]],
optimizer: OptimType,
pooling_mode: PoolingMode,
feature_dims_cpu: Tensor,
device: torch.device,
) -> invokers.lookup_args.VBEMetadata:
"""
Generate VBE metadata based on batch_size_per_feature_per_rank.
Metadata includes:
1) B_offsets - A tensor that contains batch size offsets for each
feature
2) output_offsets_feature_rank - A tensor that contains output
offsets for each feature
3) B_offsets_per_rank_per_feature - A tensor that contains batch
size offsets for each feature
and rank
4) max_B - The maximum batch size for all features
5) max_B_feature_rank - The maximum batch size for all ranks and
features
6) output_size - The output size (number of elements)
"""
if batch_size_per_feature_per_rank is not None:
assert (
optimizer == OptimType.EXACT_ROWWISE_ADAGRAD
or optimizer == OptimType.EXACT_SGD
or optimizer == OptimType.NONE
), "Variable batch size TBE support is enabled for OptimType.EXACT_ROWWISE_ADAGRAD only"
assert (
pooling_mode != PoolingMode.NONE
), "Variable batch size TBE support is not enabled for PoolingMode.NONE"
# TODO: Add input check
zero_tensor = torch.zeros(1, device="cpu", dtype=torch.int32)
# Create B offsets
total_batch_size_per_feature = torch.tensor(
batch_size_per_feature_per_rank, dtype=torch.int32, device="cpu"
).sum(dim=1)
max_B = total_batch_size_per_feature.max().item()
if not torch.jit.is_scripting() and is_torchdynamo_compiling():
torch._check_is_size(max_B)
torch._check(max_B < offsets.numel())
Bs = torch.concat([zero_tensor, total_batch_size_per_feature])
B_offsets = Bs.cumsum(dim=0).to(torch.int)
# Create output offsets
B_feature_rank = torch.tensor(
batch_size_per_feature_per_rank,
device="cpu",
dtype=torch.int64,
)
max_B_feature_rank = B_feature_rank.max().item()
if not torch.jit.is_scripting() and is_torchdynamo_compiling():
torch._check_is_size(max_B_feature_rank)
torch._check(max_B_feature_rank <= offsets.size(0))
output_sizes_feature_rank = B_feature_rank.transpose(
0, 1
) * feature_dims_cpu.view(1, -1)
output_offsets_feature_rank = torch.concat(
[
zero_tensor.to(torch.int64),
output_sizes_feature_rank.flatten().cumsum(dim=0),
]
)
output_size = output_offsets_feature_rank[-1].item()
if not torch.jit.is_scripting() and is_torchdynamo_compiling():
torch._check_is_size(output_size)
# TODO: Support INT8 output
# B_offsets_rank_per_feature is for rank and (b, t) mapping
B_offsets_rank_per_feature = (
torch.tensor(
[
[0] + batch_size_per_feature
for batch_size_per_feature in batch_size_per_feature_per_rank
],
device="cpu",
dtype=torch.int32,
)
.cumsum(dim=1)
.to(torch.int)
)
B_offsets = B_offsets.to(device, non_blocking=True)
output_offsets_feature_rank = output_offsets_feature_rank.to(
device, non_blocking=True
)
B_offsets_rank_per_feature = B_offsets_rank_per_feature.to(
device, non_blocking=True
)
# TODO: Use int32 for B_offsets and int64 for output_offsets_feature_rank
vbe_metadata = invokers.lookup_args.VBEMetadata(
B_offsets=B_offsets,
output_offsets_feature_rank=output_offsets_feature_rank,
B_offsets_rank_per_feature=B_offsets_rank_per_feature,
# pyre-ignore
max_B=max_B,
# pyre-ignore
max_B_feature_rank=max_B_feature_rank,
# pyre-ignore
output_size=output_size,
)
else:
vbe_metadata = invokers.lookup_args.VBEMetadata(
B_offsets=None,
output_offsets_feature_rank=None,
B_offsets_rank_per_feature=None,
max_B=-1,
max_B_feature_rank=-1,
output_size=-1,
)
return vbe_metadata
# pyre-fixme[13]: Attribute `uvm_cache_stats` is never initialized.
# pyre-fixme[13]: Attribute `local_uvm_cache_stats` is never initialized.
[docs]class SplitTableBatchedEmbeddingBagsCodegen(nn.Module):
"""
Table Batched Embedding (TBE) operator. Please see
docs/table_batched_embedding_ops.py for the extended documentation.
Multiple sparse features can share one embedding table.
'feature_table_map' specifies the feature-table mapping.
T: number of logical tables
T_: number of physical tables
T >= T_
For supported optimizer hyperparams, see inline comments below
"""
embedding_specs: List[Tuple[int, int, EmbeddingLocation, ComputeDevice]]
optimizer_args: invokers.lookup_args.OptimizerArgs
lxu_cache_locations_list: List[Tensor]
lxu_cache_locations_empty: Tensor
timesteps_prefetched: List[int]
record_cache_metrics: RecordCacheMetrics
uvm_cache_stats: torch.Tensor
local_uvm_cache_stats: torch.Tensor
uuid: str
last_uvm_cache_print_state: torch.Tensor
_vbe_B_offsets: Optional[torch.Tensor]
_vbe_max_B: int
def __init__( # noqa C901
self,
embedding_specs: List[
Tuple[int, int, EmbeddingLocation, ComputeDevice]
], # tuple of (rows, dims, placements, compute_devices)
feature_table_map: Optional[List[int]] = None, # [T]
cache_algorithm: CacheAlgorithm = CacheAlgorithm.LRU,
cache_load_factor: float = 0.2,
cache_sets: int = 0,
cache_reserved_memory: float = 0.0,
cache_precision: SparseType = SparseType.FP32,
weights_precision: SparseType = SparseType.FP32,
output_dtype: SparseType = SparseType.FP32,
enforce_hbm: bool = False, # place all weights/momentums in HBM when using cache
optimizer: OptimType = OptimType.EXACT_SGD,
record_cache_metrics: Optional[RecordCacheMetrics] = None,
gather_uvm_cache_stats: Optional[bool] = False,
# General Optimizer args
stochastic_rounding: bool = True,
gradient_clipping: bool = False,
max_gradient: float = 1.0,
max_norm: float = 0.0,
learning_rate: float = 0.01,
# used by EXACT_ADAGRAD, EXACT_ROWWISE_ADAGRAD, LAMB, and ADAM only
# NOTE that default is different from nn.optim.Adagrad default of 1e-10
eps: float = 1.0e-8,
momentum: float = 0.9, # used by LARS-SGD
# EXACT_ADAGRAD, SGD, EXACT_SGD do not support weight decay
# LAMB, ADAM, PARTIAL_ROWWISE_ADAM, PARTIAL_ROWWISE_LAMB, LARS_SGD support decoupled weight decay
# EXACT_ROWWISE_ADAGRAD support both L2 and decoupled weight decay (via weight_decay_mode)
weight_decay: float = 0.0,
weight_decay_mode: WeightDecayMode = WeightDecayMode.NONE,
eta: float = 0.001, # used by LARS-SGD,
beta1: float = 0.9, # used by LAMB and ADAM
beta2: float = 0.999, # used by LAMB and ADAM
counter_based_regularization: Optional[
CounterBasedRegularizationDefinition
] = None, # used by Rowwise Adagrad
cowclip_regularization: Optional[
CowClipDefinition
] = None, # used by Rowwise Adagrad
pooling_mode: PoolingMode = PoolingMode.SUM,
device: Optional[Union[str, int, torch.device]] = None,
bounds_check_mode: BoundsCheckMode = BoundsCheckMode.WARNING,
uvm_non_rowwise_momentum: bool = False, # place non-rowwise momentum on UVM
use_experimental_tbe: bool = False, # set to True to use TBE v2 (only support NVIDIA GPUs)
# set to True to enable prefetch pipeline, currently only supports LRU cache policy.
# If a separate stream is used for prefetch, the optional forward_stream arg of prefetch function
# should be set.
prefetch_pipeline: bool = False,
stats_reporter_config: Optional[TBEStatsReporterConfig] = None,
# Embedding table names that are contained in this TBE.
table_names: Optional[List[str]] = None,
optimizer_state_dtypes: Optional[Dict[str, SparseType]] = None,
multipass_prefetch_config: Optional[MultiPassPrefetchConfig] = None,
# Global weight decay params
global_weight_decay: Optional[GlobalWeightDecayDefinition] = None,
) -> None:
super(SplitTableBatchedEmbeddingBagsCodegen, self).__init__()
self.uuid = str(uuid.uuid4())
self.logging_table_name: str = self.get_table_name_for_logging(table_names)
self.pooling_mode = pooling_mode
self.bounds_check_mode_int: int = bounds_check_mode.value
self.weights_precision = weights_precision
self.output_dtype: int = output_dtype.as_int()
assert (
not prefetch_pipeline or cache_algorithm == CacheAlgorithm.LRU
), "Only LRU cache policy supports prefetch_pipeline."
self.prefetch_pipeline: bool = prefetch_pipeline
self.lock_cache_line: bool = self.prefetch_pipeline
self.use_uniq_cache_locations_bwd: bool = self.prefetch_pipeline
self.multipass_prefetch_config: Optional[MultiPassPrefetchConfig] = (
multipass_prefetch_config
)
if record_cache_metrics is not None:
self.record_cache_metrics = record_cache_metrics
else:
self.record_cache_metrics = RecordCacheMetrics(False, False)
if multipass_prefetch_config:
assert (
prefetch_pipeline
), "Multipass prefetch makes no sense in non-prefetch mode."
assert (
cache_algorithm == CacheAlgorithm.LRU
), "Multipass prefetch is only supported in LRU cache."
assert (
multipass_prefetch_config.num_passes > 0
), f"num_passes must be positive, get {multipass_prefetch_config.num_passes}"
assert (
multipass_prefetch_config.min_splitable_pass_size > 0
), f"min_splitable_pass_size must be positive, get {multipass_prefetch_config.min_splitable_pass_size}"
assert (
not self.record_cache_metrics.record_cache_miss_counter
and not self.record_cache_metrics.record_tablewise_cache_miss
), "Unique cache miss counters are not accurate in multipass prefetch and therefore not supported"
self.embedding_specs = embedding_specs
(rows, dims, locations, compute_devices) = zip(*embedding_specs)
T_ = len(self.embedding_specs)
self.dims: List[int] = dims
assert T_ > 0
# mixed D is not supported by no bag kernels
mixed_D = False
D = self.dims[0]
for d in self.dims:
if d != D:
mixed_D = True
break
if mixed_D:
assert (
self.pooling_mode != PoolingMode.NONE
), "Mixed dimension tables only supported for pooling tables."
assert all(
cd == compute_devices[0] for cd in compute_devices
), "Heterogenous compute_devices are NOT supported!"
