diff --git a/coremltools/converters/mil/mil/passes/__init__.py b/coremltools/converters/mil/mil/passes/__init__.py
index 4e3f40b1b..05b5e149b 100644
--- a/coremltools/converters/mil/mil/passes/__init__.py
+++ b/coremltools/converters/mil/mil/passes/__init__.py
@@ -44,5 +44,6 @@
     optimize_state,
     optimize_tensor_operation,
     preprocess,
+    transformer,
     symbol_transform,
 )
diff --git a/coremltools/converters/mil/mil/passes/defs/transformer.py b/coremltools/converters/mil/mil/passes/defs/transformer.py
new file mode 100644
index 000000000..ca3b79c4a
--- /dev/null
+++ b/coremltools/converters/mil/mil/passes/defs/transformer.py
@@ -0,0 +1,185 @@
+#  Copyright (c) 2024, Apple Inc. All rights reserved.
+#
+#  Use of this source code is governed by a BSD-3-clause license that can be
+#  found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
+
+from typing import ClassVar, List, Tuple
+
+import numpy as np
+
+from coremltools.converters.mil.mil.passes.graph_pass import AbstractGraphPass
+from coremltools.converters.mil.mil.passes.pass_registry import register_pass
+from coremltools.converters.mil._deployment_compatibility import AvailableTarget as target
+from coremltools.converters.mil.mil import Builder as mb
+from coremltools.converters.mil.mil import types
+
+from coremltools import _logger as logger
+
+
+@register_pass(namespace="common")
+class scaled_dot_product_attention_sliced_q(AbstractGraphPass):
+    """
+    Replace the ios18.scaled_dot_product_attention operation with a memory efficient
+    implementation of attention calculation based on slicing Q. The benefits are clearly
+    visible for higher Q sequence lengths, though.
+
+    Graph pass options:
+      - min_seq_length: int
+        Only operations working with Q of sequence length greater or equal to this value will be transformed.
+      - seq_length_divider: int
+        Defines the size of the chunks of Q being processed in SDPA (chunk_size = seq_length / seq_length_divider)
+    """
+
+    _DEFAULT_MIN_SEQ_LENGTH: ClassVar[int] = 1280
+    _DEFAULT_SEQ_LENGTH_DIVIDER: ClassVar[int] = 16
+
+    _min_seq_length: int
+    _seq_length_divider: int
+
+    def __init__(self):
+        super().__init__()
+        self._min_seq_length = self._DEFAULT_MIN_SEQ_LENGTH
+        self._seq_length_divider = self._DEFAULT_SEQ_LENGTH_DIVIDER
+
+    @property
+    def min_seq_length(self) -> int:
+        return self._min_seq_length
+
+    @min_seq_length.setter
+    def min_seq_length(self, length: int) -> None:
+        if not isinstance(length, int):
+            raise ValueError("pass option min_seq_length must be an int")
+        if length < 0:
+            raise ValueError("pass option min_seq_length must be >= 0")
+        self._min_seq_length = length
+
+    @property
+    def seq_length_divider(self) -> int:
+        return self._seq_length_divider
+
+    @seq_length_divider.setter
+    def seq_length_divider(self, divider: int) -> None:
+        if not isinstance(divider, int):
+            raise ValueError("pass option seq_length_divider must be an int")
+        if divider < 1:
+            raise ValueError("pass option seq_length_divider must be >= 1")
+        self._seq_length_divider = divider
+
+    def apply(self, prog):
+        for f in prog.functions.values():
+            if f.opset_version < target.iOS18:
+                logger.debug(f"ignoring block '{f.name}', target {f.opset_version} (required min iOS18)")
+                return
+
+            for op in list(f.operations):
+                if op.op_type == "scaled_dot_product_attention":
+                    self._replace_scaled_dot_product_attention(op)
+
+    @staticmethod
+    def _get_input_vars(op):
+        mandatory_params = ["query", "key", "value"]
+        inputs = {}
+        for param in mandatory_params:
+            inputs[param] = op.inputs.get(param)
+            if inputs[param] is None:
+                raise ValueError(f"operation 'scaled_dot_product_attention': mandatory input '{param}' not present")
