diff --git a/einops/layers/_einmix.py b/einops/layers/_einmix.py
index ffef1de9..555441cb 100644
--- a/einops/layers/_einmix.py
+++ b/einops/layers/_einmix.py
@@ -1,7 +1,7 @@
 from typing import Any, List, Optional, Dict
 
 from einops import EinopsError
-from einops.parsing import ParsedExpression
+from einops.parsing import ParsedExpression, _ellipsis
 import warnings
 import string
 from ..einops import _product
@@ -17,21 +17,21 @@ def __init__(self, pattern: str, weight_shape: str, bias_shape: Optional[str] =
         """
         EinMix - Einstein summation with automated tensor management and axis packing/unpacking.
 
-        EinMix is an advanced tool, helpful tutorial:
+        EinMix is a combination of einops and MLP, see tutorial:
         https://github.com/arogozhnikov/einops/blob/main/docs/3-einmix-layer.ipynb
 
         Imagine taking einsum with two arguments, one of each input, and one - tensor with weights
         >>> einsum('time batch channel_in, channel_in channel_out -> time batch channel_out', input, weight)
 
-        This layer manages weights for you, syntax highlights separate role of weight matrix
+        This layer manages weights for you, syntax highlights a special role of weight matrix
         >>> EinMix('time batch channel_in -> time batch channel_out', weight_shape='channel_in channel_out')
-        But otherwise it is the same einsum under the hood.
+        But otherwise it is the same einsum under the hood. Plus einops-rearrange.
 
-        Simple linear layer with bias term (you have one like that in your framework)
+        Simple linear layer with a bias term (you have one like that in your framework)
         >>> EinMix('t b cin -> t b cout', weight_shape='cin cout', bias_shape='cout', cin=10, cout=20)
         There is no restriction to mix the last axis. Let's mix along height
         >>> EinMix('h w c-> hout w c', weight_shape='h hout', bias_shape='hout', h=32, hout=32)
-        Channel-wise multiplication (like one used in normalizations)
+        Example of channel-wise multiplication (like one used in normalizations)
         >>> EinMix('t b c -> t b c', weight_shape='c', c=128)
         Multi-head linear layer (each head is own linear layer):
         >>> EinMix('t b (head cin) -> t b (head cout)', weight_shape='head cin cout', ...)
@@ -42,14 +42,16 @@ def __init__(self, pattern: str, weight_shape: str, bias_shape: Optional[str] =
         - when channel dimension is not last, use EinMix, not transposition
         - patch/segment embeddings
         - when need only within-group connections to reduce number of weights and computations
-        - perfect as a part of sequential models
-        - next-gen MLPs (follow tutorial to learn more!)
+        - next-gen MLPs (follow tutorial link above to learn more!)
+        - in general, any time you want to combine linear layer and einops.rearrange
 
-        Uniform He initialization is applied to weight tensor. This accounts for number of elements mixed.
+        Uniform He initialization is applied to weight tensor.
+        This accounts for the number of elements mixed and produced.
 
