Patch moonshine (#35731)

* udpate expected logits for T4 runners * update doc * correct order of the args for better readability * remove generate wrap * convert modular
2025-08-03 03:31:05 +06:00 · 2025-01-20 16:19:29 +01:00 · 2025-01-20 16:19:29 +01:00 · 5f0f4b1b93
commit 5f0f4b1b93
parent a142f16131
4 changed files with 40 additions and 74 deletions
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@ -508,8 +508,6 @@
        title: MobileBERT
      - local: model_doc/modernbert
        title: ModernBert
-      - local: model_doc/moonshine
-        title: moonshine
      - local: model_doc/mpnet
        title: MPNet
      - local: model_doc/mpt
@ -774,6 +772,8 @@
        title: Mimi
      - local: model_doc/mms
        title: MMS
+      - local: model_doc/moonshine
+        title: Moonshine
      - local: model_doc/moshi
        title: Moshi
      - local: model_doc/musicgen
--- a/src/transformers/models/moonshine/modeling_moonshine.py
+++ b/src/transformers/models/moonshine/modeling_moonshine.py
@ -1169,10 +1169,9 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""
            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
            `input_values`, the [`AutoFeatureExtractor`] should be used for padding
            and conversion into a tensor of type `torch.FloatTensor`.
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+        attention_mask (`torch.Tensor`)`, *optional*):
+            Moonshine does not support masking of the `input_values`, this argument is preserved for compatibility,
+            but it is not used.
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
@ -1181,9 +1180,6 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor`)`, *optional*):
-            Moonshine does not support masking of the `input_values`, this argument is preserved for compatibility,
-            but it is not used.
        decoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

@ -1204,11 +1200,10 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
-        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
+        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
+            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
+            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
+            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
@ -1231,6 +1226,11 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
+        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
@ -1552,22 +1552,5 @@ class MoonshineForConditionalGeneration(MoonshinePreTrainedModel, GenerationMixi
            encoder_attentions=outputs.encoder_attentions,
        )

-    def generate(self, *args, **kwargs):
-        # TODO: @eustlb do it rather with a custom logits processor
-        token_limit_factor = 6.5 / 16000.0  # Maximum of 6.5 tokens per second
-        if kwargs.get("max_new_tokens") is None and kwargs.get("max_length") is None:
-            if kwargs.get("attention_mask") is not None:
-                seq_lens = kwargs["attention_mask"].sum(dim=-1)
-            else:
-                seq_lens = kwargs["input_values"].shape[-1]
-            max_length = int(seq_lens.max().item() * token_limit_factor)
-            logger.warning_once(
-                f"Based on the input length, Moonshine will generate up to {max_length} tokens (ratio of 6.5 tokens/second). "
-                "To specify a different length, set either `max_new_tokens` or `max_length`."
-            )
-            kwargs["max_length"] = max_length
-
-        return super().generate(*args, **kwargs)
-

 __all__ = ["MoonshineModel", "MoonshinePreTrainedModel", "MoonshineForConditionalGeneration"]
--- a/src/transformers/models/moonshine/modular_moonshine.py
+++ b/src/transformers/models/moonshine/modular_moonshine.py
@ -816,10 +816,9 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""
            `numpy.ndarray`, *e.g.* via the soundfile library (`pip install soundfile`). To prepare the array into
            `input_values`, the [`AutoFeatureExtractor`] should be used for padding
            and conversion into a tensor of type `torch.FloatTensor`.
-        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
-            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
-            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
-            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
+        attention_mask (`torch.Tensor`)`, *optional*):
+            Moonshine does not support masking of the `input_values`, this argument is preserved for compatibility,
+            but it is not used.
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
@ -828,9 +827,6 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor`)`, *optional*):
-            Moonshine does not support masking of the `input_values`, this argument is preserved for compatibility,
-            but it is not used.
        decoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

@ -851,11 +847,10 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
-        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
+        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
+            Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
+            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
+            hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
@ -878,6 +873,11 @@ MOONSHINE_MODEL_INPUTS_DOCSTRING = r"""
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
+        decoder_position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
@ -1109,23 +1109,6 @@ class MoonshineForConditionalGeneration(MoonshinePreTrainedModel, GenerationMixi
            encoder_attentions=outputs.encoder_attentions,
        )

-    def generate(self, *args, **kwargs):
-        # TODO: @eustlb do it rather with a custom logits processor
-        token_limit_factor = 6.5 / 16000.0  # Maximum of 6.5 tokens per second
-        if kwargs.get("max_new_tokens") is None and kwargs.get("max_length") is None:
-            if kwargs.get("attention_mask") is not None:
-                seq_lens = kwargs["attention_mask"].sum(dim=-1)
-            else:
-                seq_lens = kwargs["input_values"].shape[-1]
-            max_length = int(seq_lens.max().item() * token_limit_factor)
-            logger.warning_once(
-                f"Based on the input length, Moonshine will generate up to {max_length} tokens (ratio of 6.5 tokens/second). "
-                "To specify a different length, set either `max_new_tokens` or `max_length`."
-            )
-            kwargs["max_length"] = max_length
-
-        return super().generate(*args, **kwargs)
-

 __all__ = [
    "MoonshineConfig",
--- a/tests/models/moonshine/test_modeling_moonshine.py
+++ b/tests/models/moonshine/test_modeling_moonshine.py
@ -484,9 +484,9 @@ class MoonshineModelIntegrationTests(unittest.TestCase):

