symbolic_trace: add past_key_values, llama, sdpa support (#28447)

* torch.fx: add pkv, llama, sdpa support

* Update src/transformers/models/opt/modeling_opt.py

* remove spaces

* trigger ci

* use explicit variable names

src/transformers/modeling_attn_mask_utils.py

@@ -132,6 +132,7 @@ class AttentionMaskConverter:
         expanded_attn_mask = self._expand_mask(attention_mask_2d, dtype, tgt_len=input_shape[-1]).to(
             attention_mask_2d.device
         )
+
         if causal_4d_mask is not None:
             expanded_attn_mask = causal_4d_mask.masked_fill(expanded_attn_mask.bool(), torch.finfo(dtype).min)
 
@@ -346,10 +347,10 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
     key_value_length = input_shape[-1] + past_key_values_length
     batch_size, query_length = input_shape
 
-    # torch.jit.trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
+    # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
     # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
     # TODO: Fix this as well when using torchdynamo with fullgraph=True.
-    is_tracing = torch.jit.is_tracing()
+    is_tracing = torch.jit.is_tracing() or isinstance(inputs_embeds, torch.fx.Proxy)
 
     if attention_mask is not None:
         # 4d mask is passed through
@@ -367,10 +368,8 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
                 )
                 return attention_mask
 
-        elif torch.all(attention_mask == 1):
-            if is_tracing:
-                pass
-            elif query_length == 1:
+        elif not is_tracing and torch.all(attention_mask == 1):
+            if query_length == 1:
                 # For query_length == 1, causal attention and bi-directional attention are the same.
                 attention_mask = None
             elif key_value_length == query_length:
@@ -405,7 +404,11 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
 
         # From PyTorch 2.1 onwards, F.scaled_dot_product_attention with the memory-efficient attention backend
         # produces nans if sequences are completely unattended in the attention mask. Details: https://github.com/pytorch/pytorch/issues/110213
-        if query_length > 1:
+        #
+        # This fix is not applied in case we are tracing with torch.jit.trace or symbolic_trace, as _unmask_unattended has a data-dependent
+        # controlflow that can not be captured properly.
+        # TODO: _unmask_unattended does not work either with torch.compile when using fullgraph=True. We should find a way to detect this case.
+        if query_length > 1 and not is_tracing:
             expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
                 expanded_4d_mask, attention_mask, unmasked_value=0.0
             )

src/transformers/utils/fx.py

@@ -131,6 +131,7 @@ _REGULAR_SUPPORTED_MODEL_NAMES_AND_TASKS = [
     "gptj",
     "hubert",
     "layoutlm",
+    "llama",
     "lxmert",
     "m2m_100",
     "marian",
@@ -156,6 +157,8 @@ _REGULAR_SUPPORTED_MODEL_NAMES_AND_TASKS = [
     #    "xlnet",
 ]
 
+_FX_SUPPORTED_MODELS_WITH_KV_CACHE = ["llama", "opt"]
+
 _REGULAR_SUPPORTED_MODELS = []
 for item in _REGULAR_SUPPORTED_MODEL_NAMES_AND_TASKS:
     if isinstance(item, dict):
@@ -514,6 +517,14 @@ def torch_nn_functional_one_hot(tensor, num_classes=-1):
     return torch.empty(shape, device="meta")
 
