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Drop old quantization flows #3115

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Drop old quantization flows #3115

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56d16dc
drop old quantization flows
namgyu-youn Oct 2, 2025
964b7e4
remove old quantization flows
namgyu-youn Oct 2, 2025
fbb2f2b
inline quantization API in benchmarks
namgyu-youn Oct 2, 2025
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89 changes: 24 additions & 65 deletions benchmarks/benchmark_aq.py
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@namgyu-youn namgyu-youn Oct 2, 2025 •
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How about inlining _int8wo_api, _int8da_int8w_api, _int4wo_api ? They are used only once across codebase.

Original file line number Diff line number Diff line change
Expand Up @@ -16,30 +16,6 @@
_replace_with_custom_fn_if_matches_filter,
quantize_,
)
from torchao.quantization.subclass import (
Int4WeightOnlyQuantizedLinearWeight,
Int8WeightOnlyQuantizedLinearWeight,
)


def _int8wo_api(mod, **kwargs):
quantize_(mod, Int8WeightOnlyConfig(**kwargs), set_inductor_config=False)


def _int8da_int8w_api(mod, **kwargs):
quantize_(
mod,
Int8DynamicActivationInt8WeightConfig(**kwargs),
set_inductor_config=False,
)


def _int4wo_api(mod, **kwargs):
kwargs_copy = kwargs.copy()
if "groupsize" in kwargs_copy:
kwargs_copy["group_size"] = kwargs_copy["groupsize"]
del kwargs_copy["groupsize"]
quantize_(mod, Int4WeightOnlyConfig(**kwargs_copy), set_inductor_config=False)


class ToyLinearModel(torch.nn.Module):
Expand Down Expand Up @@ -117,38 +93,18 @@ def _ref_change_linear_weights_to_woqtensors(model, filter_fn=None, **kwargs):
return _ref_change_linear_weights_to_woqtensors


_ref_change_linear_weights_to_int8_woqtensors = (
_get_ref_change_linear_weights_to_woqtensors(Int8WeightOnlyQuantizedLinearWeight)
)
_ref_change_linear_weights_to_int4_woqtensors = (
_get_ref_change_linear_weights_to_woqtensors(Int4WeightOnlyQuantizedLinearWeight)
)


torch._dynamo.config.cache_size_limit = 50000


@torch.no_grad
def _bench_quantized_tensor_subclass_perf(api, ref_api, M, N, K, kwargs=None):
if kwargs is None:
kwargs = {}

def _bench_quantized_tensor_subclass_perf(api, config, M, N, K):
m = ToyLinearModel(
M, N, K, has_bias=True, dtype=torch.bfloat16, device="cuda"
).eval()
m_bf16 = copy.deepcopy(m)
m_ref = copy.deepcopy(m)
example_inputs = m.example_inputs()

api(m, **kwargs)

# reference
ref_api(m_ref, **kwargs)

res = m(*example_inputs)
ref = m_ref(*example_inputs)

assert torch.equal(res, ref)
api(m, config) # Pass both model and config

# perf comparison
from torchao.utils import benchmark_model
Expand All @@ -158,22 +114,17 @@ def _bench_quantized_tensor_subclass_perf(api, ref_api, M, N, K, kwargs=None):
RUNS = 100

torch._dynamo.reset()
m_ref = torch.compile(m_ref, mode="max-autotune", fullgraph=True)
benchmark_model(m_ref, WARMUP, example_inputs)
ref_elapsed_time = benchmark_model(m_ref, RUNS, example_inputs)
m_bf16 = torch.compile(m_bf16, mode="max-autotune", fullgraph=True)
benchmark_model(m_bf16, WARMUP, example_inputs)
bf16_elapsed_time = benchmark_model(m_bf16, RUNS, example_inputs)

torch._dynamo.reset()
m = torch.compile(m, mode="max-autotune", fullgraph=True)
benchmark_model(m, WARMUP, example_inputs)
elapsed_time = benchmark_model(m, RUNS, example_inputs)

torch._dynamo.reset()
m_bf16 = torch.compile(m_bf16, mode="max-autotune", fullgraph=True)
benchmark_model(m_bf16, WARMUP, example_inputs)
bf16_elapsed_time = benchmark_model(m_bf16, RUNS, example_inputs)

print(
f"{(M, N, K)}: elapsed time: {elapsed_time}, ref elapsed time: {ref_elapsed_time}, bf16 elapsed time: {bf16_elapsed_time}"
f"{(M, N, K)}: elapsed time: {elapsed_time}, bf16 elapsed time: {bf16_elapsed_time}"
)


Expand All @@ -182,24 +133,32 @@ def _bench_quantized_tensor_subclass_perf(api, ref_api, M, N, K, kwargs=None):
(20, 2048, 2048),
]

print("_int8da_int8w_api")

print("Int8DynamicActivationInt8WeightConfig")
for M, N, K in all_shapes:
_bench_quantized_tensor_subclass_perf(
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Temporarily updated to use new APIs 2 times to fix CI, but maybe we can update _bench_quantized_tensor_subclass_perf to compare only original vs. new quantization flows?

_int8da_int8w_api, _ref_change_linear_weights_to_int8_dqtensors, M, N, K
quantize_,
Int8DynamicActivationInt8WeightConfig(),
M,
N,
K,
)

print("_int8wo_api")

print("Int8WeightOnlyConfig")
for M, N, K in all_shapes:
_bench_quantized_tensor_subclass_perf(
_int8wo_api, _ref_change_linear_weights_to_int8_woqtensors, M, N, K
quantize_,
Int8WeightOnlyConfig(),
M,
N,
K,
)

print("_int4wo_api")
kwargs = {"groupsize": 32, "version": 1}

print("Int4WeightOnlyConfig")
for M, N, K in all_shapes:
_bench_quantized_tensor_subclass_perf(
_int4wo_api, _ref_change_linear_weights_to_int4_woqtensors, M, N, K, kwargs
quantize_,
Int4WeightOnlyConfig(group_size=32),
M,
N,
K,
)
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