Refactor conversion function (#19799)

* Refactor conversion function

* Remove dupe line

* Fixes

* Fixes

* Use the right variable...

* Fix last test

src/transformers/modeling_tf_pytorch_utils.py

@@ -21,7 +21,8 @@ import re
 
 import numpy
 
-from .utils import ExplicitEnum, logging
+from .utils import ExplicitEnum, expand_dims, is_numpy_array, is_torch_tensor, logging, reshape, squeeze
+from .utils import transpose as transpose_func
 
 
 logger = logging.get_logger(__name__)
@@ -66,10 +67,12 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="",
     if len(tf_name) > 1:
         tf_name = tf_name[1:]  # Remove level zero
 
+    tf_weight_shape = list(tf_weight_shape)
+
     # When should we transpose the weights
-    if tf_name[-1] == "kernel" and tf_weight_shape is not None and tf_weight_shape.rank == 4:
+    if tf_name[-1] == "kernel" and tf_weight_shape is not None and len(tf_weight_shape) == 4:
         transpose = TransposeType.CONV2D
-    elif tf_name[-1] == "kernel" and tf_weight_shape is not None and tf_weight_shape.rank == 3:
+    elif tf_name[-1] == "kernel" and tf_weight_shape is not None and len(tf_weight_shape) == 3:
         transpose = TransposeType.CONV1D
     elif bool(
         tf_name[-1] in ["kernel", "pointwise_kernel", "depthwise_kernel"]
@@ -98,6 +101,43 @@ def convert_tf_weight_name_to_pt_weight_name(tf_name, start_prefix_to_remove="",
     return tf_name, transpose
 
 
+def apply_transpose(transpose: TransposeType, weight, match_shape=None, pt_to_tf=True):
+    """
+    Apply a transpose to some weight then tries to reshape the weight to the same shape as a given shape, all in a
+    framework agnostic way.
+    """
+    if transpose is TransposeType.CONV2D:
+        # Conv2D weight:
+        #    PT: (num_out_channel, num_in_channel, kernel[0], kernel[1])
+        # -> TF: (kernel[0], kernel[1], num_in_channel, num_out_channel)
+        axes = (2, 3, 1, 0) if pt_to_tf else (3, 2, 0, 1)
+        weight = transpose_func(weight, axes=axes)
+    elif transpose is TransposeType.CONV1D:
+        # Conv1D weight:
+        #    PT: (num_out_channel, num_in_channel, kernel)
+        # -> TF: (kernel, num_in_channel, num_out_channel)
+        weight = transpose_func(weight, axes=(2, 1, 0))
+    elif transpose is TransposeType.SIMPLE:
+        weight = transpose_func(weight)
+
+    if match_shape is None:
+        return weight
+
+    if len(match_shape) < len(weight.shape):
+        weight = squeeze(weight)
+    elif len(match_shape) > len(weight.shape):
+        weight = expand_dims(weight, axis=0)
+
+    if list(match_shape) != list(weight.shape):
+        try:
+            weight = reshape(weight, match_shape)
+        except AssertionError as e:
+            e.args += (match_shape, match_shape)
+            raise e
+
+    return weight
+
+
 #####################
 # PyTorch => TF 2.0 #
 #####################
@@ -155,7 +195,6 @@ def load_pytorch_weights_in_tf2_model(
     try:
         import tensorflow as tf  # noqa: F401
         import torch  # noqa: F401
-        from tensorflow.python.keras import backend as K
     except ImportError:
         logger.error(
             "Loading a PyTorch model in TensorFlow, requires both PyTorch and TensorFlow to be installed. Please see "
@@ -163,6 +202,22 @@ def load_pytorch_weights_in_tf2_model(
         )
         raise
 
+    pt_state_dict = {k: v.numpy() for k, v in pt_state_dict.items()}
+    return load_pytorch_state_dict_in_tf2_model(
+        tf_model,
+        pt_state_dict,
+        tf_inputs=tf_inputs,
+        allow_missing_keys=allow_missing_keys,
+        output_loading_info=output_loading_info,
+    )
+
+
+def load_pytorch_state_dict_in_tf2_model(
+    tf_model, pt_state_dict, tf_inputs=None, allow_missing_keys=False, output_loading_info=False
+):
+    """Load a pytorch state_dict in a TF 2.0 model."""
+    from tensorflow.python.keras import backend as K
+
     if tf_inputs is None:
         tf_inputs = tf_model.dummy_inputs
 
