[chronos-2] add support for SDPA (#331)

This pull request introduces configurable attention backends to the
Chronos-2 model, allowing users to select between eager, SDPA, and
FlashAttention-2 implementations.

---------

Co-authored-by: Oleksandr Shchur <oleks.shchur@gmail.com>
Co-authored-by: Abdul Fatir <Abdulfatirs@gmail.com>

scripts/training/train.py

@@ -663,7 +663,6 @@ def main(
         lr_scheduler_type=lr_scheduler_type,
         warmup_ratio=warmup_ratio,
         optim=optim,
-        logging_dir=str(output_dir / "logs"),
         logging_strategy="steps",
         logging_steps=log_steps,
         save_strategy="steps",

src/chronos/chronos2/config.py

@@ -4,7 +4,7 @@
 # Authors: Abdul Fatir Ansari <ansarnd@amazon.com>
 
 from dataclasses import dataclass
-from typing import List
+from typing import List, Literal
 
 from transformers.configuration_utils import PretrainedConfig
 
@@ -39,6 +39,8 @@ class Chronos2CoreConfig(PretrainedConfig):
         Token ID for padding/missing value token, by default 0
     rope_theta
         The base theta for rotary position embedding (RoPE), by default 10000.0
+    attn_implementation
+        The attention implementation to use. Options: "eager" or "sdpa", by default None (uses "sdpa")
     """
 
     model_type = "t5"
@@ -63,6 +65,7 @@ class Chronos2CoreConfig(PretrainedConfig):
         vocab_size: int = 2,
         pad_token_id: int = 0,
         rope_theta: float = 10000.0,
+        attn_implementation: Literal["eager", "sdpa"] | None = None,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -83,11 +86,17 @@ class Chronos2CoreConfig(PretrainedConfig):
 
         assert not self.is_gated_act, "gated activation is not supported"
 
+        # Attention implementation - default to "sdpa" if not specified
+        attn_implementation = attn_implementation or "sdpa"
+        assert attn_implementation in ["eager", "sdpa"], f"attn_implementation {attn_implementation} not supported"
+
         # unused
         kwargs.pop("is_encoder_decoder", None)
         kwargs.pop("eos_token_id", None)
 
-        super().__init__(pad_token_id=pad_token_id, is_encoder_decoder=False, **kwargs)
+        super().__init__(
+            pad_token_id=pad_token_id, is_encoder_decoder=False, attn_implementation=attn_implementation, **kwargs
+        )
 
 
 @dataclass

src/chronos/chronos2/layers.py

@@ -155,6 +155,7 @@ class MHA(nn.Module):
         self.n_heads: int = config.num_heads
         self.dropout: float = config.dropout_rate
         self.inner_dim: int = self.n_heads * self.kv_proj_dim
+        self.config = config
 
         self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
         self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
@@ -165,6 +166,64 @@ class MHA(nn.Module):
         if use_rope:
             self.rope_embed = RoPE(dim=self.kv_proj_dim, base=config.rope_theta)
 
+    def _eager_attention(
+        self,
+        query_states: torch.Tensor,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Eager attention implementation using manual matmul.
+
+        Args:
+            query_states: [batch, n_heads, seq_len, kv_proj_dim]
+            key_states: [batch, n_heads, seq_len, kv_proj_dim]
+            value_states: [batch, n_heads, seq_len, kv_proj_dim]
+            mask: [batch, n_heads, q_len, kv_len]
+
+        Returns:
+            attn_output: [batch, n_heads, seq_len, kv_proj_dim]
+            attn_weights: [batch, n_heads, q_len, kv_len]
+        """
+        # Compute attention weights (no scaling - this is the original Chronos-2 implementation)
+        scores = torch.matmul(query_states, key_states.transpose(3, 2))  # "bnqd,bnkd->bnqk"
+        scores += mask
+        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        return attn_output, attn_weights
+
+    def _sdpa_attention(
+        self,
+        query_states: torch.Tensor,
+        key_states: torch.Tensor,
+        value_states: torch.Tensor,
+        mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, None]:
+        """SDPA attention implementation using torch.nn.functional.scaled_dot_product_attention.
+
+        Args:
+            query_states: [batch, n_heads, seq_len, kv_proj_dim]
+            key_states: [batch, n_heads, seq_len, kv_proj_dim]
+            value_states: [batch, n_heads, seq_len, kv_proj_dim]
+            mask: [batch, n_heads, q_len, kv_len] - additive mask (0 for valid, -inf for invalid)
+
+        Returns:
+            attn_output: [batch, n_heads, seq_len, kv_proj_dim]
+            attn_weights: None (SDPA doesn't return weights)
+        """
+        attn_output = nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=mask,
+            dropout_p=self.dropout if self.training else 0.0,
+            scale=1.0,  # Match eager implementation (no scaling)
+        )
+
+        return attn_output, None
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -190,6 +249,11 @@ class MHA(nn.Module):
         if self.use_rope:
             assert position_ids is not None, "position_ids must be provided when self.use_rope=True"
 