# Split TBE has different function schemas for CUDA and CPU.
# For MTIA device type, it uses the CPU one.
self.use_cpu: bool = (
compute_devices[0] == ComputeDevice.CPU
or compute_devices[0] == ComputeDevice.MTIA
)
assert not self.use_cpu or all(
loc == EmbeddingLocation.HOST for loc in locations
), "ComputeDevice.CPU is only for EmbeddingLocation.HOST!"
assert self.use_cpu or all(
loc != EmbeddingLocation.HOST for loc in locations
), "EmbeddingLocation.HOST doesn't work for CUDA device!"
if self.use_cpu or self.pooling_mode == PoolingMode.NONE:
assert output_dtype in [
SparseType.FP32,
SparseType.FP16,
SparseType.BF16,
], "Fused pooled embedding quantization only supported for cuda."
if optimizer == OptimType.NONE:
assert all(
loc == EmbeddingLocation.DEVICE for loc in locations
), "OptimType.NONE supports only EmbeddingLocation.DEVICE"
assert all(
cd == ComputeDevice.CUDA for cd in compute_devices
), "OptimType.NONE supports only ComputeDevice.CUDA"
assert (
not mixed_D
), "OptimType.NONE does not support mixed embedding dimension"
if device is None:
self.current_device: torch.device = (
torch.device("cpu")
if self.use_cpu
else torch.device(torch.cuda.current_device())
)
elif isinstance(device, torch.device):
self.current_device = device
else:
self.current_device = torch.device(device)
# add placeholder require_grad param tensor to enable autograd with int8 weights
self.placeholder_autograd_tensor = nn.Parameter(
torch.zeros(0, device=self.current_device, dtype=torch.float)
)
self.gather_uvm_cache_stats = gather_uvm_cache_stats
# Define the size of uvm cache stats as class variable
# to make it work with torch jit script.
self.uvm_cache_stats_size = 6
# 0: N_calls, 1: N_requested_indices, 2: N_unique_indices, 3: N_unique_misses,
# 4: N_conflict_unique_misses, 5: N_conflict_misses
# Reporter to collect runtime performance stats bottom-up. Reporter may
# do aggregation across TBEs and publish results per training batch.
# Example of stats include UVM cache hit rate, table I/O size, etc.
self.stats_reporter: Optional[TBEStatsReporter] = (
stats_reporter_config.create_reporter() if stats_reporter_config else None
)
self._uvm_tensors_log: List[str] = []
self.bwd_wait_prefetch_timer: Optional[AsyncSeriesTimer] = None
if self.stats_reporter:
# When stats_reporter is present, we set up async series timer to
# measure the GPU time per tracked event accordingly. Each of them
# is attached to custom callback report function to report collected
# duration with the corresponding event name.
self.bwd_wait_prefetch_timer = AsyncSeriesTimer(
functools.partial(
SplitTableBatchedEmbeddingBagsCodegen._report_wait_prefetch_time,
self,
event_name="bwd_wait_for_prefetch",
)
)
self.int8_emb_row_dim_offset: int = INT8_EMB_ROW_DIM_OFFSET
self.feature_table_map: List[int] = (
feature_table_map if feature_table_map is not None else list(range(T_))
)
T = len(self.feature_table_map)
assert T_ <= T
table_has_feature = [False] * T_
for t in self.feature_table_map:
table_has_feature[t] = True
assert all(table_has_feature), "Each table must have at least one feature!"
feature_dims = [dims[t] for t in self.feature_table_map]
D_offsets = [0] + list(accumulate(feature_dims))
self.total_D: int = D_offsets[-1]
self.max_D: int = max(dims)
cached_dims = [
embedding_spec[1]
for embedding_spec in embedding_specs
if embedding_spec[2] == EmbeddingLocation.MANAGED_CACHING
]
self.max_D_cache: int = max(cached_dims) if len(cached_dims) > 0 else 0
self.register_buffer(
"D_offsets",
torch.tensor(D_offsets, device=self.current_device, dtype=torch.int32),
)
hash_size_cumsum = [0] + list(accumulate(rows))
self.total_hash_size: int = int(hash_size_cumsum[-1])
if self.total_hash_size == 0:
self.total_hash_size_bits: int = 0
else:
self.total_hash_size_bits: int = int(log2(float(self.total_hash_size)) + 1)
# The last element is to easily access # of rows of each table by
# hash_size_cumsum[t + 1] - hash_size_cumsum[t]
hash_size_cumsum = [hash_size_cumsum[t] for t in self.feature_table_map] + [
self.total_hash_size
]
self.register_buffer(
"hash_size_cumsum",
torch.tensor(
hash_size_cumsum, device=self.current_device, dtype=torch.int64
),
)
self.register_buffer(
"rows_per_table",
torch.tensor(
[rows[t] for t in self.feature_table_map],
device=self.current_device,
dtype=torch.int64,
),
)
self.register_buffer(
"bounds_check_warning",
torch.tensor([0], device=self.current_device, dtype=torch.int64),
)
# Required for VBE
self.register_buffer(
"feature_dims",
torch.tensor(feature_dims, device="cpu", dtype=torch.int64),
)
# A flag for indicating whether all embedding tables are placed in the
# same locations
self.use_homogeneous_placements: bool = all(
loc == locations[0] for loc in locations
)
weight_split = construct_split_state(
embedding_specs,
rowwise=False,
cacheable=True,
precision=weights_precision,
)
table_embedding_dtype = weights_precision.as_dtype()
self._apply_split(
weight_split,
prefix="weights",
# pyre-fixme[6]: For 3rd param expected `Type[Type[_dtype]]` but got
# `Type[_dtype]`.
dtype=table_embedding_dtype,
enforce_hbm=enforce_hbm,
make_dev_param=optimizer == OptimType.NONE,
dev_reshape=(-1, self.max_D) if optimizer == OptimType.NONE else None,
)
assert optimizer not in (
OptimType.SGD,
OptimType.ROWWISE_ADAGRAD,
), f"Optimizer {optimizer} is deprecated in the CPU + GPU modes."
if self.use_cpu:
# Construct optimizer states
assert optimizer in (
OptimType.EXACT_ADAGRAD,
OptimType.EXACT_ROWWISE_ADAGRAD,
OptimType.EXACT_SGD,
), f"Optimizer {optimizer} is not supported in CPU mode."
else:
assert optimizer in (
OptimType.ADAM,
OptimType.EXACT_ADAGRAD,
OptimType.EXACT_ROWWISE_ADAGRAD,
OptimType.EXACT_SGD,
OptimType.LAMB,
OptimType.LARS_SGD,
OptimType.PARTIAL_ROWWISE_ADAM,
OptimType.PARTIAL_ROWWISE_LAMB,
OptimType.NONE,
), f"Optimizer {optimizer} is not supported."
self.stochastic_rounding = stochastic_rounding
self.optimizer = optimizer
self.weight_decay_mode = weight_decay_mode
if (weight_decay_mode == WeightDecayMode.COUNTER) != (
counter_based_regularization is not None
):
raise AssertionError(
"Need to set weight_decay_mode=WeightDecayMode.COUNTER together with valid counter_based_regularization"
)
if (weight_decay_mode == WeightDecayMode.COWCLIP) != (
cowclip_regularization is not None
):
raise AssertionError(
"Need to set weight_decay_mode=WeightDecayMode.COWCLIP together with valid cowclip_regularization"
)
self._used_rowwise_adagrad_with_counter: bool = (
optimizer == OptimType.EXACT_ROWWISE_ADAGRAD
and (
weight_decay_mode in (WeightDecayMode.COUNTER, WeightDecayMode.COWCLIP)
)
)
if weight_decay_mode == WeightDecayMode.DECOUPLE_GLOBAL and (
not optimizer == OptimType.EXACT_ROWWISE_ADAGRAD
or global_weight_decay is None
):
raise AssertionError(
"""weight_decay_mode=WeightDecayMode.DECOUPLE_GLOBAL is supported for
optimizer=OptimType.EXACT_ROWWISE_ADAGRAD and global_weight_decay cannot be None.