+        return tuple([inputs[param] for param in mandatory_params]) + (op.inputs.get("attn_mask"),)
+
+    @staticmethod
+    def _split_to_chunks(seq_length: int, count: int) -> List[Tuple[int, int]]:
+        chunk_size = max(seq_length // count, 1)
+        remainder = seq_length % count
+
+        result = []
+        chunk_start = 0
+        for i in range(count):
+            if chunk_start >= seq_length:
+                break
+            chunk_end = chunk_start + chunk_size + (1 if i < remainder else 0)
+            result.append((chunk_start, chunk_end))
+            chunk_start = chunk_end
+
+        return result
+
+    def _replace_scaled_dot_product_attention(self, op):
+        q, k, v, mask = self._get_input_vars(op)
+
+        q_size = len(q.shape)
+        q_seq_length = q.shape[-2]
+        if q_seq_length < self._min_seq_length:
+            logger.debug(
+                f"skipping SDPA op, Q seq_length is {q_seq_length} (minimum seq length needed: {self._min_seq_length}"
+            )
+            return
+
+        dims = q.shape[-1]
+        normalize_factor = float(dims) ** -0.5
+
+        q_dtype = types.nptype_from_builtin(type(q.dtype()))
+
+        chunks = self._split_to_chunks(q_seq_length, self._seq_length_divider)
+
+        concat_out = None
+        with op.enclosing_block:
+            if mask is not None:
+                if mask.dtype == types.bool:
+                    cond_out = mb.logical_not(x=mask, before_op=op)
+                    mask_zeros = mb.const(val=np.zeros(mask.shape, dtype=q_dtype), before_op=op)
+                    mask_float = mb.select(cond=cond_out, a=q_dtype(-np.inf), b=mask_zeros, before_op=op)
+                else:
+                    mask_float = mask
+
+            for chunk_start, chunk_end in chunks:
+                # Get a chunk of Q.
+                slice_begin = [0] * (q_size - 2) + [chunk_start, 0]
+                slice_end = list(q.shape[:-2] + (chunk_end, dims))
+                slice_end_mask = tuple([True] * (q_size - 2) + [False, True])
+                slice_out = mb.slice_by_index(
+                    x=q,
+                    begin=slice_begin,
+                    end=slice_end,
+                    end_mask=slice_end_mask,
+                    before_op=op,
+                )
+
+                # Calculate chunk of Q x KT
+                matmul_out = mb.matmul(x=slice_out, y=k, transpose_x=False, transpose_y=True, before_op=op)
+                mul_out = mb.mul(x=matmul_out, y=np.array(normalize_factor, dtype=q_dtype), before_op=op)
+
+                # Apply the attention mask.
+                if mask is not None:
+                    if mask.shape[-2] == 1:
+                        mul_out = mb.add(x=mul_out, y=mask_float, before_op=op)
+                    else:
+                        mask_out = mb.slice_by_index(
+                            x=mask_float,
+                            begin=[chunk_start, 0],
+                            end=[chunk_end, mask.shape[-1]],
+                            end_mask=[False, True],
+                            before_op=op,
+                        )
+                        mul_out = mb.add(x=mul_out, y=mask_out, before_op=op)
+
+                # Calculate softmax of the product.
+                softmax_out = mb.softmax(x=mul_out, axis=-1, before_op=op)
+
+                # Calculate the chunk of attention.
+                matmul_v_out = mb.matmul(
+                    x=softmax_out,
+                    y=v,
+                    transpose_x=False,
+                    transpose_y=False,
+                    before_op=op,
+                )
+
+                # Add the chunk of attention to the result value.
+                concat_values = [concat_out] if concat_out is not None else []
+                concat_out = mb.concat(values=concat_values + [matmul_v_out], axis=-2, interleave=False, before_op=op)
+
+            # Remove the original SDPA operation.
