[PyTorch] Python `GroupedTensor` (#2654)

* PyTorch-Python GroupedTensor
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Update transformer_engine/pytorch/tensor/storage/grouped_tensor.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Remove mxfp8 gq test
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Fix recipe tests and FP8 weights
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Fix device test
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

* Disable grouped weights for unsupported recipes
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>

---------
Signed-off-by: Kirthi Shankar Sivamani <ksivamani@nvidia.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

tests/pytorch/test_grouped_tensor.py

+# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+"""Tests for GroupedTensor class"""
+
+from typing import List, Tuple
+import pytest
+import torch
+import transformer_engine.pytorch as te
+from transformer_engine.pytorch.tensor.storage.grouped_tensor import GroupedTensor
+from transformer_engine.pytorch import (
+    Quantizer,
+    Float8Quantizer,
+    Float8CurrentScalingQuantizer,
+    Float8BlockQuantizer,
+    MXFP8Quantizer,
+    NVFP4Quantizer,
+)
+from transformer_engine.pytorch.constants import TE_DType_To_Torch
+import transformer_engine_torch as tex
+
+# Check available recipes
+fp8_available, reason_for_no_fp8 = te.is_fp8_available(return_reason=True)
+fp8_block_scaling_available, reason_for_no_fp8_block_scaling = te.is_fp8_block_scaling_available(
+    return_reason=True
+)
+mxfp8_available, reason_for_no_mxfp8 = te.is_mxfp8_available(return_reason=True)
+nvfp4_available, reason_for_no_nvfp4 = te.is_nvfp4_available(return_reason=True)
+
+_quantization_params = [
+    pytest.param(
+        "fp8_delayed_scaling",
+        marks=pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8),
+    ),
+    pytest.param(
+        "fp8_current_scaling",
+        marks=pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8),
+    ),
+    pytest.param(
+        "fp8_blockwise",
+        marks=pytest.mark.skipif(
+            not fp8_block_scaling_available, reason=reason_for_no_fp8_block_scaling
+        ),
+    ),
+    pytest.param(
+        "mxfp8",
+        marks=pytest.mark.skipif(not mxfp8_available, reason=reason_for_no_mxfp8),
+    ),
+    pytest.param(
+        "nvfp4",
+        marks=pytest.mark.skipif(not nvfp4_available, reason=reason_for_no_nvfp4),
+    ),
+]
+
+
+def make_quantizer(quantization: str, num_tensors: int, shape: List[Tuple[int, int]]) -> Quantizer:
+    """Create quantizers for given quantization scheme"""
+
+    if quantization == "fp8_delayed_scaling":
+        quantizer = Float8Quantizer(
+            scale=torch.ones(1, dtype=torch.float32, device="cuda"),
+            amax=torch.zeros(1, dtype=torch.float32, device="cuda"),
+            fp8_dtype=tex.DType.kFloat8E4M3,
+        )
+    elif quantization == "fp8_current_scaling":
+        quantizer = Float8CurrentScalingQuantizer(
+            fp8_dtype=tex.DType.kFloat8E4M3,
+            device="cuda",
+        )
+        quantizer.set_usage(rowwise=True, columnwise=False)
+    elif quantization == "fp8_blockwise":
+        quantizer = Float8BlockQuantizer(
+            fp8_dtype=tex.DType.kFloat8E4M3,
+            rowwise=True,
+            columnwise=False,
+            force_pow_2_scales=True,
+            amax_epsilon=0.0,
+            block_scaling_dim=1,
+        )
+    elif quantization == "mxfp8":
+        quantizer = MXFP8Quantizer(fp8_dtype=tex.DType.kFloat8E4M3)
+    elif quantization == "nvfp4":
+        quantizer = NVFP4Quantizer(
+            with_rht=False,
+            with_post_rht_amax=False,
+            with_2d_quantization=False,
+            stochastic_rounding=False,
+            with_random_sign_mask=False,
+        )
+    else:
+        raise ValueError(f"Unknown quantization scheme: {quantization}")
+
+    quantizer.internal = False
+
+    return quantizer
+
+
+def _get_rowwise_data_tensor(qtensor, quantization: str) -> torch.Tensor:
+    if quantization in ("fp8_delayed_scaling", "fp8_current_scaling"):
+        return qtensor._data
+    if quantization in ("fp8_blockwise", "mxfp8", "nvfp4"):
+        return qtensor._rowwise_data
+    raise ValueError(f"Unknown quantization scheme: {quantization}")
+
+
+def _rowwise_offset_bytes(numel: int, quantization: str) -> int:
+    if quantization == "nvfp4":
+        return numel // 2
+    return numel
+
+
+class TestGroupedTensor:
+    @staticmethod
+    def setup_class(cls) -> None:
+        # Configure RNG
+        seed = 1234
+        torch.manual_seed(seed)
+        torch.cuda.manual_seed(seed)
+
+    def test_basic_construction_all_same_shape(self) -> None:
+        """Test GroupedTensor construction with all tensors having same shape"""
+        num_tensors = 4
+        shape = [(256, 512) for _ in range(num_tensors)]
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=None,
+            device="cuda",
+            dtype=torch.float32,
+        )
+
+        assert grouped_tensor.num_tensors == num_tensors
+        assert grouped_tensor.all_same_shape()
+        assert grouped_tensor.all_same_first_dim()
+        assert grouped_tensor.all_same_last_dim()
+        assert grouped_tensor.logical_shape == (num_tensors * 256, 512)
+        assert grouped_tensor.get_common_first_dim() == 256
+        assert grouped_tensor.get_common_last_dim() == 512
+        assert grouped_tensor.has_data()
+
+    def test_basic_construction_varying_first_dim(self) -> None:
+        """Test GroupedTensor construction with varying first dimension"""
+        num_tensors = 3
+        shape = [(128, 512), (256, 512), (384, 512)]
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=None,
+            device="cuda",
+            dtype=torch.float32,
+        )
+
+        assert grouped_tensor.num_tensors == num_tensors
+        assert not grouped_tensor.all_same_shape()
+        assert not grouped_tensor.all_same_first_dim()
+        assert grouped_tensor.all_same_last_dim()
+        assert grouped_tensor.get_common_last_dim() == shape[0][1]
+        assert grouped_tensor.logical_shape == (
+            sum(v for v, _ in shape),
+            shape[0][1],
+        )  # sum of first dims
+
+    def test_split_into_quantized_tensors_no_quantization(self) -> None:
+        """Test split_into_quantized_tensors for unquantized tensors"""
+        num_tensors = 3
+        shape = [(256, 512) for _ in range(num_tensors)]
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=None,
+            device="cuda",
+            dtype=torch.float32,
+        )
+
+        # Get the original data pointer
+        original_data_ptr = grouped_tensor.data.data_ptr()
+
+        # Split into tensors
+        tensors = grouped_tensor.split_into_quantized_tensors()
+
+        assert len(tensors) == num_tensors
+
+        # Verify each tensor has correct shape and shares storage
+        for i, tensor in enumerate(tensors):
+            assert tensor.shape == shape[i]
+            assert isinstance(tensor, torch.Tensor)
+            assert not hasattr(tensor, "_data")  # Not a quantized tensor
+
+            # Verify data pointer is within the original grouped tensor storage
+            # The tensor should be a view of the original data
+            assert tensor.data_ptr() >= original_data_ptr
+
+            # Calculate expected offset
+            expected_offset = i * (shape[i][0] * shape[i][1]) * tensor.element_size()