        # fmt: off
        EXPECTED_LOGITS = torch.tensor([
-            -9.1107,  4.5538,  6.3902, -6.8141, -7.2459, -7.9077, -7.2842, -7.6045, -8.0387, -7.8354,
-            -7.3870, -7.2453, -7.7423, -7.3914, -7.3869, -7.6982, -7.6422, -7.0507, -7.3982, -7.2486,
-            -8.0799, -7.3303, -7.3675, -6.8769, -7.6879, -7.2684, -6.9868, -6.7459, -7.6858, -7.3052,
+            -9.1106,  4.5542,  6.3892, -6.8139, -7.2456, -7.9074, -7.2839, -7.6043, -8.0384, -7.8351,
+            -7.3867, -7.2450, -7.7420, -7.3912, -7.3866, -7.6979, -7.6420, -7.0504, -7.3979, -7.2483,
+            -8.0796, -7.3300, -7.3672, -6.8765, -7.6876, -7.2682, -6.9866, -6.7457, -7.6855, -7.3050,
        ])
        # fmt: on
        self.assertTrue(torch.allclose(outputs.logits[0][0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@ -502,9 +502,9 @@ class MoonshineModelIntegrationTests(unittest.TestCase):

        # fmt: off
        EXPECTED_LOGITS = torch.tensor([
-            -6.7340,  1.9483,  5.2449, -8.0277, -7.9167, -7.8956, -7.9649, -7.9348, -8.1312, -8.0616,
-            -8.1070, -7.7696, -7.8809, -7.9451, -8.1013, -7.8177, -7.8598, -7.8257, -7.8729, -7.9657,
-            -7.9310, -8.1024, -7.8698, -7.8231, -8.0752, -7.9764, -7.8127, -8.0536, -7.9492, -7.9289,
+            -6.7336,  1.9482,  5.2448, -8.0277, -7.9167, -7.8956, -7.9649, -7.9348, -8.1312, -8.0616,
+            -8.1070, -7.7696, -7.8809, -7.9450, -8.1013, -7.8177, -7.8598, -7.8257, -7.8729, -7.9657,
+            -7.9310, -8.1024, -7.8699, -7.8231, -8.0752, -7.9764, -7.8127, -8.0536, -7.9492, -7.9290,
        ])
        # fmt: on
        self.assertTrue(torch.allclose(outputs.logits[0][0, :30].cpu(), EXPECTED_LOGITS, atol=1e-4))
@ -519,10 +519,10 @@ class MoonshineModelIntegrationTests(unittest.TestCase):
        outputs = model.generate(**inputs, max_new_tokens=1, return_dict_in_generate=True, output_logits=True)
        # fmt: off
        EXPECTED_LOGITS = torch.tensor([
-            [-8.0098,  5.0239,  4.5986, -6.8125, -7.1676, -7.8782, -7.2152, -7.5188, -7.9078, -7.7394],
-            [-4.4394, -1.4429,  6.6715, -6.8927, -7.3748, -7.0967, -6.5255, -7.0255, -7.2583, -7.0007],
-            [-10.0088, 3.2862,  0.7342, -6.5558, -6.8514, -6.5309, -6.4173, -6.9485, -6.6215, -6.6230],
-            [-10.8083, 4.0034, -0.0635, -5.0501, -5.3903, -5.4587, -5.2416, -5.4742, -5.2662, -5.3154]
+            [-8.0109,  5.0241,  4.5979, -6.8125, -7.1675, -7.8783, -7.2152, -7.5188, -7.9077, -7.7394],
+            [-4.4399, -1.4422,  6.6710, -6.8929, -7.3751, -7.0969, -6.5257, -7.0257, -7.2585, -7.0008],
+            [-10.0086, 3.2859,  0.7345, -6.5557, -6.8514, -6.5308, -6.4172, -6.9484, -6.6214, -6.6229],
+            [-10.8078, 4.0030, -0.0633, -5.0505, -5.3906, -5.4590, -5.2420, -5.4746, -5.2665, -5.3158]
        ])
        # fmt: on
        self.assertTrue(torch.allclose(outputs.logits[0][:, :10].cpu(), EXPECTED_LOGITS, atol=1e-4))
@ -538,10 +538,10 @@ class MoonshineModelIntegrationTests(unittest.TestCase):

        # fmt: off
        EXPECTED_LOGITS = torch.tensor([
-            [-7.7288,  1.4636,  5.2273, -7.7310, -7.6249, -7.6009, -7.6786, -7.6438, -7.8450, -7.7546],
-            [-6.2161, -0.5891,  7.9489, -7.0693, -6.9996, -6.9980, -7.0952, -7.0830, -7.1685, -7.0136],
-            [-7.3186,  3.1192,  3.8938, -5.7208, -5.8429, -5.7610, -5.9997, -5.8213, -5.8616, -5.8720],
-            [-9.5488,  1.0147,  4.1174, -5.9972, -6.0616, -6.0331, -6.2105, -6.0320, -6.0791, -6.0875]
+            [-7.7272,  1.4630,  5.2294, -7.7313, -7.6252, -7.6011, -7.6788, -7.6441, -7.8452, -7.7549],
+            [-6.2173, -0.5891,  7.9493, -7.0694, -6.9997, -6.9982, -7.0953, -7.0831, -7.1686, -7.0137],
+            [-7.3184,  3.1192,  3.8937, -5.7206, -5.8428, -5.7609, -5.9996, -5.8212, -5.8615, -5.8719],
+            [-9.5475,  1.0146,  4.1179, -5.9971, -6.0614, -6.0329, -6.2103, -6.0318, -6.0789, -6.0873]
        ])

        # fmt: on