         Parameters
         :param pattern: transformation pattern, left side - dimensions of input, right side - dimensions of output
         :param weight_shape: axes of weight. A tensor of this shape is created, stored, and optimized in a layer
+               If bias_shape is not specified, bias is not created.
         :param bias_shape: axes of bias added to output. Weights of this shape are created and stored. If `None` (the default), no bias is added.
         :param axes_lengths: dimensions of weight tensor
         """
@@ -71,9 +73,13 @@ def initialize_einmix(self, pattern: str, weight_shape: str, bias_shape: Optiona
             set.difference(right.identifiers, {*left.identifiers, *weight.identifiers}),
             "Unrecognized identifiers on the right side of EinMix {}",
         )
-
-        if left.has_ellipsis or right.has_ellipsis or weight.has_ellipsis:
-            raise EinopsError("Ellipsis is not supported in EinMix (right now)")
+        if weight.has_ellipsis:
+            raise EinopsError("Ellipsis is not supported in weight, as its shape should be fully specified")
+        if left.has_ellipsis or right.has_ellipsis:
+            if not (left.has_ellipsis and right.has_ellipsis):
+                raise EinopsError(f"Ellipsis in EinMix should be on both sides, {pattern}")
+            if left.has_ellipsis_parenthesized:
+                raise EinopsError(f"Ellipsis on left side can't be in parenthesis, got {pattern}")
         if any(x.has_non_unitary_anonymous_axes for x in [left, right, weight]):
             raise EinopsError("Anonymous axes (numbers) are not allowed in EinMix")
         if "(" in weight_shape or ")" in weight_shape:
@@ -86,16 +92,18 @@ def initialize_einmix(self, pattern: str, weight_shape: str, bias_shape: Optiona
             names: List[str] = []
             for group in left.composition:
                 names += group
+            names = [name if name != _ellipsis else "..." for name in names]
             composition = " ".join(names)
-            pre_reshape_pattern = f"{left_pattern}->{composition}"
+            pre_reshape_pattern = f"{left_pattern}-> {composition}"
             pre_reshape_lengths = {name: length for name, length in axes_lengths.items() if name in names}
 
-        if any(len(group) != 1 for group in right.composition):
+        if any(len(group) != 1 for group in right.composition) or right.has_ellipsis_parenthesized:
             names = []
             for group in right.composition:
                 names += group
+            names = [name if name != _ellipsis else "..." for name in names]
             composition = " ".join(names)
-            post_reshape_pattern = f"{composition}->{right_pattern}"
+            post_reshape_pattern = f"{composition} ->{right_pattern}"
 
         self._create_rearrange_layers(pre_reshape_pattern, pre_reshape_lengths, post_reshape_pattern, {})
 
@@ -116,22 +124,36 @@ def initialize_einmix(self, pattern: str, weight_shape: str, bias_shape: Optiona
         # single output element is a combination of fan_in input elements
         _fan_in = _product([axes_lengths[axis] for (axis,) in weight.composition if axis not in right.identifiers])
         if bias_shape is not None:
+            # maybe I should put ellipsis in the beginning for simplicity?
             if not isinstance(bias_shape, str):
                 raise EinopsError("bias shape should be string specifying which axes bias depends on")
             bias = ParsedExpression(bias_shape)
-            _report_axes(set.difference(bias.identifiers, right.identifiers), "Bias axes {} not present in output")
+            _report_axes(
+                set.difference(bias.identifiers, right.identifiers),
+                "Bias axes {} not present in output",
+            )
             _report_axes(
                 set.difference(bias.identifiers, set(axes_lengths)),
                 "Sizes not provided for bias axes {}",
             )
 
             _bias_shape = []
+            used_non_trivial_size = False
             for axes in right.composition:
-                for axis in axes:
-                    if axis in bias.identifiers:
-                        _bias_shape.append(axes_lengths[axis])
-                    else:
-                        _bias_shape.append(1)
+                if axes == _ellipsis:
+                    if used_non_trivial_size:
+                        raise EinopsError("all bias dimensions should go after ellipsis in the output")
+                else:
+                    # handles ellipsis correctly
+                    for axis in axes:
+                        if axis == _ellipsis:
+                            if used_non_trivial_size:
+                                raise EinopsError("all bias dimensions should go after ellipsis in the output")
+                        elif axis in bias.identifiers:
+                            _bias_shape.append(axes_lengths[axis])
+                            used_non_trivial_size = True
+                        else:
+                            _bias_shape.append(1)
         else:
             _bias_shape = None
 