+            assert tensor.data_ptr() == original_data_ptr + expected_offset
+
+    @pytest.mark.parametrize("quantization", _quantization_params)
+    def test_split_into_quantized_tensors_quantized(self, quantization: str) -> None:
+        """Test split_into_quantized_tensors for quantized tensors"""
+        num_tensors = 3
+        shape = [(512, 512) for _ in range(num_tensors)]
+        quantizers = make_quantizer(quantization, num_tensors, shape)
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=quantizers,
+            device="cuda",
+        )
+
+        # Get the original data pointer
+        original_data_ptr = grouped_tensor.data.data_ptr()
+
+        # Split into tensors
+        tensors = grouped_tensor.split_into_quantized_tensors()
+
+        assert len(tensors) == num_tensors
+
+        # Verify each tensor shares storage with the grouped tensor
+        for i, tensor in enumerate(tensors):
+            rowwise_data = _get_rowwise_data_tensor(tensor, quantization)
+            assert rowwise_data is not None
+            assert rowwise_data.data_ptr() >= original_data_ptr
+            numel = shape[i][0] * shape[i][1]
+            expected_offset = _rowwise_offset_bytes(i * numel, quantization)
+            assert rowwise_data.data_ptr() == original_data_ptr + expected_offset
+
+    def test_split_varying_shapes(self) -> None:
+        """Test split_into_quantized_tensors with varying shapes"""
+        num_tensors = 3
+        shape = [(128, 512), (256, 512), (384, 512)]
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=None,
+            device="cuda",
+            dtype=torch.float32,
+        )
+
+        original_data_ptr = grouped_tensor.data.data_ptr()
+        tensors = grouped_tensor.split_into_quantized_tensors()
+
+        assert len(tensors) == num_tensors
+
+        # Verify shapes and storage
+        cumulative_offset = 0
+        for i, tensor in enumerate(tensors):
+            assert tensor.shape == shape[i]
+            expected_offset = cumulative_offset * tensor.element_size()
+            assert tensor.data_ptr() == original_data_ptr + expected_offset
+            cumulative_offset += shape[i][0] * shape[i][1]
+
+    @pytest.mark.parametrize("quantization", _quantization_params)
+    def test_quantize_inplace(self, quantization: str) -> None:
+        """Test that quantize is done in-place for all recipes"""
+        num_tensors = 3
+        shape = [(512, 512) for _ in range(num_tensors)]
+        quantizers = make_quantizer(quantization, num_tensors, shape)
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=quantizers,
+            device="cuda",
+        )
+
+        # Get original data pointers before quantization
+        original_data_ptr = grouped_tensor.data.data_ptr()
+        original_scale_inv_ptr = grouped_tensor.scale_inv.data_ptr()
+        original_scale_ptr = (
+            grouped_tensor.scale.data_ptr() if grouped_tensor.scale is not None else None
+        )
+
+        # Create input tensors
+        input_tensors = [torch.randn(s, dtype=torch.float32, device="cuda") for s in shape]
+
+        # Quantize in place
+        quantized_tensors = grouped_tensor.quantize(input_tensors)
+
+        # Verify data pointers haven't changed (in-place operation)
+        assert grouped_tensor.data.data_ptr() == original_data_ptr
+        assert grouped_tensor.scale_inv.data_ptr() == original_scale_inv_ptr
+        if original_scale_ptr is not None:
+            assert grouped_tensor.scale.data_ptr() == original_scale_ptr
+
+        # Verify returned tensors point to the same storage
+        for i, qtensor in enumerate(quantized_tensors):
+            rowwise_data = _get_rowwise_data_tensor(qtensor, quantization)
+            numel = shape[i][0] * shape[i][1]
+            expected_offset = _rowwise_offset_bytes(i * numel, quantization)
+            assert rowwise_data.data_ptr() == original_data_ptr + expected_offset
+
+    @pytest.mark.parametrize("quantization", _quantization_params)
+    def test_quantize_varying_shapes(self, quantization: str) -> None:
+        """Test quantize with varying shapes"""
+        num_tensors = 3
+        shape = [(256, 512), (512, 512), (768, 512)]
+        quantizers = make_quantizer(quantization, num_tensors, shape)
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=quantizers,
+            device="cuda",
+        )
+
+        # Get original data pointers
+        original_data_ptr = grouped_tensor.data.data_ptr()
+
+        # Create input tensors with varying shapes
+        input_tensors = [torch.randn(s, dtype=torch.float32, device="cuda") for s in shape]
+
+        # Quantize in place
+        quantized_tensors = grouped_tensor.quantize(input_tensors)
+
+        # Verify data pointer hasn't changed
+        assert grouped_tensor.data.data_ptr() == original_data_ptr
+
+        # Verify each tensor points to correct location
+        cumulative_numel = 0
+        for qtensor, tensor_shape in zip(quantized_tensors, shape):
+            rowwise_data = _get_rowwise_data_tensor(qtensor, quantization)
+            expected_offset = _rowwise_offset_bytes(cumulative_numel, quantization)
+            assert rowwise_data.data_ptr() == original_data_ptr + expected_offset
+            cumulative_numel += tensor_shape[0] * tensor_shape[1]
+
+    @pytest.mark.parametrize("quantization", _quantization_params)
+    def test_static_quantize_method(self, quantization: str) -> None:
+        """Test the static quantize method"""
+        num_tensors = 3
+        shape = [(512, 512) for _ in range(num_tensors)]
+        quantizers = make_quantizer(quantization, num_tensors, shape)
+
+        # Create input tensors
+        input_tensors = [torch.randn(s, dtype=torch.float32, device="cuda") for s in shape]
+
+        # Use static quantize method
+        grouped_tensor = GroupedTensor.create_and_quantize(
+            tensors=input_tensors,
+            quantizer=quantizers,
+            device="cuda",
+        )
+
+        # Verify the grouped tensor was created correctly
+        assert grouped_tensor.num_tensors == num_tensors
+        assert grouped_tensor.has_data()
+
+        # Verify quantized_tensors were created and point to same storage
+        assert grouped_tensor.quantized_tensors is not None
+        assert len(grouped_tensor.quantized_tensors) == num_tensors
+
+        original_data_ptr = grouped_tensor.data.data_ptr()
+        for i, qtensor in enumerate(grouped_tensor.quantized_tensors):
+            rowwise_data = _get_rowwise_data_tensor(qtensor, quantization)
+            numel = shape[i][0] * shape[i][1]
+            expected_offset = _rowwise_offset_bytes(i * numel, quantization)
+            assert rowwise_data.data_ptr() == original_data_ptr + expected_offset
+
+    def test_clear(self) -> None:
+        """Test clear method"""
+        num_tensors = 3
+        shape = [(256, 512) for _ in range(num_tensors)]
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=num_tensors,
+            shape=shape,
+            quantizer=None,
+            device="cuda",
+            dtype=torch.float32,
+        )
+
+        assert grouped_tensor.has_data()
+        assert grouped_tensor.num_tensors == num_tensors
+
+        grouped_tensor.clear()
+
+        assert not grouped_tensor.has_data()
+        assert grouped_tensor.num_tensors == 0
+        assert grouped_tensor.data is None
+        assert grouped_tensor.logical_shape == (0, 0)