@@ -216,41 +271,9 @@ def load_pytorch_weights_in_tf2_model(
                     continue
             raise AttributeError(f"{name} not found in PyTorch model")
 
-        array = pt_state_dict[name].numpy()
-
-        if transpose is TransposeType.CONV2D:
-            # Conv2D weight:
-            #    PT: (num_out_channel, num_in_channel, kernel[0], kernel[1])
-            # -> TF: (kernel[0], kernel[1], num_in_channel, num_out_channel)
-            array = numpy.transpose(array, axes=(2, 3, 1, 0))
-        elif transpose is TransposeType.CONV1D:
-            # Conv1D weight:
-            #    PT: (num_out_channel, num_in_channel, kernel)
-            # -> TF: (kernel, num_in_channel, num_out_channel)
-            array = numpy.transpose(array, axes=(2, 1, 0))
-        elif transpose is TransposeType.SIMPLE:
-            array = numpy.transpose(array)
-
-        if len(symbolic_weight.shape) < len(array.shape):
-            array = numpy.squeeze(array)
-        elif len(symbolic_weight.shape) > len(array.shape):
-            array = numpy.expand_dims(array, axis=0)
-
-        if list(symbolic_weight.shape) != list(array.shape):
-            try:
-                array = numpy.reshape(array, symbolic_weight.shape)
-            except AssertionError as e:
-                e.args += (symbolic_weight.shape, array.shape)
-                raise e
-
-        try:
-            assert list(symbolic_weight.shape) == list(array.shape)
-        except AssertionError as e:
-            e.args += (symbolic_weight.shape, array.shape)
-            raise e
+        array = apply_transpose(transpose, pt_state_dict[name], symbolic_weight.shape)
 
         tf_loaded_numel += array.size
-        # logger.warning(f"Initialize TF weight {symbolic_weight.name}")
 
         weight_value_tuples.append((symbolic_weight, array))
         all_pytorch_weights.discard(name)
@@ -370,6 +393,15 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
         )
         raise
 
+    tf_state_dict = {tf_weight.name: tf_weight.numpy() for tf_weight in tf_weights}
+    return load_tf2_state_dict_in_pytorch_model(
+        pt_model, tf_state_dict, allow_missing_keys=allow_missing_keys, output_loading_info=output_loading_info
+    )
+
+
+def load_tf2_state_dict_in_pytorch_model(pt_model, tf_state_dict, allow_missing_keys=False, output_loading_info=False):
+    import torch
+
     new_pt_params_dict = {}
     current_pt_params_dict = dict(pt_model.named_parameters())
 
@@ -381,11 +413,11 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
     # Build a map from potential PyTorch weight names to TF 2.0 Variables
     tf_weights_map = {}
-    for tf_weight in tf_weights:
+    for name, tf_weight in tf_state_dict.items():
         pt_name, transpose = convert_tf_weight_name_to_pt_weight_name(
-            tf_weight.name, start_prefix_to_remove=start_prefix_to_remove, tf_weight_shape=tf_weight.shape
+            name, start_prefix_to_remove=start_prefix_to_remove, tf_weight_shape=tf_weight.shape
         )
-        tf_weights_map[pt_name] = (tf_weight.numpy(), transpose)
+        tf_weights_map[pt_name] = (tf_weight, transpose)
 
     all_tf_weights = set(list(tf_weights_map.keys()))
     loaded_pt_weights_data_ptr = {}
@@ -406,43 +438,18 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
 
         array, transpose = tf_weights_map[pt_weight_name]
 