"""
)
self._used_rowwise_adagrad_with_global_weight_decay: bool = (
optimizer == OptimType.EXACT_ROWWISE_ADAGRAD
and (weight_decay_mode == WeightDecayMode.DECOUPLE_GLOBAL)
)
logging.info(
f"Using global weight decay = {self._used_rowwise_adagrad_with_global_weight_decay}"
)
# Declare GWD params here to avoid torch.jit.script error
if global_weight_decay is None:
global_weight_decay = GlobalWeightDecayDefinition()
self.gwd_start_iter: int = global_weight_decay.start_iter
self.gwd_lower_bound: float = global_weight_decay.lower_bound
if counter_based_regularization is None:
counter_based_regularization = CounterBasedRegularizationDefinition()
if cowclip_regularization is None:
cowclip_regularization = CowClipDefinition()
self._max_counter_update_freq: int = -1
# Extract parameters from CounterBasedRegularizationDefinition or CowClipDefinition
# which are passed as entries for OptimizerArgs
if self._used_rowwise_adagrad_with_counter:
if self.weight_decay_mode == WeightDecayMode.COUNTER:
self._max_counter_update_freq = (
counter_based_regularization.max_counter_update_freq
)
opt_arg_weight_decay_mode = (
counter_based_regularization.counter_weight_decay_mode
)
counter_halflife = counter_based_regularization.counter_halflife
else:
opt_arg_weight_decay_mode = (
cowclip_regularization.counter_weight_decay_mode
)
counter_halflife = cowclip_regularization.counter_halflife
else:
opt_arg_weight_decay_mode = weight_decay_mode
# Default: -1, no decay applied, as a placeholder for OptimizerArgs
# which should not be effective when CounterBasedRegularizationDefinition
# and CowClipDefinition are not used
counter_halflife = -1
self.optimizer_args = invokers.lookup_args.OptimizerArgs(
stochastic_rounding=stochastic_rounding,
gradient_clipping=gradient_clipping,
max_gradient=max_gradient,
max_norm=max_norm,
learning_rate=learning_rate,
eps=eps,
beta1=beta1,
beta2=beta2,
weight_decay=weight_decay,
weight_decay_mode=opt_arg_weight_decay_mode.value,
eta=eta,
momentum=momentum,
counter_halflife=counter_halflife,
adjustment_iter=counter_based_regularization.adjustment_iter,
adjustment_ub=counter_based_regularization.adjustment_ub,
learning_rate_mode=counter_based_regularization.learning_rate_mode.value,
grad_sum_decay=counter_based_regularization.grad_sum_decay.value,
tail_id_threshold=counter_based_regularization.tail_id_threshold.val,
is_tail_id_thresh_ratio=int(
counter_based_regularization.tail_id_threshold.is_ratio
),
total_hash_size=self.total_hash_size,
weight_norm_coefficient=cowclip_regularization.weight_norm_coefficient,
lower_bound=cowclip_regularization.lower_bound,
regularization_mode=weight_decay_mode.value,
)
if optimizer != OptimType.NONE:
assert (
optimizer == OptimType.PARTIAL_ROWWISE_ADAM
or optimizer_state_dtypes is None
), "optimizer_state_dtypes option is only supported for OptimType.PARTIAL_ROWWISE_ADAM"
if optimizer in (OptimType.EXACT_SGD,):
# NOTE: make TorchScript work!
self._register_nonpersistent_buffers("momentum1")
else:
momentum1_dtype = (
torch.float32
if (
optimizer_state_dtypes is None
or "momentum1" not in optimizer_state_dtypes
)
else optimizer_state_dtypes["momentum1"].as_dtype()
)
rowwise = optimizer in [
OptimType.EXACT_ROWWISE_ADAGRAD,
]
self._apply_split(
construct_split_state(
embedding_specs,
rowwise=rowwise,
cacheable=False,
placement=(
EmbeddingLocation.MANAGED
if ((not rowwise) and uvm_non_rowwise_momentum)
else None
),
),
prefix="momentum1",
# pyre-fixme[6]: Expected `Type[Type[torch._dtype]]` for 3rd param
# but got `Type[torch.float32]`.
dtype=momentum1_dtype,
enforce_hbm=enforce_hbm,
)
if optimizer in (
OptimType.ADAM,
OptimType.PARTIAL_ROWWISE_ADAM,
OptimType.LAMB,
OptimType.PARTIAL_ROWWISE_LAMB,
):
rowwise = optimizer in (
OptimType.PARTIAL_ROWWISE_ADAM,
OptimType.PARTIAL_ROWWISE_LAMB,
)
momentum2_dtype = (
torch.float32
if (
optimizer_state_dtypes is None
or "momentum2" not in optimizer_state_dtypes
)
else optimizer_state_dtypes["momentum2"].as_dtype()
)
self._apply_split(
construct_split_state(
embedding_specs,
rowwise=rowwise,
cacheable=False,
placement=(
EmbeddingLocation.MANAGED
if ((not rowwise) and uvm_non_rowwise_momentum)
else None
),
),
prefix="momentum2",
# pyre-fixme[6]: Expected `Type[Type[torch._dtype]]` for 3rd param
# but got `Type[torch.float32]`.
dtype=momentum2_dtype,
)
else:
# NOTE: make TorchScript work!
self._register_nonpersistent_buffers("momentum2")
if self._used_rowwise_adagrad_with_counter:
self._apply_split(
construct_split_state(
embedding_specs,
rowwise=True,
cacheable=False,
),
prefix="prev_iter",
# TODO: ideally we should use int64 to track iter but it failed to compile.
# It may be related to low precision training code. Currently using float32
# as a workaround while investigating the issue.
# pyre-fixme[6]: Expected `Type[Type[torch._dtype]]` for 3rd param
# but got `Type[torch.float32]`.
dtype=torch.float32,
)
self._apply_split(
construct_split_state(
embedding_specs,
rowwise=True,
cacheable=False,
),
prefix="row_counter",
# pyre-fixme[6]: Expected `Type[Type[torch._dtype]]` for 3rd param
# but got `Type[torch.float32]`.
dtype=torch.float32,
)
self.register_buffer(
"max_counter", torch.tensor([1], dtype=torch.float32)
)
elif self._used_rowwise_adagrad_with_global_weight_decay:
self._apply_split(
construct_split_state(
embedding_specs,
rowwise=True,
cacheable=False,
),
prefix="prev_iter",
# TODO: ideally we should use int64 to track iter but it failed to compile.
# It may be related to low precision training code. Currently using float32
# as a workaround while investigating the issue.
# pyre-fixme[6]: Expected `Type[Type[torch._dtype]]` for 3rd param
# but got `Type[torch.float32]`.
dtype=torch.float32,
)
self._register_nonpersistent_buffers("row_counter")
self.register_buffer(
"max_counter",
torch.ones(1, dtype=torch.float32, device=self.current_device),
persistent=False,
)
else:
self._register_nonpersistent_buffers("prev_iter")
self._register_nonpersistent_buffers("row_counter")
self.register_buffer(
"max_counter",
torch.ones(1, dtype=torch.float32, device=self.current_device),
persistent=False,
)
if (
optimizer
in (
OptimType.ADAM,
OptimType.LAMB,
OptimType.PARTIAL_ROWWISE_ADAM,
OptimType.PARTIAL_ROWWISE_LAMB,
)
or self._used_rowwise_adagrad_with_global_weight_decay
):
self.register_buffer(
"iter",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
)
else:
self.register_buffer(
"iter",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
cache_state = construct_cache_state(rows, locations, self.feature_table_map)
# Add table-wise cache miss counter
if self.record_cache_metrics.record_tablewise_cache_miss:
num_tables = len(cache_state.cache_hash_size_cumsum) - 1
self.register_buffer(
"table_wise_cache_miss",
torch.zeros(
num_tables,
device=self.current_device,
dtype=torch.int64,
),
)
# NOTE: make TorchScript work!
else:
self.register_buffer(
"table_wise_cache_miss",
torch.zeros(
0,
device=self.current_device,
dtype=torch.int64,
),
)
if cache_precision == SparseType.FP32:
cache_embedding_dtype = torch.float32
elif cache_precision == SparseType.FP16:
cache_embedding_dtype = torch.float16
else:
raise AssertionError(f"cache_precision {cache_precision} not supported!")
self._apply_cache_state(
cache_state,
cache_algorithm,
cache_load_factor,
cache_sets,
cache_reserved_memory,
dtype=cache_embedding_dtype,
)
self.log(f"Contents: {table_names}")
self.log(
f"Using fused {optimizer} with optimizer_args={self.optimizer_args if optimizer != OptimType.NONE else None}"
)
self.log(
f"Using rowwise_adagrad_with_counter={self._used_rowwise_adagrad_with_counter}"
)
self.step = 0
self.last_reported_step = 0
self.last_reported_uvm_stats: List[float] = []
# Check whether to use TBE v2
is_experimental = False
if use_experimental_tbe:
is_experimental = True
self.log("use_experimental_tbe is set to True; Using experimental TBE")
elif int(os.environ.get("FBGEMM_EXPERIMENTAL_TBE", "0")) == 1:
# Keep the old feature enablement mechanism to ensure no negative impact on models that have already adopted TBE v2
is_experimental = True
self.log("FBGEMM_EXPERIMENTAL_TBE is set to True; Using experimental TBE")
# NOTE: Keep this disabled for now until the backend lands into Pyper
# elif FeatureGateName.TBE_V2.is_enabled():
# is_experimental = True
# self.log("TBE_V2 Knob is set to True; Using experimental TBE")
self.is_experimental: bool = is_experimental
@torch.jit.ignore
def log(self, msg: str) -> None:
"""Log with TBE id prefix to distinguish between multiple TBE instances per process."""
logging.info(f"[TBE={self.uuid}] {msg}")
def _register_nonpersistent_buffers(self, prefix: str) -> None:
# NOTE: make TorchScript work!
self.register_buffer(
f"{prefix}_dev",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
f"{prefix}_host",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
f"{prefix}_uvm",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
f"{prefix}_placements",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
f"{prefix}_offsets",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
@staticmethod
def get_table_name_for_logging(table_names: Optional[List[str]]) -> str:
"""
Given list of all table names in the TBE, generate a string to represent
them in logging. If there's more than one table, this method will count
them than list them.
"""
if table_names is None:
return "<Unknown>"
# Do this because sometimes multiple shards of the same table could appear
# in one TBE.
table_name_set = set(table_names)
if len(table_name_set) == 1:
return next(iter(table_name_set))
return f"<{len(table_name_set)} tables>"
@staticmethod
def get_prefetch_passes(
multipass_prefetch_config: Optional[MultiPassPrefetchConfig],
input_tensor: Tensor,
output_tensor: Tensor,
) -> List[Tuple[Tensor, Tensor, int]]:
"""
Given input (the indices to forward), return the segmentation for each pass
in the format of (input[start_idx:end_idx], output[start_idx:end_idx], start_idx).
Caller should guarantee input and output are having the size on dimension 0
The returned segments are guaranteed to completely and non-overlappingly cover the input tensor.
In non-multipass-prefetch mode, it returns the input/output tensor itself.