+            op.enclosing_block.replace_uses_of_var_after_op(
+                anchor_op=op,
+                old_var=op.outputs[0],
+                new_var=concat_out,
+            )
+            op.enclosing_block.remove_ops([op])
diff --git a/coremltools/converters/mil/mil/passes/tests/test_passes.py b/coremltools/converters/mil/mil/passes/tests/test_passes.py
index 4a07b1627..b5fa03560 100644
--- a/coremltools/converters/mil/mil/passes/tests/test_passes.py
+++ b/coremltools/converters/mil/mil/passes/tests/test_passes.py
@@ -7,6 +7,8 @@
 import itertools
 import unittest
 
+from typing import ClassVar, Dict, List, Optional
+
 import numpy as np
 import pytest
 import torch
@@ -38,6 +40,7 @@
     get_op_types_in_program,
 )
 from coremltools.models.utils import _macos_version
+from coremltools.converters.mil.frontend.milproto.load import load as _milproto_to_pymil
 
 np.random.seed(1984)
 _VALIDATE_MODEL = True
@@ -7371,3 +7374,133 @@ def prog(x, y, z):
 
         apply_pass_and_basic_check(prog, "common::fuse_stack_split")
         assert get_op_types_in_program(prog) == ["stack", "split"] + ["squeeze"] * 3
+
+
+class TestScaledDotProductAttentionSlicedQ:
+
+    class AttentionPyTorch(torch.nn.Module):
+        @staticmethod
+        def forward(q, k, v, attn_mask=None):
+            return torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask)
+
+    @staticmethod
+    def _get_example_inputs(
+        shape_size: int = 3,
+        qkv_same_shape: bool = True,
+        dtype: torch.dtype = torch.float16,
+        attn_mask_dtype: Optional[torch.dtype] = None,
+    ):
+        batches, seq_length, dimensions = 4, 256, 768
+        q_shape = (batches, seq_length, dimensions)
+        kv_shape = q_shape if qkv_same_shape else (batches, seq_length - 16, dimensions)
+        if shape_size > 3:
+            q_shape = tuple([1] * (shape_size - len(q_shape)) + list(q_shape))
+            kv_shape = tuple([1] * (shape_size - len(kv_shape)) + list(kv_shape))
+        inputs = {
+            "q": torch.rand(q_shape, dtype=dtype),
+            "k": torch.rand(kv_shape, dtype=dtype),
+            "v": torch.rand(kv_shape, dtype=dtype),
+        }
+        if attn_mask_dtype is not None:
+            if attn_mask_dtype == torch.bool:
+                inputs["attn_mask"] = torch.randint(0, 2, (seq_length, seq_length), dtype=torch.bool)
+            else:
+                inputs["attn_mask"] = torch.randn((seq_length, seq_length), dtype=dtype)
+        return inputs
+
+    @staticmethod
+    def _get_trace_coreml_inputs(example_inputs: Dict[str, torch.Tensor]):
+        model_inputs = [example_inputs[key] for key in ["q", "k", "v"]]
+        if "attn_mask" in example_inputs:
+            model_inputs.append(example_inputs["attn_mask"])
+
+        coreml_model_inputs = []
+        for key in ["q", "k", "v", "attn_mask"]:
+            if key in example_inputs:
+                dtype = example_inputs[key].numpy().dtype
+                if dtype == bool:
+                    dtype = np.float32
+                coreml_model_inputs.append(ct.TensorType(key, shape=example_inputs[key].shape, dtype=dtype))
+
+        return model_inputs, coreml_model_inputs
+
+    def verify_sdpa_outputs(self, example_inputs: Dict[str, torch.Tensor]):
+        pipeline_1 = ct.PassPipeline.DEFAULT
+
+        pipeline_2 = ct.PassPipeline.DEFAULT
+        pipeline_2.append_pass("common::scaled_dot_product_attention_sliced_q")
+
+        pipeline_3 = ct.PassPipeline.DEFAULT
+        pipeline_3.append_pass("common::scaled_dot_product_attention_sliced_q")
+        pipeline_3.set_options("common::scaled_dot_product_attention_sliced_q", {"min_seq_length": 256})
+
+        pipeline_4 = ct.PassPipeline.DEFAULT
+        pipeline_4.append_pass("common::scaled_dot_product_attention_sliced_q")
+        pipeline_4.set_options(
+            "common::scaled_dot_product_attention_sliced_q", {"min_seq_length": 256, "seq_length_divider": 32}
