ColossalAI/tests/kit/model_zoo/torchrec/torchrec.py

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from functools import partial
import torch
from torchrec.models import deepfm, dlrm
from torchrec.modules.embedding_configs import EmbeddingBagConfig
from torchrec.modules.embedding_modules import EmbeddingBagCollection
from torchrec.sparse.jagged_tensor import KeyedJaggedTensor, KeyedTensor
from ..registry import model_zoo
BATCH = 2
SHAPE = 10
def gen_kt():
KT = KeyedTensor(keys=["f1", "f2"], length_per_key=[SHAPE, SHAPE], values=torch.rand((BATCH, 2 * SHAPE)))
return KT
# KeyedJaggedTensor
def gen_kjt():
KJT = KeyedJaggedTensor.from_offsets_sync(
keys=["f1", "f2"], values=torch.tensor([1, 2, 3, 4, 5, 6, 7, 8]), offsets=torch.tensor([0, 2, 4, 6, 8])
)
return KJT
data_gen_fn = lambda: dict(features=torch.rand((BATCH, SHAPE)))
def interaction_arch_data_gen_fn():
KT = gen_kt()
return dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KT)
def simple_dfm_data_gen_fn():
KJT = gen_kjt()
return dict(dense_features=torch.rand((BATCH, SHAPE)), sparse_features=KJT)
def sparse_arch_data_gen_fn():
KJT = gen_kjt()
return dict(features=KJT)
def output_transform_fn(x):
if isinstance(x, KeyedTensor):
output = dict()
for key in x.keys():
output[key] = x[key]
return output
else:
return dict(output=x)
def output_transform_fn(x):
if isinstance(x, KeyedTensor):
output = dict()
for key in x.keys():
output[key] = x[key]
return output
else:
return dict(output=x)
def get_ebc():
# EmbeddingBagCollection
eb1_config = EmbeddingBagConfig(name="t1", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f1"])
eb2_config = EmbeddingBagConfig(name="t2", embedding_dim=SHAPE, num_embeddings=SHAPE, feature_names=["f2"])
return EmbeddingBagCollection(tables=[eb1_config, eb2_config], device=torch.device("cpu"))
def sparse_arch_model_fn():
ebc = get_ebc()
return deepfm.SparseArch(ebc)
def simple_deep_fmnn_model_fn():
ebc = get_ebc()
return deepfm.SimpleDeepFMNN(SHAPE, ebc, SHAPE, SHAPE)
def dlrm_model_fn():
ebc = get_ebc()
return dlrm.DLRM(ebc, SHAPE, [SHAPE, SHAPE], [5, 1])
def dlrm_sparsearch_model_fn():
ebc = get_ebc()
return dlrm.SparseArch(ebc)
model_zoo.register(
name="deepfm_densearch",
model_fn=partial(deepfm.DenseArch, SHAPE, SHAPE, SHAPE),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="deepfm_interactionarch",
model_fn=partial(deepfm.FMInteractionArch, SHAPE * 3, ["f1", "f2"], SHAPE),
data_gen_fn=interaction_arch_data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="deepfm_overarch",
model_fn=partial(deepfm.OverArch, SHAPE),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="deepfm_simpledeepfmnn",
model_fn=simple_deep_fmnn_model_fn,
data_gen_fn=simple_dfm_data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="deepfm_sparsearch",
model_fn=sparse_arch_model_fn,
data_gen_fn=sparse_arch_data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="dlrm", model_fn=dlrm_model_fn, data_gen_fn=simple_dfm_data_gen_fn, output_transform_fn=output_transform_fn
)
model_zoo.register(
name="dlrm_densearch",
model_fn=partial(dlrm.DenseArch, SHAPE, [SHAPE, SHAPE]),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="dlrm_interactionarch",
model_fn=partial(dlrm.InteractionArch, 2),
data_gen_fn=interaction_arch_data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="dlrm_overarch",
model_fn=partial(dlrm.OverArch, SHAPE, [5, 1]),
data_gen_fn=data_gen_fn,
output_transform_fn=output_transform_fn,
)
model_zoo.register(
name="dlrm_sparsearch",
model_fn=dlrm_sparsearch_model_fn,
data_gen_fn=sparse_arch_data_gen_fn,
output_transform_fn=output_transform_fn,
)