-        if transpose is TransposeType.CONV2D:
-            # Conv2D weight:
-            #    TF: (kernel[0], kernel[1], num_in_channel, num_out_channel)
-            # -> PT: (num_out_channel, num_in_channel, kernel[0], kernel[1])
-            array = numpy.transpose(array, axes=(3, 2, 0, 1))
-        elif transpose is TransposeType.CONV1D:
-            # Conv1D weight:
-            #    TF: (kernel, num_in_channel, num_out_channel)
-            # -> PT: (num_out_channel, num_in_channel, kernel)
-            array = numpy.transpose(array, axes=(2, 1, 0))
-        elif transpose is TransposeType.SIMPLE:
-            array = numpy.transpose(array)
+        array = apply_transpose(transpose, array, pt_weight.shape, pt_to_tf=False)
 
-        if len(pt_weight.shape) < len(array.shape):
-            array = numpy.squeeze(array)
-        elif len(pt_weight.shape) > len(array.shape):
-            array = numpy.expand_dims(array, axis=0)
-
-        if list(pt_weight.shape) != list(array.shape):
-            try:
-                array = numpy.reshape(array, pt_weight.shape)
-            except AssertionError as e:
-                e.args += (pt_weight.shape, array.shape)
-                raise e
-
-        try:
-            assert list(pt_weight.shape) == list(array.shape)
-        except AssertionError as e:
-            e.args += (pt_weight.shape, array.shape)
-            raise e
-
-        # logger.warning(f"Initialize PyTorch weight {pt_weight_name}")
-        # Make sure we have a proper numpy array
         if numpy.isscalar(array):
             array = numpy.array(array)
-        new_pt_params_dict[pt_weight_name] = torch.from_numpy(array)
-        loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = torch.from_numpy(array)
+        if not is_torch_tensor(array) and not is_numpy_array(array):
+            array = array.numpy()
+        if is_numpy_array(array):
+            # Convert to torch tensor
+            array = torch.from_numpy(array)
+
+        new_pt_params_dict[pt_weight_name] = array
+        loaded_pt_weights_data_ptr[pt_weight.data_ptr()] = array
         all_tf_weights.discard(pt_weight_name)
 
     missing_keys, unexpected_keys = pt_model.load_state_dict(new_pt_params_dict, strict=False)

src/transformers/utils/__init__.py

@@ -38,6 +38,7 @@ from .generic import (
     PaddingStrategy,
     TensorType,
     cached_property,
+    expand_dims,
     find_labels,
     flatten_dict,
     is_jax_tensor,
@@ -46,8 +47,11 @@ from .generic import (
     is_tf_tensor,
     is_torch_device,
     is_torch_tensor,
+    reshape,
+    squeeze,
     to_numpy,
     to_py_obj,
+    transpose,
     working_or_temp_dir,
 )
 from .hub import (

src/transformers/utils/generic.py

@@ -29,6 +29,13 @@ import numpy as np
 from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
 
 
+if is_tf_available():
+    import tensorflow as tf
+
+if is_flax_available():
+    import jax.numpy as jnp
+
+
 class cached_property(property):
     """
     Descriptor that mimics @property but caches output in member variable.
@@ -370,3 +377,71 @@ def working_or_temp_dir(working_dir, use_temp_dir: bool = False):
             yield tmp_dir
     else:
         yield working_dir
+
+
+def transpose(array, axes=None):
+    """
+    Framework-agnostic version of `numpy.transpose` that will work on torch/TensorFlow/Jax tensors as well as NumPy
+    arrays.
+    """
+    if is_numpy_array(array):
+        return np.transpose(array, axes=axes)
+    elif is_torch_tensor(array):
+        return array.T if axes is None else array.permute(*axes)
+    elif is_tf_tensor(array):
+        return tf.transpose(array, perm=axes)
+    elif is_jax_tensor(array):
+        return jnp.transpose(array, axes=axes)
+    else:
+        raise ValueError(f"Type not supported for transpose: {type(array)}.")
+
+
+def reshape(array, newshape):
+    """
+    Framework-agnostic version of `numpy.reshape` that will work on torch/TensorFlow/Jax tensors as well as NumPy
+    arrays.
+    """
+    if is_numpy_array(array):
+        return np.reshape(array, newshape)
+    elif is_torch_tensor(array):
+        return array.reshape(*newshape)
+    elif is_tf_tensor(array):
+        return tf.reshape(array, newshape)
+    elif is_jax_tensor(array):
+        return jnp.reshape(array, newshape)
+    else:
+        raise ValueError(f"Type not supported for reshape: {type(array)}.")
+
+
+def squeeze(array, axis=None):
+    """
+    Framework-agnostic version of `numpy.squeeze` that will work on torch/TensorFlow/Jax tensors as well as NumPy
+    arrays.
+    """
+    if is_numpy_array(array):
+        return np.squeeze(array, axis=axis)
+    elif is_torch_tensor(array):
+        return array.squeeze() if axis is None else array.squeeze(dim=axis)
+    elif is_tf_tensor(array):
+        return tf.squeeze(array, axis=axis)
+    elif is_jax_tensor(array):
+        return jnp.squeeze(array, axis=axis)
+    else:
+        raise ValueError(f"Type not supported for squeeze: {type(array)}.")
+
+
+def expand_dims(array, axis):
+    """
+    Framework-agnostic version of `numpy.expand_dims` that will work on torch/TensorFlow/Jax tensors as well as NumPy
+    arrays.
+    """
+    if is_numpy_array(array):
+        return np.expand_dims(array, axis)
+    elif is_torch_tensor(array):
+        return array.unsqueeze(dim=axis)
+    elif is_tf_tensor(array):
+        return tf.expand_dims(array, axis=axis)
+    elif is_jax_tensor(array):
+        return jnp.expand_dims(array, axis=axis)
+    else:
+        raise ValueError(f"Type not supported for expand_dims: {type(array)}.")