+        # Force eager attention if output_attentions is True (only eager returns weights)
+        attn_implementation = self.config._attn_implementation
+        if output_attentions:
+            attn_implementation = "eager"
+
         seq_length = hidden_states.shape[1]
 
         def shape(states: torch.Tensor) -> torch.Tensor:
@@ -215,12 +279,10 @@ class MHA(nn.Module):
                 cos, sin = self.rope_embed(value_states, position_ids)
                 query_states, key_states = RoPE.apply_rotary_pos_emb(query_states, key_states, cos, sin)
 
-        # Compute attention weights
-        scores = torch.matmul(query_states, key_states.transpose(3, 2))  # "bnqd,bnkd->bnqk"
-        scores += mask
-        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
-        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
+        if attn_implementation == "sdpa":
+            attn_output, attn_weights = self._sdpa_attention(query_states, key_states, value_states, mask)
+        else:  # eager
+            attn_output, attn_weights = self._eager_attention(query_states, key_states, value_states, mask)
 
         # Project attention output
         attn_output = unshape(attn_output)

src/chronos/chronos2/model.py

@@ -199,6 +199,7 @@ class Chronos2Model(PreTrainedModel):
     config_class = Chronos2CoreConfig  # type: ignore[assignment]
     _supports_long_horizon: bool = True
     _supports_future_covariates: bool = True
+    _supports_sdpa: bool = True
 
     def __init__(self, config: Chronos2CoreConfig):
         assert hasattr(config, "chronos_config"), "Not a valid Chronos config"

src/chronos/chronos2/pipeline.py

@@ -211,7 +211,6 @@ class Chronos2Pipeline(BaseChronosPipeline):
             lr_scheduler_type="linear",
             warmup_ratio=0.0,
             optim="adamw_torch_fused",
-            logging_dir=str(output_dir / "logs"),
             logging_strategy="steps",
             logging_steps=100,
             disable_tqdm=False,

test/test_chronos2.py

@@ -14,6 +14,9 @@ import torch
 
 from chronos import BaseChronosPipeline, Chronos2Pipeline
 from chronos.chronos2.dataset import convert_df_input_to_list_of_dicts_input
+from chronos.chronos2.config import Chronos2CoreConfig
+from chronos.chronos2.layers import MHA
+
 from test.util import validate_tensor
 
 DUMMY_MODEL_PATH = Path(__file__).parent / "dummy-chronos2-model"
@@ -317,13 +320,11 @@ def test_when_input_is_invalid_then_predict_raises_value_error(pipeline, inputs,
         _ = pipeline.predict(inputs, prediction_length=10)
 
 
-@pytest.mark.parametrize("torch_dtype", [torch.float32, torch.bfloat16])
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bfloat16])
 @pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
-def test_pipeline_predict_can_handle_different_model_and_input_dtypes(
-    torch_dtype: torch.dtype, input_dtype: torch.dtype
-):
+def test_pipeline_predict_can_handle_different_model_and_input_dtypes(dtype: torch.dtype, input_dtype: torch.dtype):
     pipeline = BaseChronosPipeline.from_pretrained(
-        Path(__file__).parent / "dummy-chronos2-model", device_map="cpu", torch_dtype=torch_dtype
+        Path(__file__).parent / "dummy-chronos2-model", device_map="cpu", dtype=dtype
     )
     context = 10 * torch.rand(size=(4, 3, 16)) + 10
     context = context.to(dtype=input_dtype)
@@ -936,3 +937,129 @@ def test_two_step_finetuning_with_df_input_works(pipeline, context_setup, future
 