@@ -142,15 +164,26 @@ def initialize_einmix(self, pattern: str, weight_shape: str, bias_shape: Optiona
         # rewrite einsum expression with single-letter latin identifiers so that
         # expression will be understood by any framework
         mapped_identifiers = {*left.identifiers, *right.identifiers, *weight.identifiers}
+        if _ellipsis in mapped_identifiers:
+            mapped_identifiers.remove(_ellipsis)
+        mapped_identifiers = list(sorted(mapped_identifiers))
         mapping2letters = {k: letter for letter, k in zip(string.ascii_lowercase, mapped_identifiers)}
-
-        def write_flat(axes: list):
-            return "".join(mapping2letters[axis] for axis in axes)
+        mapping2letters[_ellipsis] = "..."  # preserve ellipsis
+
+        def write_flat_remapped(axes: ParsedExpression):
+            result = []
+            for composed_axis in axes.composition:
+                if isinstance(composed_axis, list):
+                    result.extend([mapping2letters[axis] for axis in composed_axis])
+                else:
+                    assert composed_axis == _ellipsis
+                    result.append("...")
+            return "".join(result)
 
         self.einsum_pattern: str = "{},{}->{}".format(
-            write_flat(left.flat_axes_order()),
-            write_flat(weight.flat_axes_order()),
-            write_flat(right.flat_axes_order()),
+            write_flat_remapped(left),
+            write_flat_remapped(weight),
+            write_flat_remapped(right),
         )
 
     def _create_rearrange_layers(
@@ -174,3 +207,23 @@ def __repr__(self):
         for axis, length in self.axes_lengths.items():
             params += ", {}={}".format(axis, length)
         return "{}({})".format(self.__class__.__name__, params)
+
+
+class _EinmixDebugger(_EinmixMixin):
+    """Used only to test mixin"""
+
+    def _create_rearrange_layers(
+        self,
+        pre_reshape_pattern: Optional[str],
+        pre_reshape_lengths: Optional[Dict],
+        post_reshape_pattern: Optional[str],
+        post_reshape_lengths: Optional[Dict],
+    ):
+        self.pre_reshape_pattern = pre_reshape_pattern
+        self.pre_reshape_lengths = pre_reshape_lengths
+        self.post_reshape_pattern = post_reshape_pattern
+        self.post_reshape_lengths = post_reshape_lengths
+
+    def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
+        self.saved_weight_shape = weight_shape
+        self.saved_bias_shape = bias_shape
diff --git a/einops/tests/test_layers.py b/einops/tests/test_layers.py
index 83f18163..a70a5d67 100644
--- a/einops/tests/test_layers.py
+++ b/einops/tests/test_layers.py
@@ -4,7 +4,7 @@
 import numpy
 import pytest
 
-from einops import rearrange, reduce
+from einops import rearrange, reduce, EinopsError
 from einops.tests import collect_test_backends, is_backend_tested, FLOAT_REDUCTIONS as REDUCTIONS
 