 
+def torch_nn_functional_scaled_dot_product_attention(
+    query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False, scale=None
+):
+    target_length = query.shape[-2]
+    head_dim = value.shape[-1]
+    return torch.empty((*query.shape[:-2], target_length, head_dim), device="meta")
+
+
 def torch_nn_mseloss(self, input, target):
     if self.reduction == "none":
         shape = target.shape
@@ -597,6 +608,7 @@ _MANUAL_META_OVERRIDES: Dict[Callable, Callable] = {
     torch.Tensor.unsqueeze: torch_tensor_unsqueeze,
     torch.unique_consecutive: torch_unique_consecutive,
     torch.nn.functional.one_hot: torch_nn_functional_one_hot,
+    torch.nn.functional.scaled_dot_product_attention: torch_nn_functional_scaled_dot_product_attention,
     torch.nn.MSELoss: torch_nn_mseloss,
     torch.nn.CrossEntropyLoss: torch_nn_crossentropyloss,
     torch.nn.BCEWithLogitsLoss: torch_nn_bcewithlogitsloss,
@@ -868,6 +880,23 @@ class HFTracer(Tracer):
             inputs_dict[input_name] = torch.zeros(batch_size, seq_length, dtype=torch.float, device=device)
         elif "mask" in input_name or "ids" in input_name:
             inputs_dict[input_name] = torch.zeros(shape, dtype=torch.long, device=device)
+        elif "past_key_values" in input_name:
+            if model.config.model_type not in _FX_SUPPORTED_MODELS_WITH_KV_CACHE:
+                raise NotImplementedError(
+                    f"Symbolic trace with past_key_values input is not supported yet for the model {model.config.model_type}. Please open an issue or a PR in Transformers repository if you would like to see the support added."
+                )
+            num_heads = model.config.num_attention_heads
+            head_dim = model.config.hidden_size // model.config.num_attention_heads
+
+            cache_shape = (shape[0], num_heads, 0, head_dim)
+            pkv = tuple(
+                (
+                    torch.rand(cache_shape, dtype=torch.float, device=device),
+                    torch.rand(cache_shape, dtype=torch.float, device=device),
+                )
+                for i in range(model.config.num_hidden_layers)
+            )
+            inputs_dict[input_name] = pkv
         else:
             shape_with_hidden_size = shape + [model.config.hidden_size]
             inputs_dict[input_name] = torch.zeros(shape_with_hidden_size, dtype=torch.float, device=device)

tests/models/llama/test_modeling_llama.py

@@ -292,6 +292,7 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
     )
     test_headmasking = False
     test_pruning = False
+    fx_compatible = True
 
     def setUp(self):
         self.model_tester = LlamaModelTester(self)

tests/test_modeling_common.py

@@ -118,7 +118,7 @@ if is_flax_available():
     )
 
 if is_torch_fx_available():
-    from transformers.utils.fx import symbolic_trace
+    from transformers.utils.fx import _FX_SUPPORTED_MODELS_WITH_KV_CACHE, symbolic_trace
 
 
 def _config_zero_init(config):
@@ -1004,7 +1004,9 @@ class ModelTesterMixin:
 
     def _create_and_check_torch_fx_tracing(self, config, inputs_dict, output_loss=False):
         if not is_torch_fx_available() or not self.fx_compatible:
-            return
+            self.skipTest(
+                f"Either torch.fx is not available, or the model type {config.model_type} is not compatible with torch.fx"
+            )
 
         configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
         configs_no_init.return_dict = False
@@ -1060,6 +1062,26 @@ class ModelTesterMixin:
                     if end_positions is not None:
                         input_names.append("end_positions")
 
+                    if model.config.model_type in _FX_SUPPORTED_MODELS_WITH_KV_CACHE:
+                        input_names.append("past_key_values")
+
+                        # Generally model_tester.prepare_config_and_inputs_for_common seem not to generate past key values inputs.
+                        if "past_key_values" not in inputs:
+                            batch_size = inputs[next(iter(inputs))].shape[0]
+                            num_heads = model.config.num_attention_heads
+                            head_dim = model.config.hidden_size // model.config.num_attention_heads
+
+                            cache_shape = (batch_size, num_heads, 0, head_dim)
+                            pkv = tuple(
+                                (
+                                    torch.rand(cache_shape, dtype=torch.float, device=torch_device),
+                                    torch.rand(cache_shape, dtype=torch.float, device=torch_device),
+                                )
+                                for i in range(model.config.num_hidden_layers)
+                            )
+
+                            inputs["past_key_values"] = pkv
+
                     filtered_inputs = {k: v for (k, v) in inputs.items() if k in input_names}
                     input_names = list(filtered_inputs.keys())
 
@@ -1069,6 +1091,8 @@ class ModelTesterMixin:
                         model.config.problem_type = "single_label_classification"
 
                     traced_model = symbolic_trace(model, input_names)
+
+                    with torch.no_grad():
                         traced_output = traced_model(**filtered_inputs)
                         model_output = model(**filtered_inputs)