Allows to convert a model with other task inputs

CHANGELOGS.rst

@@ -4,6 +4,7 @@ Change Logs
 0.7.15
 ++++++
 
+* :pr:`264`: allows to validate a model with inputs defined from another task
 * :pr:`261`: updates to support ``transformers>=5.0``
 
 0.7.14

_doc/conf.py

@@ -229,7 +229,7 @@ def linkcode_resolve(domain, info):
     "Linux": "https://www.linux.org/",
     "ml_dtypes": "https://github.com/jax-ml/ml_dtypes",
     "ModelBuilder": "https://onnxruntime.ai/docs/genai/howto/build-model.html",
-    "monai": "https://monai.io/",
+    "monai": "https://github.com/Project-MONAI/MONAI",
     "numpy": "https://numpy.org/",
     "onnx": "https://onnx.ai/onnx/",
     "onnx-ir": "https://github.com/onnx/ir-py",

_unittests/ut_torch_models/test_validate_whole_models1.py

@@ -98,7 +98,7 @@ def test_f_validate_model_onnx_dynamo_ir(self):
     @requires_torch("2.7")
     @requires_onnxscript("0.7")
     @hide_stdout()
-    @ignore_warnings(FutureWarning)
+    @ignore_warnings((FutureWarning, RuntimeWarning))
     def test_g_validate_model_onnx_dynamo_os_ort(self):
         mid = "arnir0/Tiny-LLM"
         summary, data = validate_model(

onnx_diagnostic/helpers/config_helper.py

@@ -95,7 +95,8 @@ def config_class_from_architecture(arch: str, exc: bool = False) -> Optional[typ
     mod_name = cls.__module__
     mod = importlib.import_module(mod_name)
     source = inspect.getsource(mod)
-    reg = re.compile("config: ([A-Za-z0-9]+)")
+    # [^O] avoids capturing Optional[Something]
+    reg = re.compile("config: ([^O][A-Za-z0-9]+)")
     fall = reg.findall(source)
     if len(fall) == 0:
         assert not exc, (

onnx_diagnostic/tasks/text_generation.py

@@ -19,6 +19,9 @@
 def reduce_model_config(config: Any) -> Dict[str, Any]:
     """Reduces a model size."""
     # FalconMambaConfig: use_mambapy
+    if hasattr(config, "text_config"):
+        # The model is probably of mixture of models used only for text.
+        config = config.text_config
     check_hasattr(
         config,
         ("head_dim", ("hidden_size", "num_attention_heads"), "use_mambapy"),
@@ -308,6 +311,9 @@ def random_input_kwargs(config: Any) -> Tuple[Dict[str, Any], Callable]:
 
     If the configuration is None, the function selects typical dimensions.
     """
+    if hasattr(config, "text_config"):
+        # The model is probably of mixture of models used only for text.
+        config = config.text_config
     if config is not None:
         check_hasattr(
             config,

onnx_diagnostic/torch_export_patches/patches/patch_transformers.py

@@ -1019,6 +1019,26 @@ def patched__compute_dynamic_ntk_parameters(
     return inv_freq, attention_factor
 
 
+def _get_rope_init_fn(self, layer_type=None) -> Callable:
+    if hasattr(self, "rope_init_fn"):
+        # transformers<=5.0
+        rope_init_fn = (
+            patched__compute_dynamic_ntk_parameters
+            if self.rope_init_fn
+            is transformers.modeling_rope_utils._compute_dynamic_ntk_parameters
+            else self.rope_init_fn
+        )
+        return rope_init_fn
+
+    rope_type = self.rope_type if layer_type is None else self.rope_type[layer_type]
+    rope_init_fn = self.compute_default_rope_parameters
+    if rope_type != "default":
+        rope_init_fn = transformers.modeling_rope_utils.ROPE_INIT_FUNCTIONS[self.rope_type]
+    if rope_init_fn is transformers.modeling_rope_utils._compute_dynamic_ntk_parameters:
+        return patched__compute_dynamic_ntk_parameters
+    return rope_init_fn
+
+
 def patched_dynamic_rope_update(rope_forward):
     """manual patch: ``[patch:transformers.modeling_rope_utils.dynamic_rope_update]``
 
@@ -1082,22 +1102,27 @@ def wrapper(self, x, position_ids):
 
     """
 