"""
if multipass_prefetch_config is None:
return [(input_tensor, output_tensor, 0)]
mpp_config: MultiPassPrefetchConfig = multipass_prefetch_config
N = input_tensor.size(0)
if N <= mpp_config.num_passes or mpp_config.num_passes == 1:
# One row per pass, just don't split
return [(input_tensor, output_tensor, 0)]
pass_size: int = max(
(N + mpp_config.num_passes - 1) // mpp_config.num_passes,
mpp_config.min_splitable_pass_size,
)
return list(
zip(
torch.split(input_tensor, pass_size),
torch.split(output_tensor, pass_size),
range(0, N, pass_size),
)
)
def get_states(self, prefix: str) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]:
if not hasattr(self, f"{prefix}_physical_placements"):
raise DoesNotHavePrefix()
dev_param = getattr(self, f"{prefix}_dev")
host_param = getattr(self, f"{prefix}_host")
uvm_param = getattr(self, f"{prefix}_uvm")
placements = getattr(self, f"{prefix}_physical_placements")
offsets = getattr(self, f"{prefix}_physical_offsets")
return (
dev_param,
host_param,
uvm_param,
torch.tensor(placements, dtype=torch.int32),
torch.tensor(offsets, dtype=torch.int64),
)
def get_all_states(self) -> List[Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]]:
all_states = []
for prefix in ["weights", "momentum1", "momentum2", "prev_iter", "row_counter"]:
try:
all_states.append(self.get_states(prefix))
except DoesNotHavePrefix:
pass
return all_states
@torch.jit.export
def get_cache_miss_counter(self) -> Tensor:
# cache_miss_counter contains two items:
# The first one is cache_miss_forward_count which records the total number of forwards which has at least one cache miss
# The second one is the unique_cache_miss_count which records to total number of unique (dedup) cache misses
return self.cache_miss_counter
@torch.jit.export
def get_table_wise_cache_miss(self) -> Tensor:
# table_wise_cache_miss contains all the cache miss count for each table in this embedding table object:
return self.table_wise_cache_miss
# The callback function for AsyncTimer to record duration to different event
def _report_wait_prefetch_time(
self,
it_step: int,
dur_ms: float,
event_name: str,
) -> None:
assert (
self.stats_reporter
), "We should not be here. AsyncTimer only happens with reporter present."
self.stats_reporter.report_duration(
iteration_step=it_step,
event_name=event_name,
duration_ms=dur_ms,
embedding_id=self.logging_table_name,
tbe_id=self.uuid,
)
@torch.jit.ignore
def _report_tbe_mem_usage(
self,
) -> None:
if self.stats_reporter is None:
return
stats_reporter: TBEStatsReporter = self.stats_reporter
if not stats_reporter.should_report(self.step):
return
total_mem_usage = sum(
param.numel() * param.element_size() for param in self.parameters()
) + sum(buffer.numel() * buffer.element_size() for buffer in self.buffers())
if self.use_cpu:
total_hbm_usage = 0
total_uvm_usage = total_mem_usage
else:
# hbm usage is total usage minus uvm usage
total_uvm_usage = sum(
getattr(self, tensor_name).numel()
* getattr(self, tensor_name).element_size()
for tensor_name in self._uvm_tensors_log
if hasattr(self, tensor_name)
)
total_hbm_usage = total_mem_usage - total_uvm_usage
stats_reporter.report_data_amount(
iteration_step=self.step,
event_name="tbe.total_hbm_usage",
data_bytes=total_hbm_usage,
embedding_id=self.logging_table_name,
tbe_id=self.uuid,
)
stats_reporter.report_data_amount(
iteration_step=self.step,
event_name="tbe.total_uvm_usage",
data_bytes=total_uvm_usage,
embedding_id=self.logging_table_name,
tbe_id=self.uuid,
)
@torch.jit.ignore
def _report_io_size_count(self, event: str, data: Tensor) -> Tensor:
if self.stats_reporter is None:
return data
stats_reporter: TBEStatsReporter = self.stats_reporter
if stats_reporter.should_report(self.step):
stats_reporter.report_data_amount(
iteration_step=self.step,
event_name=f"tbe.{event}_size",
data_bytes=data.element_size() * data.numel(),
embedding_id=self.logging_table_name,
tbe_id=self.uuid,
)
stats_reporter.report_data_amount(
iteration_step=self.step,
event_name=f"tbe.{event}_count",
data_bytes=data.numel(),
embedding_id=self.logging_table_name,
tbe_id=self.uuid,
)
return data
@torch.jit.ignore
def _generate_vbe_metadata(
self,
offsets: Tensor,
batch_size_per_feature_per_rank: Optional[List[List[int]]],
) -> invokers.lookup_args.VBEMetadata:
# Blocking D2H copy, but only runs at first call
self.feature_dims = self.feature_dims.cpu()
return generate_vbe_metadata(
offsets,
batch_size_per_feature_per_rank,
self.optimizer,
self.pooling_mode,
self.feature_dims,
self.current_device,
)
def forward( # noqa: C901
self,
indices: Tensor,
offsets: Tensor,
per_sample_weights: Optional[Tensor] = None,
feature_requires_grad: Optional[Tensor] = None,
# 2D tensor of batch size for each rank and feature.
# Shape (number of features, number of ranks)
batch_size_per_feature_per_rank: Optional[List[List[int]]] = None,
total_unique_indices: Optional[int] = None,
) -> Tensor:
# Generate VBE metadata
vbe_metadata = self._generate_vbe_metadata(
offsets, batch_size_per_feature_per_rank
)
(indices, offsets) = indices.long(), offsets.long()
# Force casting per_sample_weights to float
if per_sample_weights is not None:
per_sample_weights = per_sample_weights.float()
if self.bounds_check_mode_int != BoundsCheckMode.NONE.value:
torch.ops.fbgemm.bounds_check_indices(
self.rows_per_table,
indices,
offsets,
self.bounds_check_mode_int,
self.bounds_check_warning,
per_sample_weights,
B_offsets=vbe_metadata.B_offsets,
max_B=vbe_metadata.max_B,
)
if not is_torchdynamo_compiling():
# Mutations of nn.Module attr forces dynamo restart of Analysis which increases compilation time
# Storing tensors for linear_cache_indices recomputation
self._indices = indices
self._offsets = offsets
self._vbe_B_offsets = vbe_metadata.B_offsets
self._vbe_max_B = vbe_metadata.max_B
self.step += 1
self._report_io_size_count("fwd_input", indices)
self._report_tbe_mem_usage()
if len(self.timesteps_prefetched) == 0:
# In forward, we don't enable multi-pass prefetch as we want the process
# to be as fast as possible and memory usage doesn't matter (will be recycled
# by dense fwd/bwd)
self._prefetch(
indices, offsets, vbe_metadata, multipass_prefetch_config=None
)
if len(self.timesteps_prefetched) > 0:
self.timesteps_prefetched.pop(0)
self.lxu_cache_locations = (
self.lxu_cache_locations_empty
if len(self.lxu_cache_locations_list) == 0
else self.lxu_cache_locations_list.pop(0)
)
common_args = invokers.lookup_args.CommonArgs(
placeholder_autograd_tensor=self.placeholder_autograd_tensor,
dev_weights=self.weights_dev,
host_weights=self.weights_host,
uvm_weights=self.weights_uvm,
lxu_cache_weights=self.lxu_cache_weights,
weights_placements=self.weights_placements,
weights_offsets=self.weights_offsets,
D_offsets=self.D_offsets,
total_D=self.total_D,
max_D=self.max_D,
hash_size_cumsum=self.hash_size_cumsum,
total_hash_size_bits=self.total_hash_size_bits,
indices=indices,
offsets=offsets,
pooling_mode=self.pooling_mode,
indice_weights=per_sample_weights,
feature_requires_grad=feature_requires_grad,
lxu_cache_locations=self.lxu_cache_locations,
# Pass the local_uvm_cache_stats bc only that information is
# relevant for the current iteration
uvm_cache_stats=(
self.local_uvm_cache_stats
if (
self.gather_uvm_cache_stats
# Unique conflict misses are only collected when using CacheAlgorithm.LRU
and self.cache_algorithm == CacheAlgorithm.LRU
)
else None
),
output_dtype=self.output_dtype,
vbe_metadata=vbe_metadata,
is_experimental=self.is_experimental,
use_uniq_cache_locations_bwd=self.use_uniq_cache_locations_bwd,
use_homogeneous_placements=self.use_homogeneous_placements,
)
if self.optimizer == OptimType.NONE:
assert (
total_unique_indices is not None
and total_unique_indices <= indices.numel()
), f"OptimType.NONE requires total_unique_indices. Please pass it or check the value (total_unique_indices = {total_unique_indices})"
return self._report_io_size_count(
"fwd_output",
invokers.lookup_none.invoke(
common_args, self.optimizer_args, total_unique_indices
),
)
elif self.optimizer == OptimType.EXACT_SGD:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_sgd.invoke(common_args, self.optimizer_args),
)
momentum1 = invokers.lookup_args.Momentum(
dev=self.momentum1_dev,
host=self.momentum1_host,
uvm=self.momentum1_uvm,
offsets=self.momentum1_offsets,
placements=self.momentum1_placements,
)
if self.optimizer == OptimType.LARS_SGD:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_lars_sgd.invoke(
common_args, self.optimizer_args, momentum1
),
)
if self.optimizer == OptimType.EXACT_ADAGRAD:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_adagrad.invoke(
common_args, self.optimizer_args, momentum1
),
)
momentum2 = invokers.lookup_args.Momentum(
dev=self.momentum2_dev,
host=self.momentum2_host,
uvm=self.momentum2_uvm,
offsets=self.momentum2_offsets,
placements=self.momentum2_placements,
)
# Ensure iter is always on CPU so the increment doesn't synchronize.
if not self.iter.is_cpu:
self.iter = self.iter.cpu()
self.iter[0] += 1
if self.optimizer == OptimType.ADAM:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_adam.invoke(
common_args,
self.optimizer_args,
momentum1,
momentum2,
# pyre-fixme[6]: Expected `int` for 5th param but got `Union[float,
# int]`.
self.iter.item(),
),
)
if self.optimizer == OptimType.PARTIAL_ROWWISE_ADAM:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_partial_rowwise_adam.invoke(
common_args,
self.optimizer_args,
momentum1,
momentum2,
# pyre-fixme[6]: Expected `int` for 5th param but got `Union[float,
# int]`.
self.iter.item(),
),
)
if self.optimizer == OptimType.LAMB:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_lamb.invoke(
common_args,
self.optimizer_args,
momentum1,
momentum2,
# pyre-fixme[6]: Expected `int` for 5th param but got `Union[float,
# int]`.
self.iter.item(),
),
)
if self.optimizer == OptimType.PARTIAL_ROWWISE_LAMB:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_partial_rowwise_lamb.invoke(
