fix: resolve CPU runtime error introduced by previous commit (c1e365f) (#2375)

BienBoy

2025-02-05 6ebf6e48eb0368518452312c803c58a65fe9bd26

fix: resolve CPU runtime error introduced by previous commit (c1e365f) (#2375)

Fixed a bug that caused a runtime error when running the model on CPU, which was introduced in commit c1e365fea09aafda387cac12fdff43d28c598979. The error was related to incorrect handling of device placement.

 funasr/auto/auto_model.py

@@ -366,7 +366,10 @@
        if pbar:
            # pbar.update(1)
            pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
        with torch.cuda.device(next(model.parameters()).device):

        device = next(model.parameters()).device
        if device.type == 'cuda':
            with torch.cuda.device():
            torch.cuda.empty_cache()
        return asr_result_list


 funasr/bin/train.py

@@ -221,7 +221,9 @@
            )
            trainer.start_step = 0

            with torch.cuda.device(kwargs["device"]):
            device = next(model.parameters()).device
            if device.type == 'cuda':
                with torch.cuda.device():
                torch.cuda.empty_cache()

            time_escaped = (time.perf_counter() - time_slice_i) / 3600.0

 funasr/bin/train_ds.py

@@ -184,7 +184,9 @@
            )
            trainer.start_step = 0

            with torch.cuda.device(kwargs["device"]):
            device = next(model.parameters()).device
            if device.type == 'cuda':
                with torch.cuda.device():
                torch.cuda.empty_cache()

            time_escaped = (time.perf_counter() - time_slice_i) / 3600.0

 funasr/models/language_model/rnn/decoders.py

@@ -873,7 +873,9 @@
                        ctc_state[idx], accum_best_ids
                    )

        with torch.cuda.device(vscores.device):
        device = vscores.device
        if device.type == 'cuda':
            with torch.cuda.device():
            torch.cuda.empty_cache()

        dummy_hyps = [{"yseq": [self.sos, self.eos], "score": np.array([-float("inf")])}]