+        )
+
+        model = self.AttentionPyTorch()
+        model_inputs, coreml_model_inputs = self._get_trace_coreml_inputs(example_inputs)
+
+        coreml_models = [
+            ct.convert(
+                torch.jit.trace(model, model_inputs).eval(),
+                inputs=coreml_model_inputs,
+                minimum_deployment_target=ct.target.iOS18,
+                convert_to="mlprogram",
+                compute_units=ct.ComputeUnit.ALL,
+                skip_model_load=False,
+                pass_pipeline=pipeline,
+            )
+            for pipeline in [pipeline_1, pipeline_2, pipeline_3, pipeline_4]
+        ]
+
+        model_specs = [coreml_model.get_spec() for coreml_model in coreml_models]
+        progs = []
+        for i in range(len(coreml_models)):
+            progs.append(
+                _milproto_to_pymil(
+                    model_spec=model_specs[i],
+                    specification_version=model_specs[i].specificationVersion,
+                    file_weights_dir=coreml_models[i].weights_dir,
+                )
+            )
+
+        ops_counts = [len(prog.functions["main"].operations) for prog in progs]
+
+        assert ops_counts[0] == 1 or ops_counts[0] == 3  # (attn_mask might be cast to bool from input fp16 dtype)
+        assert ops_counts[1] == 1 or ops_counts[1] == 3  # the Q seq length is less than the default min seq length
+        assert ops_counts[2] >= 6 * 16  # 6 ops (without consts) per slice
+        assert ops_counts[3] >= 6 * 32
+
+        predict_inputs = copy.deepcopy(example_inputs)
+        if "attn_mask" in predict_inputs:
+            predict_inputs["attn_mask"] = predict_inputs["attn_mask"].to(dtype=torch.float32)
+
+        outputs = [list(coreml_model.predict(predict_inputs).values())[0] for coreml_model in coreml_models]
+
+        for i in range(1, len(outputs)):
+            assert outputs[0].shape == outputs[i].shape
+            np.testing.assert_allclose(outputs[0], outputs[i], rtol=0.01)
+
+    def test_scaled_dot_product_attention_sliced(self):
+        # Confirm the basic scenario.
+        example_inputs = self._get_example_inputs()
+        self.verify_sdpa_outputs(example_inputs)
+
+        # Confirm sdpa with Q, K and V as 4D tensors.
+        example_inputs = self._get_example_inputs(shape_size=4)
+        self.verify_sdpa_outputs(example_inputs)
+
+        # Confirm sdpa with attn_mask as a bias.
+        example_inputs = self._get_example_inputs(attn_mask_dtype=torch.float16)
+        self.verify_sdpa_outputs(example_inputs)
+
+        # Confirm sdpa with attn_mask as boolean flags.
+        example_inputs = self._get_example_inputs(attn_mask_dtype=torch.bool)
+        self.verify_sdpa_outputs(example_inputs)
+
+        # Confirm sdpa works well with different shapes for Q and K & V.
+        example_inputs = self._get_example_inputs(qkv_same_shape=False)
+        self.verify_sdpa_outputs(example_inputs)
diff --git a/docs/source/coremltools.converters.mil.mil.passes.defs.rst b/docs/source/coremltools.converters.mil.mil.passes.defs.rst
index 2c361eec7..5a9e3919d 100644
--- a/docs/source/coremltools.converters.mil.mil.passes.defs.rst
+++ b/docs/source/coremltools.converters.mil.mil.passes.defs.rst
@@ -147,3 +147,11 @@ symbol_transform
 .. automodule:: coremltools.converters.mil.mil.passes.defs.symbol_transform
 
     .. autoclass:: materialize_symbolic_shape_program
+
+
+transformer
+---------------------------------------------------------
+
+.. automodule:: coremltools.converters.mil.mil.passes.defs.transformer
+
+    .. autoclass:: scaled_dot_product_attention_sliced_q
diff --git a/reqs/test.pip b/reqs/test.pip
index 0800bc334..27fb447b0 100644
--- a/reqs/test.pip
+++ b/reqs/test.pip
@@ -53,7 +53,7 @@ pytest-timeout
 
 transformers==4.26.0; platform_machine != "arm64"
 transformers==4.38.2; platform_machine == "arm64"
-peft
+peft==0.13.2
 
 # coremltools.optimize.torch
 filelock==3.6.0