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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
import torch
from nemo.collections.asr.parts.context_biasing.biasing_multi_model import (
GPUBiasingMultiModel,
GPUBiasingMultiModelReference,
)
from nemo.collections.asr.parts.context_biasing.boosting_graph_batched import (
BoostingTreeModelConfig,
GPUBoostingTreeModel,
)
from nemo.core.utils.optional_libs import TRITON_AVAILABLE
DEVICES = [torch.device("cpu")]
if torch.cuda.is_available():
DEVICES.append(torch.device("cuda"))
if hasattr(torch, "mps") and torch.mps.is_available():
DEVICES.append(torch.device("mps"))
# Triton only works on CUDA, so only test use_triton=True if Triton is available
USE_TRITON_OPTIONS = [False, True] if TRITON_AVAILABLE else [False]
def create_boosting_model(phrases: list[str], tokenizer, device: torch.device) -> GPUBoostingTreeModel:
"""Helper to create boosting model from phrases"""
cfg = BoostingTreeModelConfig(key_phrases_list=phrases, context_score=1.0)
model = GPUBoostingTreeModel.from_config(cfg, tokenizer=tokenizer)
return model.to(device)
class TestGPUBiasingMultiModel:
@pytest.mark.unit
@pytest.mark.with_downloads
@pytest.mark.parametrize("device", DEVICES)
def test_add_models_incremental(self, stt_en_conformer_transducer_small, device: torch.device):
"""Test adding 2 boosting models one-by-one, verifying arcs and states are correctly merged."""
tokenizer = stt_en_conformer_transducer_small.tokenizer
vocab_size = tokenizer.vocab_size
# Create empty multi-model
multi_model = GPUBiasingMultiModel(vocab_size=vocab_size).to(device)
# Initially empty
assert multi_model.num_models == 0
assert multi_model.has_models() is False
assert multi_model.num_states_total == 0
assert multi_model.num_arcs_extended_total == 0
# Create and add first model
model1 = create_boosting_model(["hello", "world"], tokenizer, device)
model_id1 = multi_model.add_model(model1, alpha=1.0)
# Verify after first model
assert model_id1 == 0
assert multi_model.num_models == 1
assert multi_model.has_models() is True
assert multi_model.model2active[model_id1].item() is True
assert multi_model.num_states_total == model1.num_states
assert multi_model.num_arcs_extended_total == model1.num_arcs_extended
assert multi_model.model2num_states[model_id1].item() == model1.num_states
assert multi_model.model2num_arcs_extended[model_id1].item() == model1.num_arcs_extended
# Create and add second model
model2 = create_boosting_model(["test", "one", "two"], tokenizer, device)
model_id2 = multi_model.add_model(model2, alpha=1.5)
# Verify after second model
assert model_id2 == 1
assert multi_model.num_models == 2
assert multi_model.has_models() is True
assert multi_model.model2active[model_id1].item() is True
assert multi_model.model2active[model_id2].item() is True
assert multi_model.num_states_total == model1.num_states + model2.num_states
assert multi_model.num_arcs_extended_total == model1.num_arcs_extended + model2.num_arcs_extended
# Verify offsets
assert multi_model.model2states_offset[model_id1].item() == 0
assert multi_model.model2states_offset[model_id2].item() == model1.num_states
assert multi_model.model2arcs_offset[model_id1].item() == 0
assert multi_model.model2arcs_offset[model_id2].item() == model1.num_arcs_extended
# Verify init states work
init_states = multi_model.get_init_states(batch_size=4, bos=True)
assert init_states.shape == (4,)
assert init_states.device.type == device.type
@pytest.mark.unit
@pytest.mark.with_downloads
@pytest.mark.parametrize("device", DEVICES)
def test_add_then_remove_model(self, stt_en_conformer_transducer_small, device: torch.device):
"""Test adding 2 models then removing the first one."""
tokenizer = stt_en_conformer_transducer_small.tokenizer
vocab_size = tokenizer.vocab_size
multi_model = GPUBiasingMultiModel(vocab_size=vocab_size).to(device)
# Add two models
model1 = create_boosting_model(["alpha", "beta"], tokenizer, device)
model2 = create_boosting_model(["gamma", "delta"], tokenizer, device)
model_id1 = multi_model.add_model(model1, alpha=1.0)
model_id2 = multi_model.add_model(model2, alpha=2.0)
# Store counts before removal
model1_num_states = model1.num_states
model1_num_arcs = model1.num_arcs_extended
total_states_before = multi_model.num_states_total
total_arcs_before = multi_model.num_arcs_extended_total
assert multi_model.model2active[model_id1].item() is True
assert multi_model.model2active[model_id2].item() is True
# Remove first model
multi_model.remove_model(model_id1)
# Verify removal
assert model_id1 in multi_model.free_ids
assert multi_model.model2active[model_id1].item() is False
assert multi_model.model2active[model_id2].item() is True
assert multi_model.model2alpha[model_id1].item() == 0.0
assert multi_model.model2alpha[model_id2].item() == 2.0
# Verify state/arc counts decreased
assert multi_model.num_states_total == total_states_before - model1_num_states
assert multi_model.num_arcs_extended_total == total_arcs_before - model1_num_arcs
# Verify model2 offset updated (shifted left)
assert multi_model.model2states_offset[model_id2].item() == 0
assert multi_model.model2arcs_offset[model_id2].item() == 0
@pytest.mark.unit
@pytest.mark.with_downloads
@pytest.mark.parametrize("device", DEVICES)
def test_model_id_reuse(self, stt_en_conformer_transducer_small, device):
"""Test that removed model IDs are reused."""