-    def longrope_frequency_update(self, position_ids, device):
+    def longrope_frequency_update(self, position_ids, device, layer_type=None):
         # It is no use to patch the function after the model is created
         # as rope_init_fn is an attribute set to one function when the model
         # is created and when no patch is applied yet.
         # So we select the patched version here.
-        rope_init_fn = (
-            patched__compute_dynamic_ntk_parameters
-            if self.rope_init_fn
-            is transformers.modeling_rope_utils._compute_dynamic_ntk_parameters
-            else self.rope_init_fn
-        )
+        rope_init_fn = _get_rope_init_fn(self, layer_type=layer_type)
         seq_len = torch.max(position_ids) + 1
         if hasattr(self.config, "original_max_position_embeddings"):
             original_max_position_embeddings = self.config.original_max_position_embeddings
         else:
             original_max_position_embeddings = self.config.max_position_embeddings
+
+        if layer_type is None:
+            # rope_type = self.rope_type
+            original_inv_freq = self.original_inv_freq
+            prefix = ""
+        else:
+            # rope_type = self.rope_type[layer_type]
+            original_inv_freq = getattr(self, f"{layer_type}_original_inv_freq")
+            prefix = f"{layer_type}_"
+
         # At export time, seq_len is unknown.
         long_inv_freq, _ = rope_init_fn(
             self.config, device, seq_len=original_max_position_embeddings + 1
@@ -1112,13 +1137,13 @@ def longrope_frequency_update(self, position_ids, device):
             (lambda x, y: y.clone()),
             [long_inv_freq, original_inv_freq],
         )
-        self.inv_freq = inv_freq
+        setattr(self, f"{prefix}inv_freq", inv_freq)
         # if seq_len > original_max_position_embeddings:
         #    self.inv_freq = self.long_inv_freq
         # else:
         #    self.inv_freq = self.original_inv_freq
 
-    def dynamic_frequency_update(self, position_ids, device):
+    def dynamic_frequency_update(self, position_ids, device, layer_type=None):
         # constructor:
         # - self.max_seq_len_cached = config.max_position_embeddings
         # - self.original_max_seq_len = config.max_position_embeddings
@@ -1128,12 +1153,7 @@ def dynamic_frequency_update(self, position_ids, device):
         # as rope_init_fn is an attribute set to one function when the model
         # is created and when no patch is applied yet.
         # So we select the patched version here.
-        rope_init_fn = (
-            patched__compute_dynamic_ntk_parameters
-            if self.rope_init_fn
-            is transformers.modeling_rope_utils._compute_dynamic_ntk_parameters
-            else self.rope_init_fn
-        )
+        rope_init_fn = _get_rope_init_fn(self, layer_type=layer_type)
 
         # This behaviour is difficult to translate.
         # The sequence always grows.
@@ -1162,6 +1182,19 @@ def dynamic_frequency_update(self, position_ids, device):
             self.config, device, seq_len=seq_len
         )
 
+        if layer_type is None:
+            # rope_type = self.rope_type
+            # max_seq_len_cached = self.max_seq_len_cached
+            original_inv_freq = self.original_inv_freq
+            prefix = ""
+        else:
+            # rope_type = self.rope_type[layer_type]
+            # max_seq_len_cached = getattr(
+            #     self, f"{layer_type}_max_seq_len_cached", self.max_seq_len_cached
+            # )
+            original_inv_freq = getattr(self, f"{layer_type}_original_inv_freq")
+            prefix = f"{layer_type}_"
+
         # Second test to translate.
         # Let's keep in mind, self.max_seq_len_cached = seq_len is likely to be True.
         # But in that case the following condition is a way to restore the original cache.
@@ -1183,15 +1216,26 @@ def dynamic_frequency_update(self, position_ids, device):
             (lambda x, y: y.clone()),
             [long_inv_freq, original_inv_freq],
         )
-        self.inv_freq = inv_freq
+        setattr(self, f"{prefix}inv_freq", inv_freq)
 
     @wraps(rope_forward)
-    def wrapper(self, x, position_ids):
+    def wrapper(self, x, position_ids, layer_type=None):
+        if layer_type is None:
+            if "dynamic" in self.rope_type:
+                dynamic_frequency_update(self, position_ids, device=x.device)
+            elif self.rope_type == "longrope":
+                longrope_frequency_update(self, position_ids, device=x.device)
+            return rope_forward(self, x, position_ids)
+
         if "dynamic" in self.rope_type:
-            dynamic_frequency_update(self, position_ids, device=x.device)
+            dynamic_frequency_update(
+                self, position_ids, device=x.device, layer_type=layer_type
+            )
         elif self.rope_type == "longrope":
-            longrope_frequency_update(self, position_ids, device=x.device)
-        return rope_forward(self, x, position_ids)
+            longrope_frequency_update(
+                self, position_ids, device=x.device, layer_type=layer_type
+            )
+        return rope_forward(self, x, position_ids, layer_type=layer_type)
 
     return wrapper
 
@@ -1287,12 +1331,18 @@ class common_RotaryEmbedding(torch.nn.Module):
     # @torch.no_grad()
     # PATCHED: the decorator
     @patched_dynamic_rope_update
-    def forward(self, x, position_ids):
+    def forward(self, x, position_ids, layer_type=None):
+        if layer_type is not None:
+            # transformers>=5.0
+            inv_freq = getattr(self, f"{layer_type}_inv_freq")
+            attention_scaling = getattr(self, f"{layer_type}_attention_scaling")
+        else:
+            # transformers<5.0
+            inv_freq = self.inv_freq
+            attention_scaling = self.attention_scaling
+
         inv_freq_expanded = (
-            self.inv_freq[None, :, None]
-            .float()
-            .expand(position_ids.shape[0], -1, 1)
-            .to(x.device)
+            inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
         )
         position_ids_expanded = position_ids[:, None, :].float()
 