tests/utils/test_generic.py

@@ -15,7 +15,29 @@
 
 import unittest
 
-from transformers.utils import flatten_dict
+import numpy as np
+
+from transformers.testing_utils import require_flax, require_tf, require_torch
+from transformers.utils import (
+    expand_dims,
+    flatten_dict,
+    is_flax_available,
+    is_tf_available,
+    is_torch_available,
+    reshape,
+    squeeze,
+    transpose,
+)
+
+
+if is_flax_available():
+    import jax.numpy as jnp
+
+if is_tf_available():
+    import tensorflow as tf
+
+if is_torch_available():
+    import torch
 
 
 class GenericTester(unittest.TestCase):
@@ -43,3 +65,136 @@ class GenericTester(unittest.TestCase):
         }
 
         self.assertEqual(flatten_dict(input_dict), expected_dict)
+
+    def test_transpose_numpy(self):
+        x = np.random.randn(3, 4)
+        self.assertTrue(np.allclose(transpose(x), x.transpose()))
+
+        x = np.random.randn(3, 4, 5)
+        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), x.transpose((1, 2, 0))))
+
+    @require_torch
+    def test_transpose_torch(self):
+        x = np.random.randn(3, 4)
+        t = torch.tensor(x)
+        self.assertTrue(np.allclose(transpose(x), transpose(t).numpy()))
+
+        x = np.random.randn(3, 4, 5)
+        t = torch.tensor(x)
+        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), transpose(t, axes=(1, 2, 0)).numpy()))
+
+    @require_tf
+    def test_transpose_tf(self):
+        x = np.random.randn(3, 4)
+        t = tf.constant(x)
+        self.assertTrue(np.allclose(transpose(x), transpose(t).numpy()))
+
+        x = np.random.randn(3, 4, 5)
+        t = tf.constant(x)
+        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), transpose(t, axes=(1, 2, 0)).numpy()))
+
+    @require_flax
+    def test_transpose_flax(self):
+        x = np.random.randn(3, 4)
+        t = jnp.array(x)
+        self.assertTrue(np.allclose(transpose(x), np.asarray(transpose(t))))
+
+        x = np.random.randn(3, 4, 5)
+        t = jnp.array(x)
+        self.assertTrue(np.allclose(transpose(x, axes=(1, 2, 0)), np.asarray(transpose(t, axes=(1, 2, 0)))))
+
+    def test_reshape_numpy(self):
+        x = np.random.randn(3, 4)
+        self.assertTrue(np.allclose(reshape(x, (4, 3)), np.reshape(x, (4, 3))))
+
+        x = np.random.randn(3, 4, 5)
+        self.assertTrue(np.allclose(reshape(x, (12, 5)), np.reshape(x, (12, 5))))
+
+    @require_torch
+    def test_reshape_torch(self):
+        x = np.random.randn(3, 4)
+        t = torch.tensor(x)
+        self.assertTrue(np.allclose(reshape(x, (4, 3)), reshape(t, (4, 3)).numpy()))
+
+        x = np.random.randn(3, 4, 5)
+        t = torch.tensor(x)
+        self.assertTrue(np.allclose(reshape(x, (12, 5)), reshape(t, (12, 5)).numpy()))
+
+    @require_tf
+    def test_reshape_tf(self):