tests/pytorch/test_sanity.py

@@ -2,7 +2,7 @@
 #
 # See LICENSE for license information.
 
-from typing import Optional
+from typing import Optional, List
 
 import torch
 import pytest
@@ -137,6 +137,117 @@ def reset_global_fp8_state():
     FP8GlobalStateManager.reset()
 
 
+def check_grouped_tensor_pointers_helper(tensors, num_elems_in_byte=1, tensor_name="tensor"):
+    """
+    Verify that tensors are stored in contiguous memory.
+
+    Args:
+        tensors: List or iterable of tensors to check
+        num_elems_in_byte: Number of elements packed per byte (1 for normal, 2 for NVFP4)
+        tensor_name: Name to use in error messages
+    """
+    tensor_list = list(tensors)
+    if len(tensor_list) < 2:
+        return  # Nothing to check
+
+    for i in range(1, len(tensor_list)):
+        prev_tensor = tensor_list[i - 1]
+        curr_tensor = tensor_list[i]
+
+        # Calculate expected offset based on previous tensor size
+        prev_numel = prev_tensor.numel()
+        expected_offset = (prev_numel // num_elems_in_byte) * prev_tensor.element_size()
+
+        # Verify current tensor's data pointer is correctly offset
+        expected_ptr = prev_tensor.data_ptr() + expected_offset
+        actual_ptr = curr_tensor.data_ptr()
+
+        assert (
+            actual_ptr == expected_ptr
+        ), f"{tensor_name} {i} data pointer mismatch: expected {expected_ptr}, got {actual_ptr}"
+
+
+def check_grouped_tensor_pointers(
+    weights: List[torch.Tensor], fp8_recipe: Optional[recipe.Recipe] = None
+):
+    """
+    Verify that the pointers of the weights are in contiguous memory for GroupedTensor.
+    TODO(ksivaman): This check can be made way more efficient but for now leaving the brute force approach.
+    """
+
+    num_elems_in_a_data_byte = 1 if fp8_recipe is None else 2 if fp8_recipe.nvfp4() else 1
+
+    # Check data.
+    if hasattr(weights[0], "_data") and weights[0]._data is not None:
+        data_tensors = [w._data for w in weights]
+        check_grouped_tensor_pointers_helper(data_tensors, num_elems_in_byte=1, tensor_name="data")
+
+    # Check transpose.
+    if hasattr(weights[0], "_transpose") and weights[0]._transpose is not None:
+        transpose_tensors = [w._transpose for w in weights]
+        check_grouped_tensor_pointers_helper(
+            transpose_tensors, num_elems_in_byte=1, tensor_name="transpose"
+        )
+
+    # Check scale_inv.
+    if hasattr(weights[0], "_scale_inv") and weights[0]._scale_inv is not None:
+        scale_inv_tensors = [w._scale_inv for w in weights]
+        check_grouped_tensor_pointers_helper(
+            scale_inv_tensors, num_elems_in_byte=1, tensor_name="scale_inv"
+        )
+
+    # Check rowwise scale_inv.
+    if hasattr(weights[0], "_rowwise_scale_inv") and weights[0]._rowwise_scale_inv is not None:
+        scale_inv_tensors = [w._rowwise_scale_inv for w in weights]
+        check_grouped_tensor_pointers_helper(
+            scale_inv_tensors, num_elems_in_byte=1, tensor_name="rowwise_scale_inv"
+        )
+
+    # Check columnwise scale_inv.
+    if (
+        hasattr(weights[0], "_columnwise_scale_inv")
+        and weights[0]._columnwise_scale_inv is not None
+    ):
+        columnwise_scale_inv_tensors = [w._columnwise_scale_inv for w in weights]
+        check_grouped_tensor_pointers_helper(
+            columnwise_scale_inv_tensors,
+            num_elems_in_byte=1,
+            tensor_name="columnwise scale_inv",
+        )
+
+    # Check rowwise amax.
+    if hasattr(weights[0], "_rowwise_amax") and weights[0]._rowwise_amax is not None:
+        rowwise_amax_tensors = [w._rowwise_amax for w in weights]
+        check_grouped_tensor_pointers_helper(
+            rowwise_amax_tensors, num_elems_in_byte=1, tensor_name="rowwise amax"
+        )
+
+    # Check columnwise amax.
+    if hasattr(weights[0], "_columnwise_amax") and weights[0]._columnwise_amax is not None:
+        columnwise_amax_tensors = [w._columnwise_amax for w in weights]
+        check_grouped_tensor_pointers_helper(
+            columnwise_amax_tensors, num_elems_in_byte=1, tensor_name="columnwise amax"
+        )
+
+    # Check rowwise data.
+    if hasattr(weights[0], "_rowwise_data") and weights[0]._rowwise_data is not None:
+        rowwise_data_tensors = [w._rowwise_data for w in weights]
+        check_grouped_tensor_pointers_helper(
+            rowwise_data_tensors,
+            num_elems_in_byte=num_elems_in_a_data_byte,
+            tensor_name="rowwise data",
+        )
+
+    # Check columnwise data.
+    if hasattr(weights[0], "_columnwise_data") and weights[0]._columnwise_data is not None:
+        columnwise_data_tensors = [w._columnwise_data for w in weights]
+        check_grouped_tensor_pointers_helper(
+            columnwise_data_tensors,
+            num_elems_in_byte=num_elems_in_a_data_byte,
+            tensor_name="columnwise data",
+        )
+
+
 def _test_sanity_e2e_amp(block, dtype, config, fp8_recipe, skip_wgrad):
     te_inp_hidden_states = torch.randn(
         (config.max_seqlen_q, config.batch_size, config.hidden_size),
@@ -495,9 +606,18 @@ def test_sanity_grouped_linear(
     use_fp8 = fp8_recipe is not None
     with quantized_model_init(enabled=use_fp8 and fp8_model_params, recipe=fp8_recipe):
         te_grouped_linear = GroupedLinear(
-            num_gemms, config.hidden_size, ffn_hidden_size, bias=use_bias, params_dtype=dtype
+            num_gemms,
+            config.hidden_size,
+            ffn_hidden_size,
+            bias=use_bias,
+            params_dtype=dtype,
         ).cuda()
 
+    # Verify that weights are stored in contiguous GroupedTensor storage.
+    weights = [getattr(te_grouped_linear, f"weight{i}") for i in range(num_gemms)]
+    if fp8_recipe is None or not (fp8_recipe.delayed() or fp8_recipe.float8_current_scaling()):
+        check_grouped_tensor_pointers(weights, fp8_recipe)
+
     inp_hidden_states = torch.randn(
         num_tokens, config.hidden_size, dtype=dtype, requires_grad=True
     ).cuda()
@@ -956,7 +1076,13 @@ def test_replace_raw_data_for_float8tensor():
     random_bf16_data = torch.randn(fp8_tensor.shape, dtype=torch.bfloat16, device="cuda")
     fp8_quantizer.update_quantized(random_bf16_data, fp8_tensor)
 
-    attrs_to_check = ["_quantizer", "_fp8_dtype", "_scale_inv", "_transpose", "_transpose_invalid"]
+    attrs_to_check = [
+        "_quantizer",
+        "_fp8_dtype",
+        "_scale_inv",
+        "_transpose",
+        "_transpose_invalid",
+    ]
     attrs = {}
     for attr in attrs_to_check:
         attrs[attr] = getattr(fp8_tensor, attr)

transformer_engine/common/recipe/__init__.py

@@ -88,33 +88,40 @@ class Recipe:
     Base recipe class.
     """
 
-    def nvfp4(self):
+    @classmethod
+    def nvfp4(cls):
         """Whether the given recipe is NVFP4 1D block scaling."""
-        return isinstance(self, NVFP4BlockScaling)
+        return issubclass(cls, NVFP4BlockScaling)
 
-    def mxfp8(self):
+    @classmethod
+    def mxfp8(cls):
         """Whether the given recipe is MXFP8 block scaling."""
-        return isinstance(self, MXFP8BlockScaling)
+        return issubclass(cls, MXFP8BlockScaling)
 
-    def delayed(self):
+    @classmethod
+    def delayed(cls):
         """Whether the given recipe is delayed scaling."""
-        return isinstance(self, DelayedScaling)
+        return issubclass(cls, DelayedScaling)
 
-    def float8_current_scaling(self):
+    @classmethod
+    def float8_current_scaling(cls):
         """Whether the given recipe is (per-tensor) current scaling."""
-        return isinstance(self, Float8CurrentScaling)
+        return issubclass(cls, Float8CurrentScaling)
 
-    def float8_per_tensor_scaling(self):
+    @classmethod
+    def float8_per_tensor_scaling(cls):
         """Whether the given recipe is per-tensor scaling."""
-        return isinstance(self, (DelayedScaling, Float8CurrentScaling))
+        return issubclass(cls, (DelayedScaling, Float8CurrentScaling))
 
-    def float8_block_scaling(self):
+    @classmethod
+    def float8_block_scaling(cls):
         """Whether the given recipe is float8 blockwise scaling."""
-        return isinstance(self, Float8BlockScaling)
+        return issubclass(cls, Float8BlockScaling)
 
-    def custom(self):
+    @classmethod
+    def custom(cls):
         """Whether the given recipe is custom."""
-        return isinstance(self, CustomRecipe)
+        return issubclass(cls, CustomRecipe)
 
 
 @dataclass()

transformer_engine/pytorch/module/grouped_linear.py

@@ -13,6 +13,7 @@ import torch
 import transformer_engine_torch as tex
 
 from transformer_engine.common.recipe import Recipe
+from transformer_engine.pytorch.tensor.storage.grouped_tensor import GroupedTensor
 from .base import (
     get_dummy_wgrad,
     TransformerEngineBaseModule,
@@ -147,7 +148,10 @@ class _GroupedLinear(torch.autograd.Function):
             # tensors (like scales), but bulk allocation shares storage across all tensors,
             # so if scales can't be offloaded, nothing in the group can be offloaded.
             inputmats = tex.split_quantize(
-                inp_view, m_splits, input_quantizers, disable_bulk_allocation=cpu_offloading
+                inp_view,
+                m_splits,
+                input_quantizers,
+                disable_bulk_allocation=cpu_offloading,
             )
         elif debug:
             inputmats = DebugQuantizer.multi_tensor_quantize(
@@ -365,7 +369,10 @@ class _GroupedLinear(torch.autograd.Function):
                 for i in range(ctx.num_gemms):
                     grad_biases[i] = grad_output_mats[i].sum(dim=0)
                 grad_output = DebugQuantizer.multi_tensor_quantize(
-                    grad_output_view, ctx.grad_output_quantizers, ctx.m_splits, ctx.activation_dtype
+                    grad_output_view,
+                    ctx.grad_output_quantizers,
+                    ctx.m_splits,
+                    ctx.activation_dtype,
                 )
             else:
                 # Only split grad output. Grad bias is fused with
@@ -436,7 +443,8 @@ class _GroupedLinear(torch.autograd.Function):
                     if ctx.input_quantizers[0] is not None:
                         for input_quantizer in ctx.input_quantizers:
                             if isinstance(
-                                input_quantizer, (Float8Quantizer, Float8CurrentScalingQuantizer)
+                                input_quantizer,
+                                (Float8Quantizer, Float8CurrentScalingQuantizer),
                             ):
                                 input_quantizer.set_usage(rowwise=True, columnwise=True)
                             else:
@@ -446,7 +454,10 @@ class _GroupedLinear(torch.autograd.Function):
                         inputmats = tex.split_quantize(inp_view, ctx.m_splits, ctx.input_quantizers)
                     elif ctx.debug:
                         inputmats = DebugQuantizer.multi_tensor_quantize(
-                            inp_view, ctx.input_quantizers, ctx.m_splits, ctx.activation_dtype
+                            inp_view,
+                            ctx.input_quantizers,
+                            ctx.m_splits,
+                            ctx.activation_dtype,
                         )
                     else:
                         inputmats = torch.split(
@@ -616,7 +627,7 @@ class GroupedLinear(TransformerEngineBaseModule):
     ) -> None:
         super().__init__(name)
 