     # Check predictions from the fine-tuned model are different from the original predictions
     assert not np.allclose(orig_result_before["predictions"].to_numpy(), result["predictions"].to_numpy())
+
+
+@pytest.mark.parametrize("attn_implementation", ["eager", "sdpa"])
+def test_pipeline_works_with_different_attention_implementations(attn_implementation):
+    """Test that the pipeline works with different attention implementations."""
+    # Load the dummy model
+    model_path = Path(__file__).parent / "dummy-chronos2-model"
+
+    # Load with specified attention implementation
+    pipeline = BaseChronosPipeline.from_pretrained(
+        model_path, device_map="cpu", attn_implementation=attn_implementation
+    )
+
+    # Verify the config has the correct attention implementation
+    assert pipeline.model.config._attn_implementation == attn_implementation
+
+    # Test prediction with simple input
+    inputs = torch.rand(2, 1, 16)
+    prediction_length = 7
+
+    outputs = pipeline.predict(inputs, prediction_length=prediction_length)
+
+    # Check outputs are valid
+    assert isinstance(outputs, list) and len(outputs) == 2
+    for out in outputs:
+        validate_tensor(out, (1, DEFAULT_MODEL_NUM_QUANTILES, 7), dtype=torch.float32)
+
+
+@pytest.mark.parametrize("attn_implementation", ["eager", "sdpa"])
+@pytest.mark.parametrize("output_attentions", [False, True])
+def test_attention_implementations_with_output_attentions(attn_implementation, output_attentions):
+    """Test that attention implementations handle output_attentions correctly."""
+    # Create config with specified attention implementation
+    config = Chronos2CoreConfig(
+        d_model=128,
+        d_kv=32,
+        num_heads=4,
+        dropout_rate=0.1,
+        attn_implementation=attn_implementation,
+    )
+
+    # Create MHA layer
+    mha = MHA(config, use_rope=True)
+    mha.eval()
+
+    # Create dummy inputs
+    batch_size = 2
+    seq_len = 10
+    hidden_states = torch.randn(batch_size, seq_len, config.d_model)
+    position_ids = torch.arange(seq_len).unsqueeze(0).expand(batch_size, -1)
+    mask = torch.zeros(batch_size, config.num_heads, seq_len, seq_len)
+
+    # Test forward pass
+    output = mha(
+        hidden_states=hidden_states,
+        mask=mask,
+        position_ids=position_ids,
+        output_attentions=output_attentions,
+    )
+
+    # Check output shape
+    assert output.hidden_states.shape == (batch_size, seq_len, config.d_model)
+
+    # Check attention weights - should only be returned when output_attentions=True
+    if output_attentions:
+        assert output.attn_weights is not None
+        assert output.attn_weights.shape == (batch_size, config.num_heads, seq_len, seq_len)
+    else:
+        # SDPA doesn't return weights
+        if attn_implementation == "sdpa":
+            assert output.attn_weights is None
+
+
+def test_eager_and_sdpa_produce_identical_outputs(pipeline):
+    """Test that eager and SDPA implementations produce identical outputs on full pipeline."""
+    # Reload pipeline with SDPA
+    model_path = Path(__file__).parent / "dummy-chronos2-model"
+    pipeline_sdpa = BaseChronosPipeline.from_pretrained(
+        model_path, device_map="cpu", attn_implementation="sdpa", dtype=torch.float32
+    )
+
+    # Note: the original pipeline fixture uses default attn_implementation which should be sdpa
+    # Force eager for comparison
+    pipeline_eager = BaseChronosPipeline.from_pretrained(
+        model_path, device_map="cpu", attn_implementation="eager", dtype=torch.float32
+    )
+
+    # Test 1: Simple univariate input
+    inputs_simple = torch.rand(2, 1, 16)
+    prediction_length = 7
+
+    with torch.no_grad():
+        outputs_eager = pipeline_eager.predict(inputs_simple, prediction_length=prediction_length)
+        outputs_sdpa = pipeline_sdpa.predict(inputs_simple, prediction_length=prediction_length)
+
+    # Verify outputs match exactly
+    assert len(outputs_eager) == len(outputs_sdpa)
+    for out_eager, out_sdpa in zip(outputs_eager, outputs_sdpa):
+        # Should match exactly or very close (numerical precision)
+        assert torch.allclose(out_eager, out_sdpa, atol=1e-5, rtol=1e-4)
+
+    # Test 2: Multivariate inputs with covariates to test group attention
+    inputs_grouped = [
+        {
+            "target": np.random.randn(2, 36),
+            "past_covariates": {
+                "temperature": np.random.randn(36),
+                "weather_type": np.random.choice(["sunny", "cloudy", "rainy"], size=36),
+            },
+            "future_covariates": {
+                "temperature": np.random.randn(prediction_length),
+                "weather_type": np.random.choice(["sunny", "cloudy", "rainy"], size=prediction_length),
+            },
+        }
+        for _ in range(5)
+    ]
+
+    with torch.no_grad():
+        outputs_eager_grouped = pipeline_eager.predict(inputs_grouped, prediction_length=prediction_length)
+        outputs_sdpa_grouped = pipeline_sdpa.predict(inputs_grouped, prediction_length=prediction_length)
+
+    # Verify outputs match for grouped inputs
+    assert len(outputs_eager_grouped) == len(outputs_sdpa_grouped)
+    for out_eager, out_sdpa in zip(outputs_eager_grouped, outputs_sdpa_grouped):
+        # Should match exactly or very close (numerical precision)
+        assert torch.allclose(out_eager, out_sdpa, atol=1e-5, rtol=1e-4)