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tests/models/timesfm/test_modeling_timesfm.py

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+# Copyright 2025 Google LLC and HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import unittest
+
+import numpy as np
+import torch
+from parameterized import parameterized
+
+from transformers import TimesFmConfig, is_torch_available
+from transformers.testing_utils import require_torch, slow, torch_device
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin
+
+
+if is_torch_available():
+    from transformers import TimesFmModelForPrediction
+
+TOLERANCE = 1e-4
+
+
+class TimesFmModelTester:
+    def __init__(
+        self,
+        parent,
+        patch_length: int = 32,
+        context_length: int = 512,
+        horizon_length: int = 128,
+        freq_size: int = 3,
+        num_hidden_layers: int = 1,
+        hidden_size: int = 16,
+        intermediate_size: int = 32,
+        head_dim: int = 8,
+        num_heads: int = 2,
+        tolerance: float = 1e-6,
+        rms_norm_eps: float = 1e-6,
+        quantiles: list[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
+        pad_val: float = 1123581321.0,
+        use_positional_embedding: bool = True,
+        initializer_factor: float = 0.0,
+        is_training: bool = False,
+        batch_size: int = 3,
+    ):
+        self.parent = parent
+        self.patch_length = patch_length
+        self.context_length = context_length
+        self.horizon_length = horizon_length
+        self.quantiles = quantiles
+        self.pad_val = pad_val
+        self.freq_size = freq_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.head_dim = head_dim
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_heads
+        self.tolerance = tolerance
+        self.rms_norm_eps = rms_norm_eps
+        self.use_positional_embedding = use_positional_embedding
+        self.initializer_factor = initializer_factor
+        self.is_training = is_training
+        self.batch_size = batch_size
+
+        # The size of test input
+        self.seq_length = context_length // patch_length
+        self.hidden_size = hidden_size
+
+    def get_config(self):
+        return TimesFmConfig(
+            patch_length=self.patch_length,
+            context_length=self.context_length,
+            horizon_length=self.horizon_length,
+            quantiles=self.quantiles,
+            pad_val=self.pad_val,
+            freq_size=self.freq_size,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size,
+            head_dim=self.head_dim,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            tolerance=self.tolerance,
+            rms_norm_eps=self.rms_norm_eps,
+            use_positional_embedding=self.use_positional_embedding,
+            initializer_factor=self.initializer_factor,
+        )
+
+    def get_pipeline_config(self):
+        return self.get_config()
+
+    def prepare_config_and_inputs(self):
+        forecast_input = [
+            torch.tensor(np.sin(np.linspace(0, 20, 100)), dtype=torch.float32, device=torch_device),
+            torch.tensor(np.cos(np.linspace(0, 20, 100)), dtype=torch.float32, device=torch_device),
+            torch.tensor(np.tan(np.linspace(0, 20, 100)), dtype=torch.float32, device=torch_device),
+        ]
+        frequency_input = torch.tensor([0, 1, 2], dtype=torch.long, device=torch_device)
+
+        return (self.get_config(), torch.stack(forecast_input, dim=0), frequency_input)
+
+    def prepare_config_and_inputs_for_common(self):
+        (config, forecast_input, frequency_input) = self.prepare_config_and_inputs()
+
+        inputs_dict = {
+            "past_values": forecast_input,
+            "freq": frequency_input,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class TimesFmModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (TimesFmModelForPrediction,) if is_torch_available() else ()
+    all_generative_model_classes = ()
+
+    test_resize_embeddings = False
+    is_encoder_decoder = False
+    test_inputs_embeds = False
+    test_torch_exportable = False
+
+    def setUp(self):
+        self.model_tester = TimesFmModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=TimesFmConfig)
+
+    def test_create_and_run_model(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        model = TimesFmModelForPrediction(config)
+        model.to(torch_device)
+        model.eval()
+        results = model(**inputs_dict)
+        assert results.mean_predictions is not None
+
+    @unittest.skip(reason="Compile not yet supported because of masks")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    def test_eager_matches_sdpa_inference(
+        self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+    ):
+        """TimesFM computes its own attention mask internally, so the generic test
+        (which injects external masks) is not compatible. This override directly
+        verifies eager vs SDPA equivalence on model outputs."""
