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Merged
facebook-github-bot merged 1 commit into from
Mar 29, 2025
Merged

Update mimi export test #9755

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30 changes: 19 additions & 11 deletions examples/models/moshi/mimi/test_mimi.py
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Original file line number Diff line number Diff line change
Expand Up @@ -173,15 +173,23 @@ def __init__(self, mimi: nn.Module):
self.mimi_model = mimi

def forward(self, x):
return self.mimi_model.decode(x)
x = x.transpose(1, 2)
x = self.mimi_model.upsample(x)
(emb,) = self.mimi_model.decoder_transformer(x)
emb.transpose(1, 2)
with self.mimi_model._context_for_encoder_decoder:
out = self.mimi_model.decoder(emb)
return out

sample_pcm = torch.tensor(self.sample_pcm, device=self.device)[None]
pcm_chunk_size = int(self.mimi.sample_rate / self.mimi.frame_rate)
chunk = sample_pcm[..., 0:pcm_chunk_size]
input = self.mimi.encode(chunk)
emb_input = torch.rand(1, 1, 512, device="cpu")

mimi_decode = MimiDecode(self.mimi)
exported_decode: ExportedProgram = export(mimi_decode, (input,), strict=False)
mimi_decode.eval()
mimi_decode(emb_input)

exported_decode: ExportedProgram = export(
mimi_decode, (emb_input,), strict=False
)
quantization_config = get_symmetric_quantization_config(
is_per_channel=True,
is_dynamic=True,
Expand All @@ -190,12 +198,12 @@ def forward(self, x):
quantizer.set_global(quantization_config)
m = exported_decode.module()
m = prepare_pt2e(m, quantizer)
m(input)
m(emb_input)
m = convert_pt2e(m)
print("quantized graph:")
print(m.graph)
# Export quantized module
exported_decode: ExportedProgram = export(m, (input,), strict=False)
exported_decode: ExportedProgram = export(m, (emb_input,), strict=False)
# Lower
edge_manager = to_edge_transform_and_lower(
exported_decode,
Expand All @@ -208,16 +216,16 @@ def forward(self, x):
with open(output_file, "wb") as file:
exec_prog.write_to_file(file)

eager_res = mimi_decode(input)
eager_res = mimi_decode(emb_input)
runtime = Runtime.get()
program = runtime.load_program(output_file)
method = program.load_method("forward")
flattened_x = tree_flatten(input)[0]
flattened_x = tree_flatten(emb_input)[0]
res = method.execute(flattened_x)
# Compare results
sqnr = compute_sqnr(eager_res, res[0])
print(f"SQNR: {sqnr}")
torch.testing.assert_close(eager_res, res[0], atol=1e-3, rtol=1e-3)
torch.testing.assert_close(eager_res, res[0], atol=4e-3, rtol=1e-3)


if __name__ == "__main__":
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