 __author__ = "Alex Rogozhnikov"
@@ -343,3 +343,127 @@ def eval_at_point(params):
         # check serialization
         fbytes = flax.serialization.to_bytes(params)
         _loaded = flax.serialization.from_bytes(params, fbytes)
+
+
+def test_einmix_decomposition():
+    """
+    Testing that einmix correctly decomposes into smaller transformations.
+    """
+    from einops.layers._einmix import _EinmixDebugger
+
+    mixin1 = _EinmixDebugger(
+        "a b c d e -> e d c b a",
+        weight_shape="d a b",
+        d=2, a=3, b=5,
+    )  # fmt: off
+    assert mixin1.pre_reshape_pattern is None
+    assert mixin1.post_reshape_pattern is None
+    assert mixin1.einsum_pattern == "abcde,dab->edcba"
+    assert mixin1.saved_weight_shape == [2, 3, 5]
+    assert mixin1.saved_bias_shape is None
+
+    mixin2 = _EinmixDebugger(
+        "a b c d e -> e d c b a",
+        weight_shape="d a b",
+        bias_shape="a b c d e",
+        a=1, b=2, c=3, d=4, e=5,
+    )  # fmt: off
+    assert mixin2.pre_reshape_pattern is None
+    assert mixin2.post_reshape_pattern is None
+    assert mixin2.einsum_pattern == "abcde,dab->edcba"
+    assert mixin2.saved_weight_shape == [4, 1, 2]
+    assert mixin2.saved_bias_shape == [5, 4, 3, 2, 1]
+
+    mixin3 = _EinmixDebugger(
+        "... -> ...",
+        weight_shape="",
+        bias_shape="",
+    )  # fmt: off
+    assert mixin3.pre_reshape_pattern is None
+    assert mixin3.post_reshape_pattern is None
+    assert mixin3.einsum_pattern == "...,->..."
+    assert mixin3.saved_weight_shape == []
+    assert mixin3.saved_bias_shape == []
+
+    mixin4 = _EinmixDebugger(
+        "b a ...  -> b c ...",
+        weight_shape="b a c",
+        a=1, b=2, c=3,
+    )  # fmt: off
+    assert mixin4.pre_reshape_pattern is None
+    assert mixin4.post_reshape_pattern is None
+    assert mixin4.einsum_pattern == "ba...,bac->bc..."
+    assert mixin4.saved_weight_shape == [2, 1, 3]
+    assert mixin4.saved_bias_shape is None
+
+    mixin5 = _EinmixDebugger(
+        "(b a) ... -> b c (...)",
+        weight_shape="b a c",
+        a=1, b=2, c=3,
+    )  # fmt: off
+    assert mixin5.pre_reshape_pattern == "(b a) ... -> b a ..."
+    assert mixin5.pre_reshape_lengths == dict(a=1, b=2)
+    assert mixin5.post_reshape_pattern == "b c ... -> b c (...)"
+    assert mixin5.einsum_pattern == "ba...,bac->bc..."
+    assert mixin5.saved_weight_shape == [2, 1, 3]
+    assert mixin5.saved_bias_shape is None
+
+    mixin6 = _EinmixDebugger(
+        "b ... (a c) -> b ... (a d)",
+        weight_shape="c d",
+        bias_shape="a d",
+        a=1, c=3, d=4,
+    )  # fmt: off
+    assert mixin6.pre_reshape_pattern == "b ... (a c) -> b ... a c"
+    assert mixin6.pre_reshape_lengths == dict(a=1, c=3)
+    assert mixin6.post_reshape_pattern == "b ... a d -> b ... (a d)"
+    assert mixin6.einsum_pattern == "b...ac,cd->b...ad"
+    assert mixin6.saved_weight_shape == [3, 4]
+    assert mixin6.saved_bias_shape == [1, 1, 4]  # (b) a d, ellipsis does not participate
+
+    mixin7 = _EinmixDebugger(
+        "a ... (b c) -> a (... d b)",
+        weight_shape="c d b",
+        bias_shape="d b",
+        b=2, c=3, d=4,
+    )  # fmt: off
+    assert mixin7.pre_reshape_pattern == "a ... (b c) -> a ... b c"
+    assert mixin7.pre_reshape_lengths == dict(b=2, c=3)
+    assert mixin7.post_reshape_pattern == "a ... d b -> a (... d b)"
+    assert mixin7.einsum_pattern == "a...bc,cdb->a...db"
+    assert mixin7.saved_weight_shape == [3, 4, 2]
+    assert mixin7.saved_bias_shape == [1, 4, 2]  # (a) d b, ellipsis does not participate
+
+
+def test_einmix_restrictions():
+    """
+    Testing different cases
+    """
+    from einops.layers._einmix import _EinmixDebugger
+
+    with pytest.raises(EinopsError):
+        _EinmixDebugger(
+            "a b c d e -> e d c b a",
+            weight_shape="d a b",
+            d=2, a=3, # missing b
+        )  # fmt: off
+
+    with pytest.raises(EinopsError):
+        _EinmixDebugger(
+            "a b c d e -> e d c b a",
+            weight_shape="w a b",
+            d=2, a=3, b=1 # missing d
+        )  # fmt: off
+
+    with pytest.raises(EinopsError):
+        _EinmixDebugger(
+            "(...) a -> ... a",
+            weight_shape="a", a=1, # ellipsis on the left
+        )  # fmt: off
+
+    with pytest.raises(EinopsError):
+        _EinmixDebugger(
+            "(...) a -> a ...",
+            weight_shape="a", a=1, # ellipsis on the right side after bias axis
+            bias_shape='a',
+        )  # fmt: off