+        x = np.random.randn(3, 4)
+        t = tf.constant(x)
+        self.assertTrue(np.allclose(reshape(x, (4, 3)), reshape(t, (4, 3)).numpy()))
+
+        x = np.random.randn(3, 4, 5)
+        t = tf.constant(x)
+        self.assertTrue(np.allclose(reshape(x, (12, 5)), reshape(t, (12, 5)).numpy()))
+
+    @require_flax
+    def test_reshape_flax(self):
+        x = np.random.randn(3, 4)
+        t = jnp.array(x)
+        self.assertTrue(np.allclose(reshape(x, (4, 3)), np.asarray(reshape(t, (4, 3)))))
+
+        x = np.random.randn(3, 4, 5)
+        t = jnp.array(x)
+        self.assertTrue(np.allclose(reshape(x, (12, 5)), np.asarray(reshape(t, (12, 5)))))
+
+    def test_squeeze_numpy(self):
+        x = np.random.randn(1, 3, 4)
+        self.assertTrue(np.allclose(squeeze(x), np.squeeze(x)))
+
+        x = np.random.randn(1, 4, 1, 5)
+        self.assertTrue(np.allclose(squeeze(x, axis=2), np.squeeze(x, axis=2)))
+
+    @require_torch
+    def test_squeeze_torch(self):
+        x = np.random.randn(1, 3, 4)
+        t = torch.tensor(x)
+        self.assertTrue(np.allclose(squeeze(x), squeeze(t).numpy()))
+
+        x = np.random.randn(1, 4, 1, 5)
+        t = torch.tensor(x)
+        self.assertTrue(np.allclose(squeeze(x, axis=2), squeeze(t, axis=2).numpy()))
+
+    @require_tf
+    def test_squeeze_tf(self):
+        x = np.random.randn(1, 3, 4)
+        t = tf.constant(x)
+        self.assertTrue(np.allclose(squeeze(x), squeeze(t).numpy()))
+
+        x = np.random.randn(1, 4, 1, 5)
+        t = tf.constant(x)
+        self.assertTrue(np.allclose(squeeze(x, axis=2), squeeze(t, axis=2).numpy()))
+
+    @require_flax
+    def test_squeeze_flax(self):
+        x = np.random.randn(1, 3, 4)
+        t = jnp.array(x)
+        self.assertTrue(np.allclose(squeeze(x), np.asarray(squeeze(t))))
+
+        x = np.random.randn(1, 4, 1, 5)
+        t = jnp.array(x)
+        self.assertTrue(np.allclose(squeeze(x, axis=2), np.asarray(squeeze(t, axis=2))))
+
+    def test_expand_dims_numpy(self):
+        x = np.random.randn(3, 4)
+        self.assertTrue(np.allclose(expand_dims(x, axis=1), np.expand_dims(x, axis=1)))
+
+    @require_torch
+    def test_expand_dims_torch(self):
+        x = np.random.randn(3, 4)
+        t = torch.tensor(x)
+        self.assertTrue(np.allclose(expand_dims(x, axis=1), expand_dims(t, axis=1).numpy()))
+
+    @require_tf
+    def test_expand_dims_tf(self):
+        x = np.random.randn(3, 4)
+        t = tf.constant(x)
+        self.assertTrue(np.allclose(expand_dims(x, axis=1), expand_dims(t, axis=1).numpy()))
+
+    @require_flax
+    def test_expand_dims_flax(self):
+        x = np.random.randn(3, 4)
+        t = jnp.array(x)
+        self.assertTrue(np.allclose(expand_dims(x, axis=1), np.asarray(expand_dims(t, axis=1))))