-        params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
+        self.params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
         self.num_gemms = num_gemms
         self.in_features = in_features
         self.out_features = out_features
@@ -631,12 +642,19 @@ class GroupedLinear(TransformerEngineBaseModule):
         assert (
             not ub_overlap_rs and not ub_overlap_ag
         ), "GroupedLinear doesn't support Userbuffer overlap."
+        self.init_method = init_method
         self.get_rng_state_tracker = get_rng_state_tracker
         self.rng_tracker_name = rng_tracker_name
 
         self.wgrad_store = WeightGradStore(delay_wgrad_compute)
 
-        self._offsets = {"input": 0, "weight": 1, "output": 2, "grad_output": 0, "grad_input": 1}
+        self._offsets = {
+            "input": 0,
+            "weight": 1,
+            "output": 2,
+            "grad_output": 0,
+            "grad_input": 1,
+        }
         self._num_fp8_tensors_per_gemm = {
             "fwd": 3,
             "bwd": 2,
@@ -678,7 +696,7 @@ class GroupedLinear(TransformerEngineBaseModule):
                         self.out_features,
                         self.in_features,
                         device=device,
-                        dtype=params_dtype,
+                        dtype=self.params_dtype,
                     ),
                 ),
                 init_fn=init_method,
@@ -694,13 +712,13 @@ class GroupedLinear(TransformerEngineBaseModule):
                         torch.empty(
                             self.out_features,
                             device=device,
-                            dtype=params_dtype,
+                            dtype=self.params_dtype,
                         ),
                     ),
                     init_fn=init_method_constant(0.0),
                 )
             else:
-                bias = torch.Tensor().to(dtype=params_dtype, device=device)
+                bias = torch.Tensor().to(dtype=self.params_dtype, device=device)
                 setattr(self, f"bias{i}", bias)
 
         if self.primary_weights_in_fp8:
@@ -724,8 +742,61 @@ class GroupedLinear(TransformerEngineBaseModule):
         if recipe.float8_current_scaling():
             self._customize_quantizers_float8_current_scaling(fwd, recipe)
 
+    def make_grouped_weights(self, defer_init=False) -> None:
+        """
+        Convert parameters into a GroupedTensor and re-register them as parameters.
+        """
+
+        if defer_init:
+            return
+
+        weight_quantizers = self._get_weight_quantizers()
+        recipe = (
+            weight_quantizers[0]._get_compatible_recipe()
+            if weight_quantizers and weight_quantizers[0] is not None
+            else None
+        )
+        if recipe is not None and (recipe.delayed() or recipe.float8_current_scaling()):
+            self.set_tensor_parallel_attributes(defer_init=defer_init)
+            return
+
+        weights = [getattr(self, f"weight{i}") for i in range(self.num_gemms)]
+
+        # Create the weight storage.
+        grouped_weights = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=self.num_gemms,
+            shape=[(self.out_features, self.in_features)] * self.num_gemms,
+            quantizer=weight_quantizers[0],
+            dtype=self.params_dtype,
+            device=weights[0].device,
+        )
+
+        # Copy existing params into storage.
+        with torch.no_grad():
+            for i in range(self.num_gemms):
+                if self.primary_weights_in_fp8:
+                    grouped_weights.quantized_tensors[i].copy_from_storage(weights[i])
+                else:
+                    grouped_weights.quantized_tensors[i].copy_(weights[i])
+
+        # Re-register the grouped weights as parameters.
+        for i in range(self.num_gemms):
+            self.register_parameter(
+                f"weight{i}",
+                torch.nn.Parameter(grouped_weights.quantized_tensors[i]),
+                init_fn=self.init_method,
+                get_rng_state_tracker=self.get_rng_state_tracker,
+                fp8_meta_index=self._offsets["weight"] + i * self._num_fp8_tensors_per_gemm["fwd"],
+            )
+
+        self.set_tensor_parallel_attributes(defer_init=defer_init)
+
     def reset_parameters(self, defer_init=False):
         super().reset_parameters(defer_init=defer_init)
+        self.make_grouped_weights(defer_init=defer_init)
+
+    def set_tensor_parallel_attributes(self, defer_init=False) -> None:
+        """Set attributes needed for TP"""
 
         if not defer_init:
             # Set parallelism attributes for linear weights
@@ -925,7 +996,7 @@ class GroupedLinear(TransformerEngineBaseModule):
 
     def _get_weight_quantizers(self) -> List[Quantizer]:
         """Get the weight quantizers of the module."""
-        if not self.fp8 and not self.fp8_calibration:
+        if not self.fp8 and not self.fp8_calibration and not self.primary_weights_in_fp8:
             return [None] * self.num_gemms
         weight_quantizers = [
             self.quantizers["scaling_fwd"][
@@ -934,7 +1005,7 @@ class GroupedLinear(TransformerEngineBaseModule):
             for i in range(self.num_gemms)
         ]
         for i in range(self.num_gemms):
-            weight_quantizers[i].internal = True
+            weight_quantizers[i].internal = not self.primary_weights_in_fp8
         return weight_quantizers
 
     def _get_quantizers(self):

transformer_engine/pytorch/quantized_tensor.py

@@ -69,7 +69,9 @@ class QuantizedTensorStorage:
             f"{self.__class__.__name__} class does not implement get_usages function"
         )
 
-    def prepare_for_saving(self) -> Tuple[list[Optional[torch.Tensor]], QuantizedTensorStorage]:
+    def prepare_for_saving(
+        self,
+    ) -> Tuple[list[Optional[torch.Tensor]], QuantizedTensorStorage]:
         """Prepare the tensor base for saving for backward"""
         raise NotImplementedError(
             f"{self.__class__.__name__} class does not implement prepare_for_saving function"
@@ -115,11 +117,18 @@ class QuantizedTensorStorage:
             warnings.warn("Quantizer is being updated, this may affect model behavior")
             self._quantizer = quantizer
 
+    def copy_from_storage(self, src: QuantizedTensorStorage) -> None:
+        """Copy data from another QuantizedTensorStorage."""
+        raise NotImplementedError(
+            f"{self.__class__.__name__} class does not implement copy_from_storage function"
+        )
+
 
 def prepare_for_saving(
     *tensors: Union[torch.Tensor, QuantizedTensorStorage],
 ) -> Tuple[
-    list[Optional[Union[torch.Tensor, torch.nn.Parameter]]], list[Optional[QuantizedTensorStorage]]
+    list[Optional[Union[torch.Tensor, torch.nn.Parameter]]],
+    list[Optional[QuantizedTensorStorage]],
 ]:
     """Prepare tensors for saving. Needed because save_for_backward accepts only
     torch.Tensor/torch.nn.Parameter types, while we want to be able to save
@@ -144,7 +153,10 @@ def restore_from_saved(
     return_saved_tensors: bool = False,
 ) -> (
     list[Optional[torch.Tensor | QuantizedTensorStorage]]
-    | tuple[list[Optional[torch.Tensor | QuantizedTensorStorage]], list[Optional[torch.Tensor]]]
+    | tuple[
+        list[Optional[torch.Tensor | QuantizedTensorStorage]],
+        list[Optional[torch.Tensor]],
+    ]
 ):
     """Recombine the tensor data and metadata during backward pass."""
     tensor_objects = []

transformer_engine/pytorch/tensor/float8_tensor.py

@@ -11,7 +11,11 @@ from torch.distributed.fsdp._fully_shard._fsdp_common import TrainingState
 import transformer_engine_torch as tex
 from transformer_engine_torch import DType as TE_DType
 
-from transformer_engine.common.recipe import DelayedScaling, Float8CurrentScaling, Recipe
+from transformer_engine.common.recipe import (
+    DelayedScaling,
+    Float8CurrentScaling,
+    Recipe,
+)
 from ..utils import canonicalize_process_group, devices_match
 from .storage.float8_tensor_storage import Float8TensorStorage, _FromFloat8Func
 from ..quantized_tensor import QuantizedTensor, Quantizer
@@ -154,6 +158,10 @@ class Float8Quantizer(Quantizer):
         amin, amax = tensor.aminmax()
         self.amax.copy_(torch.max(-amin, amax))
 