common_args,
self.optimizer_args,
momentum1,
momentum2,
# pyre-fixme[6]: Expected `int` for 5th param but got `Union[float,
# int]`.
self.iter.item(),
),
)
prev_iter = invokers.lookup_args.Momentum(
dev=self.prev_iter_dev,
host=self.prev_iter_host,
uvm=self.prev_iter_uvm,
offsets=self.prev_iter_offsets,
placements=self.prev_iter_placements,
)
row_counter = invokers.lookup_args.Momentum(
dev=self.row_counter_dev,
host=self.row_counter_host,
uvm=self.row_counter_uvm,
offsets=self.row_counter_offsets,
placements=self.row_counter_placements,
)
if self._used_rowwise_adagrad_with_counter:
if (
self._max_counter_update_freq > 0
and self.iter.item() % self._max_counter_update_freq == 0
):
row_counter_dev = self.row_counter_dev.detach()
if row_counter_dev.numel() > 0:
self.max_counter[0] = torch.max(row_counter_dev).cpu().item() + 1
else:
self.max_counter[0] = 1
if self.optimizer == OptimType.EXACT_ROWWISE_ADAGRAD:
if self._used_rowwise_adagrad_with_counter:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_rowwise_adagrad_with_counter.invoke(
common_args,
self.optimizer_args,
momentum1,
prev_iter,
row_counter,
int(
self.iter.item()
), # Cast to int to suppress pyre type error
self.max_counter.item(),
),
)
elif self._used_rowwise_adagrad_with_global_weight_decay:
apply_global_weight_decay = (
self.step >= self.gwd_start_iter and self.training
)
return self._report_io_size_count(
"fwd_output",
invokers.lookup_rowwise_adagrad.invoke(
common_args,
self.optimizer_args,
momentum1,
iter=int(self.iter.item()),
apply_global_weight_decay=apply_global_weight_decay,
prev_iter_dev=self.prev_iter_dev,
gwd_lower_bound=self.gwd_lower_bound,
),
)
else:
return self._report_io_size_count(
"fwd_output",
invokers.lookup_rowwise_adagrad.invoke(
common_args,
self.optimizer_args,
momentum1,
),
)
raise ValueError(f"Invalid OptimType: {self.optimizer}")
def reset_uvm_cache_stats(self) -> None:
assert (
self.gather_uvm_cache_stats
), "gather_uvm_cache_stats should be set to true to access uvm cache stats."
self.uvm_cache_stats.zero_()
self.local_uvm_cache_stats.zero_()
def get_uvm_cache_stats(self, use_local_cache: bool = False) -> Tensor:
assert (
self.gather_uvm_cache_stats
), "gather_uvm_cache_stats should be set to true to access uvm cache stats."
return self.local_uvm_cache_stats if use_local_cache else self.uvm_cache_stats
def _get_uvm_cache_print_state(self, use_local_cache: bool = False) -> List[float]:
snapshot = self.get_uvm_cache_stats(use_local_cache)
if use_local_cache:
return snapshot.tolist()
# Stats are accumulated over multiple steps. Compute delta, and update state.
delta = snapshot - self.last_uvm_cache_print_state
self.last_uvm_cache_print_state = snapshot.clone()
return delta.tolist()
@torch.jit.ignore
def print_uvm_cache_stats(self, use_local_cache: bool = False) -> None:
# TODO: Create a separate reporter class to unify the stdlog reporting
uvm_cache_stats: List[float] = self._get_uvm_cache_print_state(use_local_cache)
N = max(1, uvm_cache_stats[0])
m = {
"N_called": uvm_cache_stats[UVMCacheStatsIndex.num_calls],
"requested_indices": uvm_cache_stats[
UVMCacheStatsIndex.num_requested_indices
]
/ N,
"unique_indices": uvm_cache_stats[UVMCacheStatsIndex.num_unique_indices]
/ N,
"unique_misses": uvm_cache_stats[UVMCacheStatsIndex.num_unique_misses] / N,
"conflict_unique_misses": uvm_cache_stats[
UVMCacheStatsIndex.num_conflict_unique_misses
]
/ N,
"conflict_misses": uvm_cache_stats[UVMCacheStatsIndex.num_conflict_misses]
/ N,
}
if uvm_cache_stats[1]:
m.update(
{
"unique indices / requested indices": uvm_cache_stats[
UVMCacheStatsIndex.num_unique_indices
]
/ uvm_cache_stats[UVMCacheStatsIndex.num_requested_indices],
"unique misses / requested indices": uvm_cache_stats[
UVMCacheStatsIndex.num_unique_misses
]
/ uvm_cache_stats[UVMCacheStatsIndex.num_requested_indices],
}
)
self.log(f"uvm_cache_stats={m}")
@torch.jit.ignore
def _report_uvm_cache_stats(self) -> None:
if self.stats_reporter is None:
return
stats_reporter: TBEStatsReporter = self.stats_reporter
passed_steps = self.step - self.last_reported_step
if passed_steps == 0:
return
if not stats_reporter.should_report(self.step):
return
uvm_cache_stats: List[float] = self.get_uvm_cache_stats(
use_local_cache=False
).tolist()
self.last_reported_step = self.step
if len(self.last_reported_uvm_stats) == 0:
self.last_reported_uvm_stats = [0.0] * len(uvm_cache_stats)
uvm_cache_stats_delta: List[float] = [0.0] * len(uvm_cache_stats)
for i in range(len(uvm_cache_stats)):
uvm_cache_stats_delta[i] = (
uvm_cache_stats[i] - self.last_reported_uvm_stats[i]
)
self.last_reported_uvm_stats = uvm_cache_stats
element_size = self.lxu_cache_weights.element_size()
for stat_index in UVMCacheStatsIndex:
stats_reporter.report_data_amount(
iteration_step=self.step,
event_name=f"tbe.prefetch.cache_stats_by_data_size.{stat_index.name.lower()}",
data_bytes=int(
uvm_cache_stats_delta[stat_index.value]
* element_size
* self.max_D_cache
/ passed_steps
),
embedding_id=self.logging_table_name,
tbe_id=self.uuid,
)
def prefetch(
self,
indices: Tensor,
offsets: Tensor,
forward_stream: Optional[torch.cuda.Stream] = None,
batch_size_per_feature_per_rank: Optional[List[List[int]]] = None,
) -> None:
if self.prefetch_stream is None and forward_stream is not None:
self.prefetch_stream = torch.cuda.current_stream()
assert (
self.prefetch_stream != forward_stream
), "prefetch_stream and forward_stream should not be the same stream"
vbe_metadata = self._generate_vbe_metadata(
offsets, batch_size_per_feature_per_rank
)
self._prefetch(
indices,
offsets,
vbe_metadata,
multipass_prefetch_config=self.multipass_prefetch_config,
)
if forward_stream is not None:
self._prefetch_tensors_record_stream(forward_stream)
def _prefetch(
self,
indices: Tensor,
offsets: Tensor,
vbe_metadata: Optional[invokers.lookup_args.VBEMetadata] = None,
multipass_prefetch_config: Optional[MultiPassPrefetchConfig] = None,
) -> None:
if not is_torchdynamo_compiling():
# Mutations of nn.Module attr forces dynamo restart of Analysis which increases compilation time
self.timestep += 1
self.timesteps_prefetched.append(self.timestep)
if not self.lxu_cache_weights.numel():
return
# Clear the local_uvm_cache_stats before the prefetch instead of after
# the prefetch step, since it will be used in the CommonArgs in the
# forward step
if self.gather_uvm_cache_stats:
self.local_uvm_cache_stats.zero_()
self._report_io_size_count("prefetch_input", indices)
final_lxu_cache_locations = torch.empty_like(indices, dtype=torch.int32)
for (
partial_indices,
partial_lxu_cache_locations,
base_offset,
) in self.get_prefetch_passes(
multipass_prefetch_config, indices, final_lxu_cache_locations
):
linear_cache_indices = torch.ops.fbgemm.linearize_cache_indices(
self.cache_hash_size_cumsum,
partial_indices,
offsets,
vbe_metadata.B_offsets if vbe_metadata is not None else None,
vbe_metadata.max_B if vbe_metadata is not None else -1,
base_offset,
)
if (
self.record_cache_metrics.record_cache_miss_counter
or self.record_cache_metrics.record_tablewise_cache_miss
):
lxu_cache_locations = torch.ops.fbgemm.lxu_cache_lookup(
linear_cache_indices,
self.lxu_cache_state,
self.total_cache_hash_size,
self.gather_uvm_cache_stats,
self.local_uvm_cache_stats,
)
if self.record_cache_metrics.record_cache_miss_counter:
self._update_cache_miss_counter(
lxu_cache_locations, linear_cache_indices
)
if self.record_cache_metrics.record_tablewise_cache_miss:
self._update_tablewise_cache_miss(
lxu_cache_locations, linear_cache_indices, offsets
)
if self.cache_algorithm == CacheAlgorithm.LRU:
torch.ops.fbgemm.lru_cache_populate(
self.weights_uvm,
self.cache_hash_size_cumsum,
self.total_cache_hash_size,
self.cache_index_table_map,
self.weights_offsets,
self.D_offsets,
linear_cache_indices,
self.lxu_cache_state,
self.lxu_cache_weights,
self.timestep,
self.lxu_state,
self.stochastic_rounding,
self.gather_uvm_cache_stats,
self.local_uvm_cache_stats,
self.lock_cache_line,
self.lxu_cache_locking_counter,
)
elif self.cache_algorithm == CacheAlgorithm.LFU:
torch.ops.fbgemm.lfu_cache_populate(
self.weights_uvm,
self.cache_hash_size_cumsum,
self.total_cache_hash_size,
self.cache_index_table_map,
self.weights_offsets,
self.D_offsets,
linear_cache_indices,
self.lxu_cache_state,
self.lxu_cache_weights,
self.lxu_state,
self.stochastic_rounding,
)
torch.ops.fbgemm.lxu_cache_lookup(
linear_cache_indices,
self.lxu_cache_state,
self.total_cache_hash_size,
self.gather_uvm_cache_stats,
self.local_uvm_cache_stats,
lxu_cache_locations_output=partial_lxu_cache_locations,
)
assert (
len(self.lxu_cache_locations_list) < self.max_prefetch_depth
), f"self.lxu_cache_locations_list has grown to size: {len(self.lxu_cache_locations_list)}, this exceeds the maximum: {self.max_prefetch_depth}. This probably indicates an error in logic where prefetch() is being called more frequently than forward()"
self.lxu_cache_locations_list.append(final_lxu_cache_locations)
if self.gather_uvm_cache_stats:
# Accumulate local_uvm_cache_stats (int32) into uvm_cache_stats (int64).
# We may want to do this accumulation atomically, but as it's only
# for monitoring, slightly inaccurate result may be acceptable.
self.uvm_cache_stats = torch.add(
self.uvm_cache_stats, self.local_uvm_cache_stats
)
self._report_uvm_cache_stats()
if self.should_log():
self.print_uvm_cache_stats(use_local_cache=False)
def should_log(self) -> bool:
"""Determines if we should log for this step, using exponentially decreasing frequency.