tokenizer = stt_en_conformer_transducer_small.tokenizer
vocab_size = tokenizer.vocab_size
multi_model = GPUBiasingMultiModel(vocab_size=vocab_size).to(device)
# Add model1 -> id=0
model1 = create_boosting_model(["first"], tokenizer, device)
model_id1 = multi_model.add_model(model1)
assert model_id1 == 0
# Add model2 -> id=1
model2 = create_boosting_model(["second"], tokenizer, device)
model_id2 = multi_model.add_model(model2)
assert model_id2 == 1
# Remove model1
multi_model.remove_model(model_id1)
assert model_id1 in multi_model.free_ids
# Add model3 -> should reuse id=0
model3 = create_boosting_model(["third"], tokenizer, device)
model_id3 = multi_model.add_model(model3)
assert model_id3 == model_id1 # Reused ID
assert model_id1 not in multi_model.free_ids # No longer free
# Verify model3 is active
assert multi_model.model2active[model_id3].item() is True
@pytest.mark.unit
@pytest.mark.with_downloads
@pytest.mark.parametrize("device", DEVICES)
@pytest.mark.parametrize("batch_size", [1, 4])
@pytest.mark.parametrize("use_triton", USE_TRITON_OPTIONS)
@pytest.mark.parametrize("bos", [True, False])
def test_advance_matches_reference(
self, stt_en_conformer_transducer_small, device: torch.device, batch_size: int, use_triton, bos: bool
):
"""Verify GPUBiasingMultiModel produces same scores/states as reference implementation."""
tokenizer = stt_en_conformer_transducer_small.tokenizer
vocab_size = tokenizer.vocab_size
# Create both implementations
multi_model = GPUBiasingMultiModel(vocab_size=vocab_size, use_triton=use_triton).to(device)
reference = GPUBiasingMultiModelReference(vocab_size=vocab_size).to(device)
# Create boosting models with same phrases
phrases1 = ["hello world", "test"]
phrases2 = ["neural", "network"]
model1_mm = create_boosting_model(phrases1, tokenizer, device)
model1_ref = create_boosting_model(phrases1, tokenizer, device)
model2_mm = create_boosting_model(phrases2, tokenizer, device)
model2_ref = create_boosting_model(phrases2, tokenizer, device)
# Add models to both with same alpha values
alpha1, alpha2 = 1.0, 1.5
model_id1_mm = multi_model.add_model(model1_mm, alpha=alpha1)
model_id1_ref = reference.add_model(model1_ref, alpha=alpha1)
model_id2_mm = multi_model.add_model(model2_mm, alpha=alpha2)
model_id2_ref = reference.add_model(model2_ref, alpha=alpha2)
assert model_id1_mm == model_id1_ref
assert model_id2_mm == model_id2_ref
# Get initial states
states_mm = multi_model.get_init_states(batch_size=batch_size, bos=bos)
states_ref = reference.get_init_states(batch_size=batch_size, bos=bos)
# Create model_ids tensor with alternating models
model_ids = torch.tensor(
[model_id1_mm if i % 2 == 0 else model_id2_mm for i in range(batch_size)],
dtype=torch.long,
device=device,
)
# Call advance on both
scores_mm, next_states_mm = multi_model.advance(states_mm, model_ids)
scores_ref, next_states_ref = reference.advance(states_ref, model_ids)
# Verify shapes
assert scores_mm.shape == (batch_size, vocab_size)
assert next_states_mm.shape == (batch_size, vocab_size)
assert scores_ref.shape == (batch_size, vocab_size)
assert next_states_ref.shape == (batch_size, vocab_size)
# Verify scores and states match
assert torch.allclose(
scores_mm, scores_ref, atol=1e-5
), f"Scores mismatch: max diff = {(scores_mm - scores_ref).abs().max()}"
assert torch.equal(next_states_mm, next_states_ref), "Next states mismatch"
@pytest.mark.unit
@pytest.mark.parametrize("device", DEVICES)
def test_empty_multi_model(self, device: torch.device):
"""Test behavior of empty multi-model."""
vocab_size = 100
multi_model = GPUBiasingMultiModel(vocab_size=vocab_size, use_triton=False).to(device)
# Verify empty state
assert multi_model.has_models() is False
assert multi_model.num_models == 0
assert multi_model.num_states_total == 0
assert multi_model.num_arcs_extended_total == 0
# get_init_states should work and return START_STATE
init_states = multi_model.get_init_states(batch_size=4, bos=True)
assert init_states.shape == (4,)
assert (init_states == GPUBiasingMultiModel.START_STATE).all()
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