+    def get_columnwise_shape(self, rowwise_data_shape: Iterable[int]) -> Tuple[int, ...]:
+        """Calculate the shape of the columnwise data for Float8 1D blockwise quantization."""
+        return [rowwise_data_shape[-1]] + list(rowwise_data_shape[:-1])
+
     def create_tensor_from_data(
         self,
         data: torch.Tensor,
@@ -408,6 +416,10 @@ class Float8CurrentScalingQuantizer(Quantizer):
             quantizer=self,
         )
 
+    def get_columnwise_shape(self, rowwise_data_shape: Iterable[int]) -> Tuple[int, ...]:
+        """Calculate the shape of the columnwise data for Float8 1D blockwise quantization."""
+        return [rowwise_data_shape[-1]] + list(rowwise_data_shape[:-1])
+
     def onnx_quantize(self, tensor: torch.Tensor) -> QuantizedTensor:
         """Function using primitives with ONNX defined translations."""
         if tensor.dtype != torch.float32:
@@ -769,7 +781,10 @@ class Float8Tensor(Float8TensorStorage, QuantizedTensor):
                     kwargs,
                 )
             return Float8Tensor.make_like(
-                tensor, data=func_out, data_transpose=func_transposed_out, shape=func_out.shape
+                tensor,
+                data=func_out,
+                data_transpose=func_transposed_out,
+                shape=func_out.shape,
             )
 
         if func == torch.ops.aten.detach.default:

transformer_engine/pytorch/tensor/mxfp8_tensor.py

@@ -164,6 +164,49 @@ class MXFP8Quantizer(Quantizer):
         # TODO(ksivamani): No calibration needed for mxfp8?
         pass
 
+    def get_scale_shape(
+        self,
+        shape: Iterable[int],
+        columnwise: bool,
+    ) -> Tuple[int, int]:
+        """Calculate the shape of the scaling tensor for MXFP8 1D blockwise quantization.
+
+        This method determines the shape of the scaling tensor needed for blockwise quantization,
+        taking into account the input tensor shape and whether columnwise scaling is used.
+
+        Parameters
+        ----------
+        shape : Iterable[int]
+            Shape of the input tensor to be quantized
+        columnwise : bool
+            Whether to use columnwise scaling (True) or rowwise scaling (False)
+
+        Returns
+        -------
+        Tuple[int, int]
+            Shape of the scaling tensor as (outer_dim, inner_dim)
+            For MXFP8 1D blockwise quantization, blocksize is 32
+        Swizzle kernel will be performed before GEMM to suit the need of CuBLAS.
+        CuBLAS doc: https://docs.nvidia.com/cuda/cublas/index.html#d-block-scaling-factors-layout
+        """
+        if columnwise:
+            # Columnwise: scale_inv shape is [prod(shape[:-1]) // BLOCK_SIZE, shape[-1]]
+            # with padding to multiples of [4, 128]
+            return (
+                round_up_to_nearest_multiple(math.prod(shape[:-1]) // MXFP8_BLOCK_SCALING_SIZE, 4),
+                round_up_to_nearest_multiple(shape[-1], 128),
+            )
+        # Rowwise: scale_inv shape is [prod(shape[:-1]), shape[-1] // BLOCK_SIZE]
+        # with padding to multiples of [128, 4]
+        return (
+            round_up_to_nearest_multiple(math.prod(shape[:-1]), 128),
+            round_up_to_nearest_multiple(shape[-1] // MXFP8_BLOCK_SCALING_SIZE, 4),
+        )
+
+    def get_columnwise_shape(self, rowwise_data_shape: Tuple[int, ...]) -> Tuple[int, ...]:
+        """Calculate the shape of the columnwise data for MXFP8 1D blockwise quantization."""
+        return rowwise_data_shape
+
     def create_tensor_from_data(
         self,
         data: torch.Tensor,
@@ -704,7 +747,7 @@ class MXFP8Tensor(MXFP8TensorStorage, QuantizedTensor):
                 columnwise_scale_inv=columnwise_scale_inv,
                 fp8_dtype=fp8_dtype,
                 dtype=param_dtype,
-                shape=rowwise_data.shape if rowwise_data is not None else columnwise_data.shape,
+                shape=(rowwise_data.shape if rowwise_data is not None else columnwise_data.shape),
                 quantizer=self._quantizer,
                 with_gemm_swizzled_scales=False,
             )

transformer_engine/pytorch/tensor/nvfp4_tensor.py

@@ -341,7 +341,10 @@ class NVFP4Quantizer(Quantizer):
             )
             columnwise_scale_shape = self.get_scale_shape(shape, columnwise=True)
             columnwise_scale_inv = torch.empty(
-                columnwise_scale_shape, dtype=torch.uint8, device=device, pin_memory=pin_memory
+                columnwise_scale_shape,
+                dtype=torch.uint8,
+                device=device,
+                pin_memory=pin_memory,
             )
             amax_columnwise = torch.zeros(
                 1, dtype=torch.float32, device=device, pin_memory=pin_memory

transformer_engine/pytorch/tensor/storage/__init__.py

@@ -7,3 +7,4 @@ from .float8_tensor_storage import Float8TensorStorage  # noqa: F401
 from .mxfp8_tensor_storage import MXFP8TensorStorage  # noqa: F401
 from .float8_blockwise_tensor_storage import Float8BlockwiseQTensorStorage  # noqa: F401
 from .nvfp4_tensor_storage import NVFP4TensorStorage  # noqa: F401
+from .grouped_tensor import GroupedTensor  # noqa: F401

transformer_engine/pytorch/tensor/storage/float8_blockwise_tensor_storage.py

@@ -73,6 +73,24 @@ class Float8BlockwiseQTensorStorage(QuantizedTensorStorage):
             if t is not None:
                 t.data = _empty_tensor()
 
+    def copy_from_storage(self, src: QuantizedTensorStorage) -> None:
+        """Copy data buffers from another Float8BlockwiseQTensorStorage."""
+        if not isinstance(src, Float8BlockwiseQTensorStorage):
+            raise TypeError("copy_from_storage expects Float8BlockwiseQTensorStorage")
+        if self._fp8_dtype != src._fp8_dtype:
+            raise RuntimeError("FP8 dtype mismatch in copy_from_storage")
+        if self._is_2D_scaled != src._is_2D_scaled:
+            raise RuntimeError("Scale layout mismatch in copy_from_storage")
+
+        def _copy_optional(dst: Optional[torch.Tensor], src_tensor: Optional[torch.Tensor]):
+            if dst is not None and src_tensor is not None:
+                dst.copy_(src_tensor)
+
+        _copy_optional(self._rowwise_data, src._rowwise_data)
+        _copy_optional(self._columnwise_data, src._columnwise_data)
+        _copy_optional(self._rowwise_scale_inv, src._rowwise_scale_inv)
+        _copy_optional(self._columnwise_scale_inv, src._columnwise_scale_inv)
+
     def get_metadata(self) -> Dict[str, Any]:
         """Get this tensor's metadata."""
         return {

transformer_engine/pytorch/tensor/storage/float8_tensor_storage.py

@@ -104,6 +104,24 @@ class Float8TensorStorage(QuantizedTensorStorage):
                 t.data = _empty_tensor()
         self._transpose_invalid = True
 
+    def copy_from_storage(self, src: QuantizedTensorStorage) -> None:
+        """Copy data buffers from another Float8TensorStorage."""
+        if not isinstance(src, Float8TensorStorage):
+            raise TypeError("copy_from_storage expects Float8TensorStorage")
+        if self._fp8_dtype != src._fp8_dtype:
+            raise RuntimeError("FP8 dtype mismatch in copy_from_storage")
+
+        def _copy_optional(
+            dst: Optional[torch.Tensor],
+            src_tensor: Optional[torch.Tensor],
+        ):
+            if dst is not None and src_tensor is not None:
+                dst.copy_(src_tensor)
+
+        _copy_optional(self._data, src._data)
+        _copy_optional(self._transpose, src._transpose)
+        _copy_optional(self._scale_inv, src._scale_inv)
+
     def get_metadata(self) -> Dict[str, Any]:
         """Get this tensor's metadata."""
         return {