+        if not self.all_model_classes[0]._supports_sdpa:
+            self.skipTest("Model does not support SDPA")
+
+        if dtype == "fp16":
+            dtype = torch.float16
+        elif dtype == "bf16":
+            dtype = torch.bfloat16
+        elif dtype == "fp32":
+            dtype = torch.float32
+
+        tolerance = {torch.float32: 1e-5, torch.bfloat16: 1e-3, torch.float16: 1e-3}[dtype]
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+
+        model_eager = TimesFmModelForPrediction._from_config(config, attn_implementation="eager")
+        model_eager.to(dtype=dtype, device=torch_device)
+        model_eager.eval()
+
+        model_sdpa = TimesFmModelForPrediction._from_config(config, attn_implementation="sdpa")
+        model_sdpa.load_state_dict(model_eager.state_dict())
+        model_sdpa.to(dtype=dtype, device=torch_device)
+        model_sdpa.eval()
+
+        past_values = inputs_dict["past_values"].to(dtype=dtype, device=torch_device)
+        freq = inputs_dict["freq"].to(device=torch_device)
+
+        with torch.no_grad():
+            out_eager = model_eager(past_values=past_values, freq=freq)
+            out_sdpa = model_sdpa(past_values=past_values, freq=freq)
+
+        self.assertTrue(
+            torch.allclose(out_eager.mean_predictions, out_sdpa.mean_predictions, atol=tolerance),
+            f"mean_predictions max diff: {(out_eager.mean_predictions - out_sdpa.mean_predictions).abs().max().item():.2e}",
+        )
+        self.assertTrue(
+            torch.allclose(out_eager.full_predictions, out_sdpa.full_predictions, atol=tolerance),
+            f"full_predictions max diff: {(out_eager.full_predictions - out_sdpa.full_predictions).abs().max().item():.2e}",
+        )
+        hs_eager = out_eager.hidden_states[-1]
+        hs_sdpa = out_sdpa.hidden_states[-1]
+        self.assertTrue(
+            torch.allclose(hs_eager, hs_sdpa, atol=tolerance),
+            f"hidden_states max diff: {(hs_eager - hs_sdpa).abs().max().item():.2e}",
+        )
+
+    @unittest.skip(reason="Model does not have input embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    # the main input name is `inputs`
+    def test_model_main_input_name(self):
+        model_signature = inspect.signature(getattr(TimesFmModelForPrediction, "forward"))
+        # The main input is the name of the argument after `self`
+        observed_main_input_name = list(model_signature.parameters.keys())[1]
+        self.assertEqual(TimesFmModelForPrediction.main_input_name, observed_main_input_name)
+
+
+@require_torch
+@slow
+class TimesFmModelIntegrationTests(unittest.TestCase):
+    def test_inference(self):
+        model = TimesFmModelForPrediction.from_pretrained("google/timesfm-2.0-500m-pytorch").to(torch_device)
+        forecast_input = [
+            np.sin(np.linspace(0, 20, 100)),
+            np.sin(np.linspace(0, 20, 200)),
+            np.sin(np.linspace(0, 20, 400)),
+        ]
+        forecast_input_tensor = [torch.tensor(ts, dtype=torch.float32, device=torch_device) for ts in forecast_input]
+        frequency_input = [0, 1, 2]
+
+        with torch.no_grad():
+            output = model(past_values=forecast_input_tensor, freq=frequency_input)
+
+        mean_predictions = output.mean_predictions
+        self.assertEqual(mean_predictions.shape, torch.Size([3, model.config.horizon_length]))
+        # fmt: off
+        expected_slice = torch.tensor(
+            [ 0.9813,  1.0086,  0.9985,  0.9432,  0.8505,  0.7203,  0.5596,  0.3788,
+              0.1796, -0.0264, -0.2307, -0.4255, -0.5978, -0.7642, -0.8772, -0.9670,
+             -1.0110, -1.0162, -0.9848, -0.9151, -0.8016, -0.6511, -0.4707, -0.2842,
+             -0.0787,  0.1260,  0.3293,  0.5104,  0.6818,  0.8155,  0.9172,  0.9843,
+              1.0101,  1.0025,  0.9529,  0.8588,  0.7384,  0.5885,  0.4022,  0.2099,
+             -0.0035, -0.2104, -0.4146, -0.6033, -0.7661, -0.8818, -0.9725, -1.0191,
+             -1.0190, -0.9874, -0.9137, -0.8069, -0.6683, -0.4939, -0.3086, -0.1106,
+              0.0846,  0.2927,  0.4832,  0.6612,  0.8031,  0.9051,  0.9772,  1.0064
+            ],
+            device=torch_device)
+        # fmt: on
+        self.assertTrue(torch.allclose(mean_predictions[0, :64], expected_slice, atol=TOLERANCE))