Logs for steps: 100 200 ... 1,000 2,000 ... 10,000 20,000 ... 100,000 200,000 ...
"""
s = self.step + 1 # step starts at 0
return s >= 100 and s % (10 ** int(math.log10(s))) == 0
def _prefetch_tensors_record_stream(
self, forward_stream: torch.cuda.Stream
) -> None:
# Record the tensors created by prefetch stream and consumed by forward/backward
# to the forward stream. In PyTorch, each backward CUDA op runs on the same
# stream that was used for its corresponding forward op.
for t in self.lxu_cache_locations_list:
t.record_stream(forward_stream)
def _update_cache_miss_counter(
self,
lxu_cache_locations: Tensor,
linear_cache_indices: Tensor,
) -> None:
CACHE_MISS = -1
CACHE_HIT = -2
cache_missed_locations = torch.where(
lxu_cache_locations == CACHE_MISS, linear_cache_indices, CACHE_HIT
)
unique_ids_list = torch.unique(cache_missed_locations)
unique_ids_count_list = torch.where(unique_ids_list == CACHE_HIT, 0, 1)
miss_count = torch.sum(unique_ids_count_list)
self.cache_miss_counter[0] += (miss_count > 0).to(torch.int64)
self.cache_miss_counter[1] += miss_count
def _update_tablewise_cache_miss(
self,
lxu_cache_locations: Tensor,
linear_cache_indices: Tensor,
offsets: Tensor,
) -> None:
CACHE_MISS = -1
CACHE_HIT = -2
num_tables = len(self.cache_hash_size_cumsum) - 1
num_offsets_per_table = (len(offsets) - 1) // num_tables
cache_missed_locations = torch.where(
lxu_cache_locations == CACHE_MISS, linear_cache_indices, CACHE_HIT
)
for i in range(num_tables):
start = offsets[i * num_offsets_per_table]
end = offsets[(i + 1) * num_offsets_per_table]
current_cache_missed_locations = cache_missed_locations[start:end]
unique_ids_list = torch.unique(current_cache_missed_locations)
unique_ids_count_list = torch.where(unique_ids_list == CACHE_HIT, 0, 1)
miss_count = torch.sum(unique_ids_count_list)
self.table_wise_cache_miss[i] += miss_count
def init_embedding_weights_uniform(self, min_val: float, max_val: float) -> None:
splits = self.split_embedding_weights()
if self.weights_precision == SparseType.INT8:
# TODO: add in-place FloatToFused8BitRowwiseQuantized conversion
for emb in splits:
assert (
len(emb.shape) == 2
), "Int8 embedding only supported for 2D weight tensors."
shape = [emb.shape[0], emb.shape[1] - self.int8_emb_row_dim_offset]
tmp_emb = torch.zeros(shape, device=self.current_device)
tmp_emb.uniform_(min_val, max_val)
tmp_emb_i8 = torch.ops.fbgemm.FloatToFused8BitRowwiseQuantized(tmp_emb)
emb.data.copy_(tmp_emb_i8)
else:
for param in splits:
param.uniform_(min_val, max_val)
@torch.jit.ignore
def split_embedding_weights(self) -> List[Tensor]:
"""
Returns a list of weights, split by table
"""
splits = []
for t, (rows, dim, _, _) in enumerate(self.embedding_specs):
if self.weights_precision == SparseType.INT8:
dim += self.int8_emb_row_dim_offset
placement = self.weights_physical_placements[t]
offset = self.weights_physical_offsets[t]
if placement == EmbeddingLocation.DEVICE.value:
weights = self.weights_dev
elif placement == EmbeddingLocation.HOST.value:
weights = self.weights_host
else:
weights = self.weights_uvm
if weights.dim() == 2:
weights = weights.flatten()
splits.append(
weights.detach()[offset : offset + rows * dim].view(rows, dim)
)
return splits
@torch.jit.ignore
def get_optimizer_buffer(self, state: str) -> torch.Tensor:
if self.optimizer == OptimType.NONE:
raise NotImplementedError(
f"Getting optimizer buffer is not supported for {self.optimizer}"
)
for name, buffer in self.named_buffers():
if name == state:
return buffer
return torch.tensor(0)
@torch.jit.export
def get_optimizer_state(self) -> List[Dict[str, torch.Tensor]]:
r"""
Get the optimizer state dict that matches the OSS Pytorch optims
TODO: populate the supported list of optimizers
"""
split_optimizer_states = self.split_optimizer_states()
if (
self.optimizer == OptimType.EXACT_ROWWISE_ADAGRAD
or self.optimizer == OptimType.EXACT_ADAGRAD
):
list_of_state_dict = [
(
{"sum": states[0], "prev_iter": states[1], "row_counter": states[2]}
if self._used_rowwise_adagrad_with_counter
else (
{"sum": states[0], "prev_iter": states[1]}
if self._used_rowwise_adagrad_with_global_weight_decay
else {"sum": states[0]}
)
)
for states in split_optimizer_states
]
elif self.optimizer == OptimType.SGD or self.optimizer == OptimType.EXACT_SGD:
list_of_state_dict = [
{"momentum_buffer": states[0]} for states in split_optimizer_states
]
elif (
self.optimizer == OptimType.ADAM
or self.optimizer == OptimType.PARTIAL_ROWWISE_ADAM
or self.optimizer == OptimType.LAMB
or self.optimizer == OptimType.PARTIAL_ROWWISE_LAMB
):
list_of_state_dict = [
{"exp_avg": states[0], "exp_avg_sq": states[1]}
for states in split_optimizer_states
]
else:
raise NotImplementedError(
f"Getting optimizer state {self.optimizer} is not implmeneted"
)
return list_of_state_dict
@torch.jit.ignore
def split_optimizer_states(
self,
) -> List[List[torch.Tensor]]:
"""
Returns a list of states, split by table
"""
if self.optimizer == OptimType.NONE:
raise NotImplementedError(
f"Getting optimizer states is not supported for {self.optimizer}"
)
def get_optimizer_states(
state_dev: Tensor,
state_host: Tensor,
state_uvm: Tensor,
state_offsets: Tensor,
state_placements: Tensor,
rowwise: bool,
) -> List[torch.Tensor]:
splits = []
for t, (rows, dim, _, _) in enumerate(self.embedding_specs):
offset = state_offsets[t]
placement = state_placements[t]
if placement == EmbeddingLocation.DEVICE:
state = state_dev
elif placement == EmbeddingLocation.HOST:
state = state_host
else:
state = state_uvm
if not rowwise:
splits.append(
state.detach()[offset : offset + rows * dim].view(rows, dim)
)
else:
splits.append(state.detach()[offset : offset + rows].view(rows))
return splits
states: List[List[torch.Tensor]] = []
if self.optimizer not in (OptimType.EXACT_SGD,):
states.append(
get_optimizer_states(
self.momentum1_dev,
self.momentum1_host,
self.momentum1_uvm,
self.momentum1_physical_offsets,
self.momentum1_physical_placements,
rowwise=self.optimizer
in [
OptimType.EXACT_ROWWISE_ADAGRAD,
],
)
)
if self.optimizer in (
OptimType.ADAM,
OptimType.PARTIAL_ROWWISE_ADAM,
OptimType.LAMB,
OptimType.PARTIAL_ROWWISE_LAMB,
):
states.append(
get_optimizer_states(
self.momentum2_dev,
self.momentum2_host,
self.momentum2_uvm,
self.momentum2_physical_offsets,
self.momentum2_physical_placements,
rowwise=self.optimizer
in (OptimType.PARTIAL_ROWWISE_ADAM, OptimType.PARTIAL_ROWWISE_LAMB),
)
)
if self._used_rowwise_adagrad_with_global_weight_decay:
states.append(
get_optimizer_states(
self.prev_iter_dev,
self.prev_iter_host,
self.prev_iter_uvm,
self.prev_iter_physical_offsets,
self.prev_iter_physical_placements,
rowwise=True,
)
)
if self._used_rowwise_adagrad_with_counter:
states.append(
get_optimizer_states(
self.prev_iter_dev,
self.prev_iter_host,
self.prev_iter_uvm,
self.prev_iter_physical_offsets,
self.prev_iter_physical_placements,
rowwise=True,
)
)
states.append(
get_optimizer_states(
self.row_counter_dev,
self.row_counter_host,
self.row_counter_uvm,
self.row_counter_physical_offsets,
self.row_counter_physical_placements,
rowwise=True,
)
)
return_states = [list(s) for s in zip(*states)]
return return_states
@torch.jit.export
def set_learning_rate(self, lr: float) -> None:
"""
Sets the learning rate.
"""
if self.optimizer == OptimType.NONE:
raise NotImplementedError(
f"Setting learning rate is not supported for {self.optimizer}"
)
self._set_learning_rate(lr)
@torch.jit.ignore
def _set_learning_rate(self, lr: float) -> float:
"""
Helper function to script `set_learning_rate`.
Note that returning None does not work.
"""
self.optimizer_args = self.optimizer_args._replace(learning_rate=lr)
return 0.0
@torch.jit.ignore
def set_optimizer_step(self, step: int) -> None:
"""
Sets the optimizer step.