transformer_engine/pytorch/tensor/storage/grouped_tensor.py

+# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+"""Grouped tensor class for handling collections of tensors with different shapes"""
+from __future__ import annotations
+from typing import Optional, Tuple, List, Union
+import math
+
+import torch
+
+from ...quantized_tensor import QuantizedTensorStorage, Quantizer
+
+from ..mxfp8_tensor import MXFP8Tensor
+from ..nvfp4_tensor import NVFP4Tensor
+from ..float8_tensor import Float8Tensor
+from ..float8_blockwise_tensor import Float8BlockwiseQTensor
+from .float8_tensor_storage import Float8TensorStorage
+from .mxfp8_tensor_storage import MXFP8TensorStorage
+from .float8_blockwise_tensor_storage import Float8BlockwiseQTensorStorage
+from .nvfp4_tensor_storage import NVFP4TensorStorage
+
+
+class GroupedTensor:
+    """
+    EXPERIMENTAL FEATURE AND SUBJECT TO CHANGE.
+
+    Grouped tensor is a collection of tensors with different shapes but the same dtype and scaling mode.
+
+    Shape Representation:
+    - logical_shape: 2D shape representing the conceptual layout, i.e. the shape when member tensors
+      are flattened to 2D and stacked together (REQUIRED)
+        + When all_same_shape(): [num_tensors * M, N] where each tensor is (M, N)
+        + When varying_first_dim(): [~sum_of_first_dims, N] where N is common
+        + When varying_last_dim(): [M, ~sum_of_last_dims] where M is common
+        + When varying_both_dims(): [1, total_elements] (fully flattened)
+
+    - first_dims and last_dims are OPTIONAL (None if dimension is uniform)
+        + None first_dims: all tensors have the same first dimension
+        + None last_dims: all tensors have the same last dimension
+        + Both None: all tensors have identical shapes
+        + Both set: each tensor has unique shape (first_dims[i], last_dims[i])
+
+    Data Layout:
+    - ALL data fields are stored as 1D flattened arrays (data, columnwise_data, scale_inv, etc.)
+    - logical_shape provides the conceptual 2D interpretation
+    - All data is stored on device in contiguous layout
+
+    Note: This structure is used only for combined storage of multiple tensors with the same dtype and scaling mode.
+    """
+
+    def __init__(
+        self,
+        num_tensors: int,
+        shape: List[Tuple[int, int]],
+        quantizer: Optional[Quantizer] = None,
+        dtype: Optional[torch.dtype] = None,
+        data: Optional[torch.Tensor] = None,
+        columnwise_data: Optional[torch.Tensor] = None,
+        scale_inv: Optional[torch.Tensor] = None,
+        columnwise_scale_inv: Optional[torch.Tensor] = None,
+        amax: Optional[torch.Tensor] = None,
+        columnwise_amax: Optional[torch.Tensor] = None,
+        scale: Optional[torch.Tensor] = None,
+        first_dims: Optional[torch.Tensor] = None,
+        last_dims: Optional[torch.Tensor] = None,
+        tensor_offsets: Optional[torch.Tensor] = None,
+        offsets: Optional[List[int]] = None,
+        scale_inv_offsets: Optional[List[int]] = None,
+        columnwise_scale_inv_offsets: Optional[List[int]] = None,
+        logical_shape: Optional[Tuple[int, int]] = None,
+    ) -> None:
+        """
+        Initialize a GroupedTensor.
+
+        Args:
+            num_tensors: Number of tensors in the group
+            shape: 2D shape of each tensor (len num_tensors)
+            quantizer: Quantizer for the grouped tensor
+            data: Row-wise data buffer (1D flattened)
+            columnwise_data: Column-wise data buffer (1D flattened)
+            scale_inv: Row-wise scale inverse buffer
+            columnwise_scale_inv: Column-wise scale inverse buffer
+            amax: Row-wise amax buffer
+            columnwise_amax: Column-wise amax buffer
+            scale: Scale buffer (for FP8-DS only)
+            first_dims: Device tensor of int64 array of length num_tensors (or None if uniform)
+            last_dims: Device tensor of int64 array of length num_tensors (or None if uniform)
+            tensor_offsets: Device tensor of int64 array of length num_tensors (or None if uniform)
+            offsets: Vector of integer offsets for each tensor.
+            logical_shape: 2D tuple representing conceptual shape
+        """
+        self.num_tensors = num_tensors
+        self.quantizer = quantizer
+        self.shape = shape
+        self.dtype = (
+            dtype if dtype is not None else torch.float32
+        )  # Default to float32 if not provided
+
+        # Data buffers
+        self.data = data
+        self.columnwise_data = columnwise_data
+        self.scale_inv = scale_inv
+        self.columnwise_scale_inv = columnwise_scale_inv
+        self.amax = amax
+        self.columnwise_amax = columnwise_amax
+        self.scale = scale
+
+        # For convenient indexing for python GroupedTensor API.
+        self.scale_inv_offsets = scale_inv_offsets
+        self.columnwise_scale_inv_offsets = columnwise_scale_inv_offsets
+
+        # Shape information (OPTIONAL - None if dimension is uniform across all tensors)
+        # first_dims[i] = first dimension of tensor i (None if all tensors have same first dim)
+        # last_dims[i] = last dimension of tensor i (None if all tensors have same last dim)
+        self.first_dims = (
+            first_dims  # Device pointer to int64_t array of length num_tensors (or None)
+        )
+        self.last_dims = (
+            last_dims  # Device pointer to int64_t array of length num_tensors (or None)
+        )
+
+        # Offsets for indexing into contiguous 1D layout (OPTIONAL - not needed if all_same_shape())
+        # tensor_offsets[i] = element offset to start of tensor i (cumulative sum of numel for tensors 0..i-1)
+        # Usage: tensor_i_ptr = data.data_ptr() + tensor_offsets[i] * element_size
+        # If None and all_same_shape(): offset[i] = i * M * N (where M, N are common dimensions)
+        self.tensor_offsets = (
+            tensor_offsets  # Device pointer to int64_t array of length num_tensors (or None)
+        )
+        self.offsets = offsets  # Vector of integer offsets for each tensor.
+
+        # Logical shape: conceptual 2D shape of the grouped data (REQUIRED)
+        # Represents how the 1D flattened data should be interpreted as 2D
+        # Always 2D with positive dimensions
+        self.logical_shape = logical_shape if logical_shape is not None else (0, 0)
+
+        # Hold a reference to the quantized tensors that occupy same storage as the GroupedTensor.
+        # Used as a convenience.
+        self.quantized_tensors = None
+
+    def has_data(self) -> bool:
+        """
+        Check if the tensor has row-wise data.
+
+        Returns:
+            True if data buffer is initialized, False otherwise
+        """
+        return self.data is not None
+
+    def has_columnwise_data(self) -> bool:
+        """
+        Check if the tensor has column-wise data.
+
+        Returns:
+            True if columnwise_data buffer is initialized, False otherwise
+        """
+        return self.columnwise_data is not None
+
+    def all_same_first_dim(self) -> bool:
+        """
+        Check if all tensors in the group have the same first dimension.
+
+        Returns:
+            True if first dimension is uniform across all tensors
+        """
+        return self.first_dims is None
+
+    def all_same_last_dim(self) -> bool:
+        """
+        Check if all tensors in the group have the same last dimension.
+
+        Returns:
+            True if last dimension is uniform across all tensors
+        """
+        return self.last_dims is None
+
+    def all_same_shape(self) -> bool:
+        """
+        Check if all tensors in the group have identical shapes.
+
+        Returns:
+            True if all tensors have the same shape
+        """
+        return self.first_dims is None and self.last_dims is None
+
+    def varying_both_dims(self) -> bool:
+        """
+        Check if both dimensions vary across tensors.
+
+        Returns:
+            True if both first and last dimensions vary
+        """
+        return self.first_dims is not None and self.last_dims is not None
+
+    def get_common_first_dim(self) -> int:
+        """
+        Get the common first dimension when all tensors share it.
+
+        Returns:
+            The common first dimension
+
+        Raises:
+            RuntimeError: If first dimension varies across tensors or logical_shape is not 2D
+        """
+        if not self.all_same_first_dim():
+            raise RuntimeError("First dim varies across tensors")
+        if len(self.logical_shape) != 2:
+            raise RuntimeError("Logical shape must be 2D")
+
+        if self.all_same_shape():
+            # When both dims are uniform: logical_shape = [num_tensors * M, N]
+            return self.logical_shape[0] // self.num_tensors
+        # When varying last dims but not first dim: logical_shape = [M, sum_of_last_dims]
+        return self.logical_shape[0]
+
+    def get_common_last_dim(self) -> int:
+        """
+        Get the common last dimension when all tensors share it.
+
+        Returns:
+            The common last dimension
+
+        Raises:
+            RuntimeError: If last dimension varies across tensors or logical_shape is not 2D
+        """
+        if not self.all_same_last_dim():
+            raise RuntimeError("Last dim varies across tensors")
+        if len(self.logical_shape) != 2:
+            raise RuntimeError("Logical shape must be 2D")
+
+        # For both uniform and varying first dim cases: logical_shape[1] is the common last dim
+        return self.logical_shape[1]
+
+    def get_dtype(self) -> torch.dtype:
+        """
+        Get the high precision data type of the tensor.
+
+        Returns:
+            The high precision dtype of the data buffer
+        """
+
+        return self.dtype
+
+    def clear(self) -> None:
+        """
+        Reset tensor data and clear all buffers.
+        """
+        self.data = None
+        self.columnwise_data = None
+        self.scale_inv = None
+        self.columnwise_scale_inv = None
+        self.amax = None
+        self.columnwise_amax = None
+        self.scale = None
+        self.first_dims = None
+        self.last_dims = None
+        self.tensor_offsets = None
+        self.logical_shape = (0, 0)
+        self.num_tensors = 0
+        self.quantizer = None
+        self.quantized_tensors = None
+        self.offsets = None
+        self.scale_inv_offsets = None
+        self.columnwise_scale_inv_offsets = None
+
+    def __repr__(self) -> str:
+        """String representation of the GroupedTensor."""
+        return (
+            f"GroupedTensor(num_tensors={self.num_tensors}, "
+            f"shape={self.shape}, "
+            f"logical_shape={self.logical_shape}, "
+            f"dtype={self.get_dtype()})"
+        )
+
+    def __str__(self) -> str:
+        """User-friendly string representation."""
+        shape_info = []
+        if self.all_same_shape():
+            shape_info.append("uniform shape")
+        else:
+            if not self.all_same_first_dim():
+                shape_info.append("varying first dim")
+            if not self.all_same_last_dim():
+                shape_info.append("varying last dim")
+
+        return (
+            f"GroupedTensor with {self.num_tensors} tensors "
+            f"({', '.join(shape_info) if shape_info else 'uniform'}), "
+            f"logical_shape={self.logical_shape}, "
+            f"dtype={self.get_dtype()}"
+        )
+
+    @staticmethod
+    def make_grouped_tensor_with_shapes(
+        num_tensors: int,
+        shape: List[Tuple[int, int]],
+        quantizer: Optional[Quantizer] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> GroupedTensor:
+        """
+        Create a GroupedTensor for storing multiple weight tensors of the same shape.
+
+        Args:
+            num_tensors: Number of tensors
+            shape: 2D shape of each tensor (len num_tensors)
+            quantizer: Quantizer for each tensor
+            device: Device to allocate tensors on, defaults to current cuda device
+            dtype: Data type of the tensor (for high precision case)
+
+        Returns:
+            A GroupedTensor.
+        """
+
+        # First dim
+        first_dim_list = [s[0] for s in shape]
+        uniform_first_dim = all(first_dim_list[0] == x for x in first_dim_list)
+        logical_first_dim = sum(first_dim_list)
+        if uniform_first_dim:
+            first_dims = None
+        else:
+            first_dims = torch.tensor([s[0] for s in shape], dtype=torch.int64, device=device)
+
+        # Last dim
+        last_dim_list = [s[1] for s in shape]
+        logical_last_dim = last_dim_list[0]
+        assert all(logical_last_dim == x for x in last_dim_list), "Last dims should be uniform"
+
+        return GroupedTensor.make_grouped_tensor(
+            num_tensors=num_tensors,
+            first_dims=first_dims,
+            last_dims=None,
+            logical_first_dim=logical_first_dim,
+            logical_last_dim=logical_last_dim,
+            quantizer=quantizer,
+            device=device,
+            dtype=dtype,
+        )
+
+    @staticmethod
+    def make_grouped_tensor(
+        num_tensors: int,
+        first_dims: Optional[torch.Tensor],
+        last_dims: Optional[torch.Tensor],
+        logical_first_dim: int,
+        logical_last_dim: int,
+        quantizer: Optional[Quantizer] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> GroupedTensor:
+        """
+        Create a GroupedTensor for storing multiple weight tensors of the same shape.
+
+        Args:
+            num_tensors: Number of tensors
+            first_dims: Device tensor of int64 array of length num_tensors (or None if uniform)
+            last_dims: Device tensor of int64 array of length num_tensors (or None if uniform)
+            logical_first_dim: Logical first dimension
+            logical_last_dim: Logical last dimension
+            quantizer: Quantizer for each tensor
+                       Used to figure out the recipe and what to allocate.
+            device: Device to allocate tensors on, defaults to current cuda device
+            dtype: Data type of the tensor (for high precision case)
+
+        Returns:
+            A GroupedTensor.
+        """
+
+        # Set device
+        if device is None:
+            device = torch.cuda.current_device()
+
+        # Shape patterns and validation.
+        all_same_first = first_dims is None
+        all_same_last = last_dims is None
+
+        assert all_same_last, "Last dim must be uniform for GroupedTensor"
+        assert logical_first_dim > 0, "Logical first dim must be positive for GroupedTensor"
+        assert logical_last_dim > 0, "Logical last dim must be positive for GroupedTensor"
+
+        # assert (
+        #     logical_first_dim % 128 == 0
+        # ), "Logical first dim must be divisible by 128"
+        # assert logical_last_dim % 128 == 0, "Logical last dim must be divisible by 128"
+
+        # Calculate tensor offsets (cumulative element offsets)
+        tensor_offsets = None
+        offsets = None
+        shape = []
+        if not all_same_first:
+            # Need explicit offsets for non-uniform shapes
+            # Offsets are based on number of elements and not pointers.
+            # Kernels need to calculate precise pointers based on size of elements.
+
+            # TODO(ksivaman): Single kernel + remove the host offset calculation.
+            tensor_offsets = torch.cat(
+                [
+                    torch.zeros(1, device=first_dims.device, dtype=first_dims.dtype),
+                    torch.cumsum(first_dims * logical_last_dim, dim=0),
+                ]
+            )
+            offsets = tensor_offsets.tolist()
+            first_dims_list = first_dims.tolist()
+            for i in range(num_tensors):
+                shape.append((first_dims_list[i], logical_last_dim))
+        else:
+            offsets = [
+                i * logical_first_dim * logical_last_dim // num_tensors
+                for i in range(num_tensors + 1)
+            ]
+            for i in range(num_tensors):
+                shape.append((logical_first_dim // num_tensors, logical_last_dim))
+
+        # Calculate logical shape based
+        logical_shape = (logical_first_dim, logical_last_dim)
+
+        no_quantization = quantizer is None
+
+        rowwise_usage = quantizer.rowwise_usage if not no_quantization else True
+        columnwise_usage = quantizer.columnwise_usage if not no_quantization else False
+
+        # Calculate total elements across all tensors
+        total_elements = logical_first_dim * logical_last_dim
+
+        data = None
+        columnwise_data = None
+        scale_inv = None
+        columnwise_scale_inv = None
+        amax = None
+        columnwise_amax = None
+        scale = None
+        scale_inv_offsets = None
+        columnwise_scale_inv_offsets = None
+        if no_quantization:
+            assert dtype is not None, "dtype must be provided for unquantized GroupedTensor"
+            if rowwise_usage:
+                # Allocate rowwise data buffer (1D flattened, uint8)
+                data = torch.empty(total_elements, dtype=dtype, device=device)
+
+            if columnwise_usage:
+                # Allocate columnwise data buffer (1D flattened, uint8)
+                columnwise_data = torch.empty(total_elements, dtype=dtype, device=device)
+        elif quantizer._get_compatible_recipe().mxfp8():
+            if rowwise_usage:
+                # Allocate rowwise data buffer (1D flattened, uint8)
+                data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Scale inverse buffer for MXFP8 - complex shape based on block scaling
+                # For grouped tensors, we need to calculate scale_inv size for all tensors
+                total_scale_elements = 0
+                scale_inv_offsets = [0]
+                for i, s in enumerate(shape):
+                    scale_inv_shape = quantizer.get_scale_shape(s, False)
+                    scale_elements = math.prod(scale_inv_shape)
+                    total_scale_elements += scale_elements
+                    if i < num_tensors - 1:
+                        scale_inv_offsets.append(total_scale_elements)
+                scale_inv = torch.empty(total_scale_elements, dtype=torch.uint8, device=device)
+
+            if columnwise_usage:
+                # Allocate columnwise data buffer (1D flattened, uint8)
+                columnwise_data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Columnwise scale inverse buffer
+                total_columnwise_scale_elements = 0
+                columnwise_scale_inv_offsets = [0]
+                for i, s in enumerate(shape):
+                    scale_inv_shape = quantizer.get_scale_shape(s, False)
+                    columnwise_scale_elements = math.prod(scale_inv_shape)
+                    total_columnwise_scale_elements += columnwise_scale_elements
+                    if i < num_tensors - 1:
+                        columnwise_scale_inv_offsets.append(total_columnwise_scale_elements)
+                columnwise_scale_inv = torch.empty(
+                    total_columnwise_scale_elements, dtype=torch.uint8, device=device
+                )
+        elif quantizer._get_compatible_recipe().delayed():
+            if rowwise_usage:
+                # Allocate rowwise data buffer (1D flattened, uint8)
+                data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Scale inverse - one per tensor
+                scale_inv = torch.empty(num_tensors, dtype=torch.float32, device=device)
+                # One scale per tensor, so offsets are simply 0, 1, 2, ..., num_tensors-1
