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Use quantizable LSTM in test when flow has quantize=True #14893

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Oct 9, 2025
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Use quantizable LSTM in test when flow has quantize=True #14893

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87 changes: 63 additions & 24 deletions backends/test/suite/operators/test_lstm.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,6 @@
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# Copyright 2025 Arm Limited and/or its affiliates.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
Expand All @@ -15,6 +16,11 @@
operator_test,
OperatorTest,
)
from torch.nn.quantizable.modules.rnn import LSTM as QuantizableLSTM


def _get_lstm_cls(use_quantizable_lstm: bool):
return QuantizableLSTM if use_quantizable_lstm else torch.nn.LSTM


class Model(torch.nn.Module):
Expand All @@ -27,9 +33,11 @@ def __init__(
batch_first=True,
dropout=0.0,
bidirectional=False,
use_quantizable_lstm: bool = False,
):
super().__init__()
self.lstm = torch.nn.LSTM(
lstm_cls = _get_lstm_cls(use_quantizable_lstm)
self.lstm = lstm_cls(
input_size=input_size,
hidden_size=hidden_size,
num_layers=num_layers,
Expand All @@ -47,116 +55,144 @@ def forward(self, x):
class LSTM(OperatorTest):
@dtype_test
def test_lstm_dtype(self, flow: TestFlow, dtype) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(num_layers=2).to(dtype),
Model(num_layers=2, use_quantizable_lstm=use_quantizable_lstm).to(dtype),
((torch.rand(1, 10, 64) * 10).to(dtype),), # (batch=1, seq_len, input_size)
flow,
)

@dtype_test
def test_lstm_no_bias_dtype(self, flow: TestFlow, dtype) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(num_layers=2, bias=False).to(dtype),
Model(
num_layers=2, bias=False, use_quantizable_lstm=use_quantizable_lstm
).to(dtype),
((torch.rand(1, 10, 64) * 10).to(dtype),),
flow,
)

def test_lstm_feature_sizes(self, flow: TestFlow) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(input_size=32, hidden_size=16),
Model(
input_size=32,
hidden_size=16,
use_quantizable_lstm=use_quantizable_lstm,
),
(torch.randn(1, 8, 32),), # (batch=1, seq_len, input_size)
flow,
)
self._test_op(
Model(input_size=128, hidden_size=64),
Model(
input_size=128,
hidden_size=64,
use_quantizable_lstm=use_quantizable_lstm,
),
(torch.randn(1, 12, 128),),
flow,
)
self._test_op(
Model(input_size=256, hidden_size=128),
Model(
input_size=256,
hidden_size=128,
use_quantizable_lstm=use_quantizable_lstm,
),
(torch.randn(1, 6, 256),),
flow,
)
self._test_op(
Model(input_size=16, hidden_size=32),
Model(
input_size=16,
hidden_size=32,
use_quantizable_lstm=use_quantizable_lstm,
),
(torch.randn(1, 5, 16),),
flow,
)

def test_lstm_batch_sizes(self, flow: TestFlow) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(),
Model(use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(8, 10, 64),),
flow,
)
self._test_op(
Model(),
Model(use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(32, 10, 64),),
flow,
)
self._test_op(
Model(),
Model(use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(100, 10, 64),),
flow,
)

def test_lstm_seq_lengths(self, flow: TestFlow) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(),
Model(use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(1, 5, 64),),
flow,
)
self._test_op(
Model(),
Model(use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(1, 20, 64),),
flow,
)
self._test_op(
Model(),
Model(use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(1, 50, 64),),
flow,
)

def test_lstm_batch_first_false(self, flow: TestFlow) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(batch_first=False),
Model(batch_first=False, use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(10, 1, 64),), # (seq_len, batch=1, input_size)
flow,
)

def test_lstm_num_layers(self, flow: TestFlow) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(num_layers=2),
Model(num_layers=2, use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(1, 10, 64),),
flow,
)
self._test_op(
Model(num_layers=3),
Model(num_layers=3, use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(1, 10, 64),),
flow,
)

def test_lstm_bidirectional(self, flow: TestFlow) -> None:
use_quantizable_lstm = flow.quantize
self._test_op(
Model(bidirectional=True),
Model(bidirectional=True, use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(1, 10, 64),),
flow,
)

def test_lstm_with_dropout(self, flow: TestFlow) -> None:
# Note: Dropout is only effective with num_layers > 1
use_quantizable_lstm = flow.quantize
self._test_op(
Model(num_layers=2, dropout=0.2),
Model(num_layers=2, dropout=0.2, use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(1, 10, 64),),
flow,
)

def test_lstm_with_initial_states(self, flow: TestFlow) -> None:
# Create a model that accepts initial states
class ModelWithStates(torch.nn.Module):
def __init__(self):
def __init__(self, use_quantizable_lstm: bool = False):
super().__init__()
self.lstm = torch.nn.LSTM(
lstm_cls = _get_lstm_cls(use_quantizable_lstm)
self.lstm = lstm_cls(
input_size=64,
hidden_size=32,
num_layers=2,
Expand All @@ -169,9 +205,10 @@ def forward(self, x, h0, c0):
batch_size = 1
num_layers = 2
hidden_size = 32
use_quantizable_lstm = flow.quantize

self._test_op(
ModelWithStates(),
ModelWithStates(use_quantizable_lstm=use_quantizable_lstm),
(
torch.randn(batch_size, 10, 64), # input
torch.randn(num_layers, batch_size, hidden_size), # h0
Expand All @@ -183,9 +220,10 @@ def forward(self, x, h0, c0):
def test_lstm_return_hidden_states(self, flow: TestFlow) -> None:
# Create a model that returns both output and hidden states
class ModelWithHiddenStates(torch.nn.Module):
def __init__(self):
def __init__(self, use_quantizable_lstm: bool = False):
super().__init__()
self.lstm = torch.nn.LSTM(
lstm_cls = _get_lstm_cls(use_quantizable_lstm)
self.lstm = lstm_cls(
input_size=64,
hidden_size=32,
num_layers=2,
Expand All @@ -200,9 +238,10 @@ def forward(self, x):
batch_size = 1
seq_len = 10
input_size = 64
use_quantizable_lstm = flow.quantize

self._test_op(
ModelWithHiddenStates(),
ModelWithHiddenStates(use_quantizable_lstm=use_quantizable_lstm),
(torch.randn(batch_size, seq_len, input_size),),
flow,
)
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