"""
self.log(f"set_optimizer_step from {self.iter[0]} to {step}")
if self.optimizer == OptimType.NONE:
raise NotImplementedError(
f"Setting optimizer step is not supported for {self.optimizer}"
)
self.iter[0] = step
@torch.jit.export
def flush(self) -> None:
if not self.lxu_cache_weights.numel():
return
torch.ops.fbgemm.lxu_cache_flush(
self.weights_uvm,
self.cache_hash_size_cumsum,
self.cache_index_table_map,
self.weights_offsets,
self.D_offsets,
self.total_D,
self.lxu_cache_state,
self.lxu_cache_weights,
self.stochastic_rounding,
)
def _apply_split(
self,
split: SplitState,
prefix: str,
dtype: Type[torch.dtype],
enforce_hbm: bool = False,
make_dev_param: bool = False,
dev_reshape: Optional[Tuple[int, ...]] = None,
) -> None:
apply_split_helper(
self.register_buffer,
functools.partial(setattr, self),
self.current_device,
self.use_cpu,
self.feature_table_map,
split,
prefix,
dtype,
enforce_hbm,
make_dev_param,
dev_reshape,
self._uvm_tensors_log,
)
def _apply_cache_state(
self,
cache_state: CacheState,
cache_algorithm: CacheAlgorithm,
cache_load_factor: float,
cache_sets: int,
cache_reserved_memory: float,
dtype: torch.dtype,
) -> None:
self.cache_algorithm = cache_algorithm
self.timestep = 1
self.timesteps_prefetched = []
self.max_prefetch_depth = MAX_PREFETCH_DEPTH
self.lxu_cache_locations_list = []
self.lxu_cache_locations_empty = torch.empty(
0, device=self.current_device, dtype=torch.int32
).fill_(-1)
self.lxu_cache_locations = self.lxu_cache_locations_empty
self._indices = self.lxu_cache_locations_empty
self._offsets = self.lxu_cache_locations_empty
self._vbe_B_offsets = self.lxu_cache_locations_empty
self._vbe_max_B = -1
self.prefetch_stream: Optional[torch.cuda.Stream] = None
self._init_uvm_cache_stats()
# NOTE: no cache for CPU mode!
if cache_state.total_cache_hash_size == 0 or self.use_cpu:
self.register_buffer(
"lxu_cache_weights",
torch.zeros(0, 0, device=self.current_device, dtype=dtype),
)
# NOTE: make TorchScript work!
self.register_buffer(
"cache_hash_size_cumsum",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
"total_cache_hash_size",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
"cache_index_table_map",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
"lxu_cache_state",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
"lxu_state",
torch.zeros(1, dtype=torch.int64, device=self.current_device),
persistent=False,
)
self.register_buffer(
"cache_miss_counter",
torch.tensor([0, 0], dtype=torch.int64),
persistent=False,
)
self._init_uvm_cache_counter(cache_sets, persistent=False)
return
assert cache_load_factor > 0
element_size = 2 if dtype == torch.float16 else 4
if cache_sets <= 0:
total_memory = torch.cuda.get_device_properties(
self.current_device
).total_memory
free_memory = (
total_memory
- torch.cuda.memory_reserved(self.current_device)
- int(cache_reserved_memory)
)
assert free_memory > 0
cache_sets = (
int(cache_state.total_cache_hash_size * cache_load_factor)
+ DEFAULT_ASSOC
- 1
) // DEFAULT_ASSOC
cache_sets = 1 if cache_sets == 0 else cache_sets
cache_size = cache_sets * DEFAULT_ASSOC * element_size * self.max_D_cache
if cache_size > free_memory:
cache_sets = (
int(1.0 * free_memory / self.max_D_cache / element_size)
+ DEFAULT_ASSOC
- 1
) // DEFAULT_ASSOC
cache_load_factor = (
1.0 * cache_sets * DEFAULT_ASSOC / int(cache_state.total_cache_hash_size)
)
assert cache_sets > 0
if cache_algorithm == CacheAlgorithm.LFU:
assert cache_sets < 2**24 - 1
cache_size = cache_sets * DEFAULT_ASSOC * element_size * self.max_D_cache
self.log(
f"Using on-device cache with admission algorithm "
f"{cache_algorithm}, {cache_sets} sets, "
f"load_factor: {cache_load_factor : .3f}, "
f"cache_size: {cache_size / 1024.0 / 1024.0 / 1024.0 : .2f}GB, "
f"cache_precision: {dtype}"
)
self.total_cache_hash_size = cache_state.total_cache_hash_size
self.register_buffer(
"cache_hash_size_cumsum",
torch.tensor(
cache_state.cache_hash_size_cumsum,
device=self.current_device,
dtype=torch.int64,
),
)
self.register_buffer(
"cache_index_table_map",
torch.tensor(
cache_state.cache_index_table_map,
device=self.current_device,
dtype=torch.int32,
),
)
self.register_buffer(
"lxu_cache_state",
torch.zeros(
cache_sets, DEFAULT_ASSOC, device=self.current_device, dtype=torch.int64
).fill_(-1),
)
self.register_buffer(
"lxu_cache_weights",
torch.zeros(
cache_sets * DEFAULT_ASSOC,
self.max_D_cache,
device=self.current_device,
dtype=dtype,
),
)
self.register_buffer(
"lxu_state",
torch.zeros(
size=(
(self.total_cache_hash_size + 1,)
if cache_algorithm == CacheAlgorithm.LFU
else (cache_sets, DEFAULT_ASSOC)
),
device=self.current_device,
dtype=torch.int64,
),
)
self.register_buffer(
"cache_miss_counter",
torch.tensor([0, 0], device=self.current_device, dtype=torch.int64),
)
self._init_uvm_cache_counter(cache_sets, persistent=True)
if self.prefetch_pipeline:
# using the placeholder_autograd_tensor to make sure
# the hook is executed after the backward pass
# not using register_module_full_backward_hook
# due to https://github.com/pytorch/pytorch/issues/100528
self.placeholder_autograd_tensor.register_hook(
self._sync_stream_post_backward
)
self.register_full_backward_pre_hook(
self._update_cache_counter_and_locations
)
if cache_algorithm not in (CacheAlgorithm.LFU, CacheAlgorithm.LRU):
raise ValueError(
f"cache_algorithm must be {CacheAlgorithm.LRU} "
f"or {CacheAlgorithm.LFU}"
)
# pyre-ignore
def _recording_to_timer(
self, timer: Optional[AsyncSeriesTimer], **kwargs: Any
) -> Any:
if self.stats_reporter is not None and self.stats_reporter.should_report(
self.step
):
assert (
timer
), "We shouldn't be here, async timer must have been initiated if reporter is present."
return timer.recording(**kwargs)
# No-Op context manager
return contextlib.nullcontext()
def _sync_stream_post_backward(
self,
grad: Tensor,
) -> None:
"""
backward hook function when prefetch_pipeline is enabled.
With the pipeline, prefetch(batch_{i+2}) may overlap with backward(batch_{i}).
There is race condition that backward(batch_i) writes to UVM memory and
at the same time prefetch(batch_{i+2}) loads UVM memory to cache. This stream sync forces
backward(batch_i) to finish before prefetch(batch_{i+2}).
"""
if self.prefetch_stream is not None:
self.prefetch_stream.wait_stream(torch.cuda.current_stream())
def _update_cache_counter_and_locations(
self,
module: nn.Module,
grad_input: Union[Tuple[Tensor, ...], Tensor],
) -> None:
"""
Backward prehook function when prefetch_pipeline is enabled.
This function does 3 things:
1. backward stream waits for prefetch stream to finish.
Otherwise the prefetch(batch_{i+1}) might overlap with backward(batch_i).
If an idx is not in cache in batch_i, but it is being inserted in batch_{i+1},
there is race condition that backward(batch_i) writes to UVM memory and
at the same time prefetch(batch_{i+1}) loads UVM memory to cache.
2. decrement the lxu_cache_locking_counter to indicate the current batch is finished.
The lxu_cache_locking_counter is updated in both prefetch and TBE backward.
As there is no overlap between prefetch and backward, we can decrement either before or
after backward. It's better to decrement before lxu_cache_locations gets updated.
3. update lxu_cache_locations to address the cache inconsistency issue.
In the case that the same index is not inserted into cache in batch_i,
but it is inserted in batch_{i+1}, the cache can be invalid in
the sense that the cached weight for this index does not have the
backward update of batch_i.
Example of the issue is as follows:
idx is in batch_i, batch_{i+1}
prefetch(batch_i)
- failed to insert idx into cache, cache_locations_batch_i of idx is -1 (cache miss)
forward(batch_i)
prefetch(batch_{i+1})
- insert idx into cache, cache is loaded from host memory
backward(batch_i)
- cache_locations_batch_i of idx is -1, the host memory is updated
forward(batch_{i+1})
- OUTPUT IS WRONG. the weight for idx is fetched from cache, but the cache is outdated.
The fix to this cache inconsistency is to update the cache_locations_batch_i before backward of batch_i,
so that the cache gets updated correctly by the backward pass of TBE.