@@ -1304,8 +1354,8 @@ def forward(self, x, position_ids):
         with torch.autocast(device_type=device_type, enabled=False):  # Force float32
             freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
-            cos = emb.cos() * self.attention_scaling
-            sin = emb.sin() * self.attention_scaling
+            cos = emb.cos() * attention_scaling
+            sin = emb.sin() * attention_scaling
 
         return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 

onnx_diagnostic/torch_models/hghub/model_inputs.py

@@ -95,6 +95,8 @@ def get_untrained_model_with_inputs(
         print("-- dynamic shapes:", pprint.pformat(data['dynamic_shapes']))
         print("-- configuration:", pprint.pformat(data['configuration']))
     """
+    if task == "":
+        task = None
     assert not use_preinstalled or not use_only_preinstalled, (
         f"model_id={model_id!r}, preinstalled model is only available "
         f"if use_only_preinstalled is False."
@@ -120,14 +122,16 @@ def get_untrained_model_with_inputs(
             **(model_kwargs or {}),
         )
 
-    model, task, mkwargs, diff_config = None, None, {}, None
+    model, task_, mkwargs, diff_config = None, None, {}, None
     if use_pretrained and same_as_pretrained:
         if model_id in HANDLED_MODELS:
-            model, task, config = load_specific_model(model_id, verbose=verbose)
+            model, task_, config = load_specific_model(model_id, verbose=verbose)
 
+    if task is None:
+        task = task_
     if model is None:
         arch = architecture_from_config(config)
-        if arch is None:
+        if task is None and arch is None:
             task = task_from_id(model_id, subfolder=subfolder)
         assert task is not None or arch is not None, (
             f"Unable to determine the architecture for model {model_id!r}, "

onnx_diagnostic/torch_models/validate.py

@@ -117,11 +117,21 @@ def _make_folder_name(
     drop_inputs: Optional[List[str]] = None,
     same_as_pretrained: bool = False,
     use_pretrained: bool = False,
+    task: Optional[str] = None,
 ) -> str:
     "Creates a filename unique based on the given options."
     els = [model_id.replace("/", "_")]
     if subfolder:
         els.append(subfolder.replace("/", "_"))
+    if not task:
+        els.append(task)  # type: ignore[arg-type]
+    if drop_inputs:
+        ii = "-".join(f"{s[0]}{s[-1]}" for s in drop_inputs)
+        els.append(f"I-{ii.upper()}")
+    if use_pretrained:
+        els.append("TRAINED")
+    elif same_as_pretrained:
+        els.append("SAMESIZE")
     if exporter:
         els.append(exporter)
     if optimization:
@@ -142,14 +152,7 @@ def _make_folder_name(
         els.append(sdev)
     if opset is not None:
         els.append(f"op{opset}")
-    if drop_inputs:
-        ii = "-".join(f"{s[0]}{s[-1]}" for s in drop_inputs)
-        els.append(f"I-{ii.upper()}")
-    if use_pretrained:
-        els.append("TRAINED")
-    elif same_as_pretrained:
-        els.append("SAMESIZE")
-    return "-".join(els)
+    return "/".join([e for e in els if e])
 
 
 def version_summary() -> Dict[str, Union[int, float, str]]:
@@ -476,6 +479,7 @@ def validate_model(
             drop_inputs=drop_inputs,
             use_pretrained=use_pretrained,
             same_as_pretrained=same_as_pretrained,
+            task=task,
         )
         dump_folder = os.path.join(dump_folder, folder_name)
         if not os.path.exists(dump_folder):
@@ -490,6 +494,8 @@ def validate_model(
             print(f"[validate_model] validate model id {model_id!r}, subfolder={subfolder!r}")
         else:
             print(f"[validate_model] validate model id {model_id!r}")
+        if task:
+            print(f"[validate_model] with task {task!r}")
         print(f"[validate_model] patch={patch!r}")
         if model_options:
             print(f"[validate_model] model_options={model_options!r}")
@@ -765,6 +771,10 @@ def validate_model(
             ep = data["exported_program"]
             if verbose:
                 print(f"[validate_model] -- dumps exported program in {dump_folder!r}...")
+            assert isinstance(
+                folder_name, str
+            ), f"folder_name={folder_name!r} should be a string"
+            folder_name = folder_name.replace("/", "-")
             with open(os.path.join(dump_folder, f"{folder_name}.ep"), "w") as f:
                 f.write(str(ep))
             torch.export.save(ep, os.path.join(dump_folder, f"{folder_name}.pt2"))
@@ -773,6 +783,10 @@ def validate_model(
             if verbose:
                 print("[validate_model] done (dump ep)")
         if "onnx_program" in data:
+            assert isinstance(
+                folder_name, str
+            ), f"folder_name={folder_name!r} should be a string"
+            folder_name = folder_name.replace("/", "-")
             epo = data["onnx_program"]
             if verbose:
                 print(f"[validate_model] dumps onnx program in {dump_folder!r}...")