Repository: huggingface/transformers
Pull request: [timesfm2_5] fix timesfm2.5 loss

# What does this PR do?
fixed the  bfloat16 dtype mismatch and  Loss computation shape mismatch. Also added tests for these.

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Fixes # (issue)


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Changed files:
filename: src/transformers/models/timesfm2_5/modeling_timesfm2_5.py
patch: @@ -106,6 +106,8 @@ def __init__(self, config, input_dims: int, hidden_dims: int, output_dims: int,
         use_bias = use_bias if use_bias is not None else config.use_bias
 
     def forward(self, x):
+        # Align activations to block parameter dtype for mixed precision stability
+        x = x.to(self.input_layer.weight.dtype)
         hidden = self.input_layer(x)
         hidden = self.activation(hidden)
         output = self.output_layer(hidden)
@@ -841,11 +843,14 @@ def _flip_quantiles(x: torch.Tensor) -> torch.Tensor:
 
         loss = None
         if future_values is not None:
-            mse_loss = F.mse_loss(mean_predictions, future_values)
-            quantile_indices = [i for i in range(full_predictions.shape[-1]) if i != decode_index]
+            target_len = future_values.shape[1]
+            valid_mean_predictions = mean_predictions[:, :target_len]
+            valid_full_predictions = full_predictions[:, :target_len]
+            mse_loss = F.mse_loss(valid_mean_predictions, future_values)
+            quantile_indices = [i for i in range(valid_full_predictions.shape[-1]) if i != decode_index]
             if quantile_indices:
-                index_tensor = torch.tensor(quantile_indices, device=full_predictions.device, dtype=torch.long)
-                quantile_tensor = torch.index_select(full_predictions, dim=-1, index=index_tensor)
+                index_tensor = torch.tensor(quantile_indices, device=valid_full_predictions.device, dtype=torch.long)
+                quantile_tensor = torch.index_select(valid_full_predictions, dim=-1, index=index_tensor)
                 quantile_loss = self._quantile_loss(quantile_tensor, future_values)
                 loss = mse_loss + quantile_loss
             else:

filename: src/transformers/models/timesfm2_5/modular_timesfm2_5.py
patch: @@ -237,6 +237,11 @@ def __init__(self, config, input_dims: int, hidden_dims: int, output_dims: int,
         self.residual_layer = nn.Linear(input_dims, output_dims, bias=use_bias)
         self.activation = ACT2FN[config.activation]
 
+    def forward(self, x):
+        # Align activations to block parameter dtype for mixed precision stability
+        x = x.to(self.input_layer.weight.dtype)
+        return super().forward(x)
+
 
 class TimesFm2_5RMSNorm(LlamaRMSNorm):
     pass
@@ -705,11 +710,14 @@ def _flip_quantiles(x: torch.Tensor) -> torch.Tensor:
 
         loss = None
         if future_values is not None:
-            mse_loss = F.mse_loss(mean_predictions, future_values)
-            quantile_indices = [i for i in range(full_predictions.shape[-1]) if i != decode_index]
+            target_len = future_values.shape[1]
+            valid_mean_predictions = mean_predictions[:, :target_len]
+            valid_full_predictions = full_predictions[:, :target_len]
+            mse_loss = F.mse_loss(valid_mean_predictions, future_values)
+            quantile_indices = [i for i in range(valid_full_predictions.shape[-1]) if i != decode_index]
             if quantile_indices:
-                index_tensor = torch.tensor(quantile_indices, device=full_predictions.device, dtype=torch.long)
-                quantile_tensor = torch.index_select(full_predictions, dim=-1, index=index_tensor)
+                index_tensor = torch.tensor(quantile_indices, device=valid_full_predictions.device, dtype=torch.long)
+                quantile_tensor = torch.index_select(valid_full_predictions, dim=-1, index=index_tensor)
                 quantile_loss = self._quantile_loss(quantile_tensor, future_values)
                 loss = mse_loss + quantile_loss
             else:

filename: tests/models/timesfm2_5/test_modeling_timesfm2_5.py
patch: @@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import random
 import unittest
 
 import numpy as np
@@ -22,7 +23,7 @@
 from transformers.testing_utils import require_flash_attn, require_torch, require_torch_accelerator, slow, torch_device
 
 from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin
+from ...test_modeling_common import TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin, floats_tensor
 
 
 if is_torch_available():
@@ -44,7 +45,7 @@ def __init__(
         rms_norm_eps: float = 1e-6,
         quantiles: list[float] = [0.1, 0.5, 0.9],
         output_quantile_len: int = 16,
-        is_training: bool = False,
+        is_training: bool = True,
         batch_size: int = 2,
     ):
         self.parent = parent
@@ -105,6 +106,7 @@ class TimesFm2_5ModelTest(ModelTesterMixin, unittest.TestCase):
     test_resize_embeddings = False
     is_encoder_decoder = False
     test_inputs_embeds = False
+    test_all_params_have_gradient = False
 
     def setUp(self):
         self.model_tester = TimesFm2_5ModelTester(self)
@@ -272,6 +274,14 @@ def test_retain_grad_hidden_states_attentions(self):
         if self.has_attentions and outputs.attentions is not None:
             self.assertIsNotNone(attentions.grad)
 
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+        if return_labels:
+            batch_size = inputs_dict["past_values"].shape[0]
+            rng = random.Random(42)
+            inputs_dict["future_values"] = floats_tensor([batch_size, self.model_tester.horizon_length], rng=rng)
+        return inputs_dict
+
 
 @require_torch
 @slow