"""
if self.prefetch_stream is not None:
# need to wait for the prefetch of next batch,
# so that cache states are valid
with self._recording_to_timer(
self.bwd_wait_prefetch_timer,
context=self.step,
stream=torch.cuda.current_stream(),
):
torch.cuda.current_stream().wait_stream(self.prefetch_stream)
torch.ops.fbgemm.lxu_cache_locking_counter_decrement(
self.lxu_cache_locking_counter,
self.lxu_cache_locations,
)
# Recompute linear_cache_indices
linear_cache_indices = torch.ops.fbgemm.linearize_cache_indices(
self.cache_hash_size_cumsum,
self._indices,
self._offsets,
self._vbe_B_offsets,
self._vbe_max_B,
)
(
linear_unique_indices,
linear_unique_indices_length,
_,
) = torch.ops.fbgemm.get_unique_indices(
linear_cache_indices,
self.total_cache_hash_size,
compute_count=False,
)
torch.ops.fbgemm.lxu_cache_lookup(
linear_unique_indices,
self.lxu_cache_state,
self.total_cache_hash_size,
gather_cache_stats=False, # not collecting cache stats
num_uniq_cache_indices=linear_unique_indices_length,
lxu_cache_locations_output=self.lxu_cache_locations,
)
def _init_uvm_cache_counter(self, cache_sets: int, persistent: bool) -> None:
if self.prefetch_pipeline and persistent:
self.register_buffer(
"lxu_cache_locking_counter",
torch.zeros(
cache_sets,
DEFAULT_ASSOC,
device=self.current_device,
dtype=torch.int32,
),
)
else:
self.register_buffer(
"lxu_cache_locking_counter",
torch.zeros([0, 0], dtype=torch.int32, device=self.current_device),
persistent=persistent,
)
def _init_uvm_cache_stats(self) -> None:
if not self.gather_uvm_cache_stats:
# If uvm_cache_stats is not enabled, register stub entries via buffer to state_dict for TorchScript to JIT properly.
# Since we're not using these variables, we can choose minimize tensor size to keep state_dict size small.
self.register_buffer(
"uvm_cache_stats",
torch.zeros(
1,
device=self.current_device,
dtype=torch.int64,
),
persistent=False,
)
self.register_buffer(
"local_uvm_cache_stats",
torch.zeros(
1,
device=self.current_device,
dtype=torch.int32,
),
persistent=False,
)
else:
self.register_buffer(
"uvm_cache_stats",
torch.zeros(
size=(self.uvm_cache_stats_size,),
device=self.current_device,
dtype=torch.int64,
),
)
self.register_buffer(
"local_uvm_cache_stats",
torch.zeros(
size=(self.uvm_cache_stats_size,),
device=self.current_device,
dtype=torch.int32,
),
)
self.reset_uvm_cache_stats()
self.last_uvm_cache_print_state = torch.zeros_like(self.uvm_cache_stats)
def reset_cache_states(self) -> None:
if not self.lxu_cache_weights.numel():
return
self.lxu_cache_state.fill_(-1)
self.lxu_state.fill_(0)
self.timestep = 1
def reset_embedding_weight_momentum(
self,
pruned_indices: Tensor,
pruned_indices_offsets: Tensor,
logical_table_ids: Tensor,
buffer_ids: Tensor,
) -> None:
if self.optimizer == OptimType.NONE:
raise NotImplementedError(
f"Resetting embedding weight momentum is not supported for {self.optimizer}"
)
total_cache_hash_size = 0
if isinstance(self.total_cache_hash_size, Tensor):
total_cache_hash_size = self.total_cache_hash_size.item()
else:
total_cache_hash_size = self.total_cache_hash_size
rowwise = self.optimizer in [
OptimType.EXACT_ROWWISE_ADAGRAD,
]
if rowwise:
torch.ops.fbgemm.reset_weight_momentum(
dev_weights=self.weights_dev,
uvm_weights=self.weights_uvm,
lxu_cache_weights=self.lxu_cache_weights,
weights_placements=self.weights_placements,
weights_offsets=self.weights_offsets,
momentum1_dev=self.momentum1_dev,
momentum1_uvm=self.momentum1_uvm,
momentum1_placements=self.momentum1_placements,
momentum1_offsets=self.momentum1_offsets,
D_offsets=self.D_offsets,
pruned_indices=pruned_indices.to(device=self.current_device),
pruned_indices_offsets=pruned_indices_offsets.to(
device=self.current_device
),
logical_table_ids=logical_table_ids.to(device=self.current_device),
buffer_ids=buffer_ids.to(device=self.current_device),
cache_hash_size_cumsum=self.cache_hash_size_cumsum,
lxu_cache_state=self.lxu_cache_state,
total_cache_hash_size=total_cache_hash_size,
)
class DenseTableBatchedEmbeddingBagsCodegen(nn.Module):
"""
Table-batched version of nn.EmbeddingBag(sparse=False)
"""
weights: Tensor
weights_offsets: Tensor
D_offsets: Tensor
total_D: int
max_D: int
hash_size_cumsum: Tensor
total_hash_size_bits: int
embedding_specs: List[Tuple[int, int]]
def __init__(
self,
embedding_specs: List[Tuple[int, int]], # tuple of (rows, dims)
feature_table_map: Optional[List[int]] = None, # [T]
weights_precision: SparseType = SparseType.FP32,
pooling_mode: PoolingMode = PoolingMode.SUM,
use_cpu: bool = False,
output_dtype: SparseType = SparseType.FP32,
use_mtia: bool = False,
) -> None: # noqa C901 # tuple of (rows, dims,)
super(DenseTableBatchedEmbeddingBagsCodegen, self).__init__()
self.pooling_mode = pooling_mode
self.weights_precision = weights_precision
self.output_dtype: int = output_dtype.as_int()
table_embedding_dtype = weights_precision.as_dtype()
self.use_cpu: bool = use_cpu
self.use_mtia: bool = use_mtia
assert not (use_cpu and use_mtia), "Cannot use CPU and MTIA at the same time"
if self.use_cpu or self.pooling_mode == PoolingMode.NONE:
assert output_dtype in [
SparseType.FP32,
SparseType.FP16,
SparseType.BF16,
], "Fused pooled embedding quantization only supported for cuda."
# pyre-fixme[8]: Attribute has type `device`; used as `Union[int, device]`.
self.current_device: torch.device = (
torch.device("cpu")
if self.use_cpu
else (
torch.device(f"mtia:{torch.mtia.current_device()}")
if self.use_mtia
else torch.cuda.current_device()
)
)
self.embedding_specs = embedding_specs
(rows, dims) = zip(*embedding_specs)
T_ = len(self.embedding_specs)
assert T_ > 0
feature_table_map = (
feature_table_map if feature_table_map is not None else list(range(T_))
)
T = len(feature_table_map)
assert T_ <= T
feature_dims = [dims[t] for t in feature_table_map]
D_offsets = [0] + list(accumulate(feature_dims))
self.total_D = D_offsets[-1]
self.max_D = max(dims)
self.register_buffer(
"D_offsets",
torch.tensor(D_offsets, device=self.current_device, dtype=torch.int32),
)
assert self.D_offsets.numel() == T + 1
# Required for VBE
self.register_buffer(
"feature_dims",
torch.tensor(feature_dims, device="cpu", dtype=torch.int64),
)
hash_size_cumsum = [0] + list(accumulate(rows))
if hash_size_cumsum[-1] == 0:
self.total_hash_size_bits: int = 0
else:
self.total_hash_size_bits: int = int(log2(float(hash_size_cumsum[-1])) + 1)
# The last element is to easily access # of rows of each table by
# hash_size_cumsum[t + 1] - hash_size_cumsum[t]
hash_size_cumsum = [hash_size_cumsum[t] for t in feature_table_map] + [
hash_size_cumsum[-1]
]
self.register_buffer(
"hash_size_cumsum",
torch.tensor(
hash_size_cumsum, device=self.current_device, dtype=torch.int64
),
)
weights_offsets = [0] + list(
accumulate([row * dim for (row, dim) in embedding_specs])
)
self.weights = nn.Parameter(
torch.randn(
weights_offsets[-1],
device=self.current_device,
dtype=table_embedding_dtype,
)
)
for feature in range(T):
t = feature_table_map[feature]
row, dim = embedding_specs[t]
if (
self.weights[weights_offsets[t] : weights_offsets[t + 1]].numel()
!= row * dim
):
logging.info(
f"row {row} dim {dim} feature {feature} t {t} {self.weights[weights_offsets[t] : weights_offsets[t + 1]].numel()}"
)
assert (
self.weights[weights_offsets[t] : weights_offsets[t + 1]].numel()
== row * dim
)
assert self.hash_size_cumsum[feature] == sum(
row for (row, _) in embedding_specs[:t]
)
self.weights_physical_offsets: List[int] = weights_offsets
weights_offsets = [weights_offsets[t] for t in feature_table_map]
self.register_buffer(
"weights_offsets",
torch.tensor(
weights_offsets, device=self.current_device, dtype=torch.int64
),
)
@torch.jit.ignore
def _generate_vbe_metadata(
self,
offsets: Tensor,
batch_size_per_feature_per_rank: Optional[List[List[int]]],
) -> invokers.lookup_args.VBEMetadata:
# Blocking D2H copy, but only runs at first call
self.feature_dims = self.feature_dims.cpu()
return generate_vbe_metadata(
offsets,
batch_size_per_feature_per_rank,
OptimType.NONE,
self.pooling_mode,
self.feature_dims,
self.current_device,
)
def forward(
self,
indices: Tensor,
offsets: Tensor,
per_sample_weights: Optional[Tensor] = None,
feature_requires_grad: Optional[Tensor] = None,
batch_size_per_feature_per_rank: Optional[List[List[int]]] = None,
) -> Tensor:
# Generate VBE metadata
vbe_metadata = self._generate_vbe_metadata(
offsets, batch_size_per_feature_per_rank
)
(indices, offsets) = indices.long(), offsets.long()
# Force casting per_sample_weights to float
if per_sample_weights is not None:
per_sample_weights = per_sample_weights.float()
return torch.ops.fbgemm.dense_embedding_codegen_lookup_function(
dev_weights=self.weights,
weights_offsets=self.weights_offsets,
D_offsets=self.D_offsets,
total_D=self.total_D,
max_D=self.max_D,
hash_size_cumsum=self.hash_size_cumsum,
total_hash_size_bits=self.total_hash_size_bits,
indices=indices,
offsets=offsets,
pooling_mode=self.pooling_mode,
indice_weights=per_sample_weights,
feature_requires_grad=feature_requires_grad,
output_dtype=self.output_dtype,
B_offsets=vbe_metadata.B_offsets,
vbe_output_offsets_feature_rank=vbe_metadata.output_offsets_feature_rank,
vbe_B_offsets_rank_per_feature=vbe_metadata.B_offsets_rank_per_feature,
max_B=vbe_metadata.max_B,
max_B_feature_rank=vbe_metadata.max_B_feature_rank,
vbe_output_size=vbe_metadata.output_size,
)
@torch.jit.export
def split_embedding_weights(self) -> List[Tensor]:
"""
Returns a list of weights, split by table
"""
splits = []
for t, (rows, dim) in enumerate(self.embedding_specs):
offset = self.weights_physical_offsets[t]
splits.append(
self.weights.detach()[offset : offset + rows * dim].view(rows, dim)
)
return splits
def init_embedding_weights_uniform(self, min_val: float, max_val: float) -> None:
splits = self.split_embedding_weights()
for param in splits:
param.uniform_(min_val, max_val)
