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[do not land] printing shapes for autoquant #695

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30 changes: 22 additions & 8 deletions benchmarks/benchmark_aq.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,25 +133,39 @@ def _bench_quantized_tensor_subclass_perf(api, ref_api, M, N, K, kwargs=None):
WARMUP = 20
RUNS = 100

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)

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)

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)


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}")

if __name__ == "__main__" and TORCH_VERSION_AT_LEAST_2_4 and torch.cuda.is_available():
all_shapes = [
(20, 2048, 2048),
]
# all_shapes = set([
# (20, 2048, 2048),
# ])
all_shapes = set([
(6, 12288, 4096),
(6, 4096, 4096),
(6, 11008, 4096),
(6, 4096, 11008),
(6, 32000, 4096),
(1, 12288, 4096),
(1, 4096, 4096),
(1, 11008, 4096),
(1, 4096, 11008),
(1, 32000, 4096),
])

print("_int8da_int8w_api")
from torchao.quantization.quant_api import change_linear_weights_to_int8_dqtensors
Expand Down
13 changes: 12 additions & 1 deletion torchao/_models/llama/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,6 +234,16 @@ def main(

# do autoquantization
model.finalize_autoquant()

from torchao.quantization.autoquant import AUTOQUANT_CACHE
shapes = []
for k in AUTOQUANT_CACHE.keys():
act = k[1]
w = k[2]
M, K = act
N = w[0]
shapes.append((M, N, K))
print("all shapes:", set(shapes))
else:
if not TORCH_VERSION_AT_LEAST_2_5:
unwrap_tensor_subclass(model)
Expand Down Expand Up @@ -375,10 +385,11 @@ def callback(x):
parser.add_argument('--profile', type=Path, default=None, help='Profile path.')
parser.add_argument('--device', type=str, default=default_device, help='Device to use')
parser.add_argument('--precision', type=lambda x: getattr(torch, x.split(".")[-1]), default=torch.bfloat16, help='dtype precision to use')
parser.add_argument('--print_autoquant_m_n_k', action='store_true', help='Whether to print the M, N, K shapes in AUTOQUANT_CACHE for micro benchmarking.')
parser.add_argument('--write_result', type=Path, default=None, help='Path where to write the result')

args = parser.parse_args()
main(
args.prompt, args.interactive, args.num_samples, args.max_new_tokens, args.top_k,
args.temperature, args.checkpoint_path, args.quantization, args.kv_cache_quantization, args.save, args.compile, args.compile_prefill, args.profile, args.device, args.precision, args.write_result
args.temperature, args.checkpoint_path, args.quantization, args.kv_cache_quantization, args.save, args.compile, args.compile_prefill, args.profile, args.device, args.precision, args.print_autoquant_m_n_k, args.write_result
)
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