+                scale_inv_offsets = list(range(num_tensors))
+
+            if columnwise_usage:
+                # Allocate columnwise data buffer (1D flattened, uint8)
+                columnwise_data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Columnwise scale inverse - one per tensor
+                columnwise_scale_inv = torch.empty(num_tensors, dtype=torch.float32, device=device)
+                # One scale per tensor, so offsets are simply 0, 1, 2, ..., num_tensors-1
+                columnwise_scale_inv_offsets = list(range(num_tensors))
+
+            # Amax buffer for delayed scaling - one per tensor
+            amax = torch.empty(num_tensors, dtype=torch.float32, device=device)
+        elif quantizer._get_compatible_recipe().nvfp4():
+
+            if rowwise_usage:
+                # Allocate rowwise data buffer (1D flattened, uint8, but FP4 packs 2 values per byte)
+                data = torch.empty((total_elements) // 2, dtype=torch.uint8, device=device)
+                # Scale inverse buffer for NVFP4 - complex shape based on block scaling
+                # For simplicity, calculate total scale elements needed
+                total_scale_elements = 0
+                scale_inv_offsets = [0]
+                for i, s in enumerate(shape):
+                    scale_inv_shape = quantizer.get_scale_shape(s, False)
+                    total_scale_elements += math.prod(scale_inv_shape)
+                    if i < num_tensors - 1:
+                        scale_inv_offsets.append(total_scale_elements)
+                scale_inv = torch.empty(total_scale_elements, dtype=torch.uint8, device=device)
+                # Amax buffer - one per tensor
+                amax = torch.empty(num_tensors, dtype=torch.float32, device=device)
+
+            if columnwise_usage:
+                # Allocate columnwise data buffer (1D flattened, uint8, FP4 packed)
+                columnwise_data = torch.empty(
+                    (total_elements) // 2, dtype=torch.uint8, device=device
+                )
+                # Columnwise scale inverse buffer
+                total_columnwise_scale_elements = 0
+                columnwise_scale_inv_offsets = [0]
+                for i, s in enumerate(shape):
+                    columnwise_scale_inv_shape = quantizer.get_scale_shape(s, True)
+                    total_columnwise_scale_elements += math.prod(columnwise_scale_inv_shape)
+                    if i < num_tensors - 1:
+                        columnwise_scale_inv_offsets.append(total_columnwise_scale_elements)
+                columnwise_scale_inv = torch.empty(
+                    total_columnwise_scale_elements, dtype=torch.uint8, device=device
+                )
+                # Columnwise amax buffer - one per tensor
+                columnwise_amax = torch.empty(num_tensors, dtype=torch.float32, device=device)
+        elif quantizer._get_compatible_recipe().float8_block_scaling():
+            if rowwise_usage:
+                # Allocate rowwise data buffer (1D flattened, uint8)
+                data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Scale inverse - size depends on block configuration
+                # For simplicity, calculate total scale elements needed
+                total_scale_elements = 0
+                scale_inv_offsets = [0]
+                for i, s in enumerate(shape):
+                    scale_inv_shape = quantizer.get_scale_shape(s, False)
+                    total_scale_elements += math.prod(scale_inv_shape)
+                    if i < num_tensors - 1:
+                        scale_inv_offsets.append(total_scale_elements)
+                scale_inv = torch.empty(total_scale_elements, dtype=torch.float32, device=device)
+
+            if columnwise_usage:
+                # Allocate columnwise data buffer (1D flattened, uint8)
+                columnwise_data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Columnwise scale inverse
+                total_columnwise_scale_elements = 0
+                columnwise_scale_inv_offsets = [0]
+                for i, s in enumerate(shape):
+                    columnwise_scale_inv_shape = quantizer.get_scale_shape(s, True)
+                    total_columnwise_scale_elements += math.prod(columnwise_scale_inv_shape)
+                    if i < num_tensors - 1:
+                        columnwise_scale_inv_offsets.append(total_columnwise_scale_elements)
+                columnwise_scale_inv = torch.empty(
+                    total_columnwise_scale_elements, dtype=torch.float32, device=device
+                )
+        elif quantizer._get_compatible_recipe().float8_current_scaling():
+            # Current scaling - per-tensor scaling computed on the fly
+            if rowwise_usage:
+                # Allocate rowwise data buffer (1D flattened, uint8)
+                data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Scale inverse - one per tensor
+                scale_inv = torch.empty(num_tensors, dtype=torch.float32, device=device)
+                # One scale per tensor, so offsets are simply 0, 1, 2, ..., num_tensors-1
+                scale_inv_offsets = list(range(num_tensors))
+
+            if columnwise_usage:
+                # Allocate columnwise data buffer (1D flattened, uint8)
+                columnwise_data = torch.empty(total_elements, dtype=torch.uint8, device=device)
+                # Columnwise scale inverse - one per tensor
+                columnwise_scale_inv = torch.empty(num_tensors, dtype=torch.float32, device=device)
+                # One scale per tensor, so offsets are simply 0, 1, 2, ..., num_tensors-1
+                columnwise_scale_inv_offsets = list(range(num_tensors))
+
+            # Scale and amax buffers for current scaling - one per tensor
+            scale = torch.empty(num_tensors, dtype=torch.float32, device=device)
+            amax = torch.empty(num_tensors, dtype=torch.float32, device=device)
+        else:
+            raise ValueError(f"Unsupported quantizer for GroupedTensor: {quantizer}")
+
+        grouped_tensor = GroupedTensor(
+            num_tensors=num_tensors,
+            shape=shape,
+            dtype=dtype,
+            quantizer=quantizer,
+            data=data,
+            columnwise_data=columnwise_data,
+            scale_inv=scale_inv,
+            columnwise_scale_inv=columnwise_scale_inv,
+            amax=amax,
+            columnwise_amax=columnwise_amax,
+            scale=scale,
+            first_dims=first_dims,
+            last_dims=last_dims,
+            tensor_offsets=tensor_offsets,
+            offsets=offsets,
+            scale_inv_offsets=scale_inv_offsets,
+            columnwise_scale_inv_offsets=columnwise_scale_inv_offsets,
+            logical_shape=logical_shape,
+        )
+
+        grouped_tensor.quantized_tensors = grouped_tensor.split_into_quantized_tensors()
+        return grouped_tensor
+
+    def split_into_quantized_tensors(
+        self,
+    ) -> List[Union[QuantizedTensorStorage, torch.Tensor]]:
+        """
+        Split the GroupedTensor into a list of `num_tensors`
+        quantized tensors based on the quantizer. No additional memory allocation is performed,
+        so the tensors returned are the same as the ones used to create the GroupedTensor.
+
+        If quantizer is None, returns normal torch tensors.
+        If quantizer.internal is True, returns QuantizedTensorStorage.
+        Otherwise, returns QuantizedTensor.
+
+        TODO(ksivaman): Block cases where any dims are varying. This is needed only
+        to expose the weights as separate parameters.
+        """
+
+        result = []
+
+        no_quantization = self.quantizer is None
+
+        # Case 1: No quantization - return regular torch tensors
+        if no_quantization:
+            for i in range(self.num_tensors):
+                # Get tensor shape
+                tensor_shape = self.shape[i]
+
+                # Get tensor data slice
+                if self.offsets is not None:
+                    start_offset = self.offsets[i]
+                    numel = tensor_shape[0] * tensor_shape[1]
+                    end_offset = start_offset + numel
+
+                    if self.has_data():
+                        tensor_data = self.data[start_offset:end_offset].view(tensor_shape)
+                        result.append(tensor_data)
+                    elif self.has_columnwise_data():
+                        tensor_data = self.columnwise_data[start_offset:end_offset].view(
+                            tensor_shape
+                        )
+                        result.append(tensor_data)
+                    else:
+                        raise RuntimeError("GroupedTensor has no data to split")
+                else:
+                    # All same shape case
+                    numel = tensor_shape[0] * tensor_shape[1]
+                    start_offset = i * numel
+                    end_offset = start_offset + numel
+
+                    if self.has_data():
+                        tensor_data = self.data[start_offset:end_offset].view(tensor_shape)
+                        result.append(tensor_data)
+                    elif self.has_columnwise_data():
+                        tensor_data = self.columnwise_data[start_offset:end_offset].view(
+                            tensor_shape
+                        )
+                        result.append(tensor_data)
+                    else:
+                        raise RuntimeError("GroupedTensor has no data to split")
+
+            return result
+
+        # Case 2: Quantized tensors
+        recipe = self.quantizer._get_compatible_recipe()
+
+        for i in range(self.num_tensors):
+            # Get tensor shape
+            tensor_shape = self.shape[i]
+            numel = tensor_shape[0] * tensor_shape[1]
+
+            # Get data offsets
+            if self.offsets is not None:
+                data_start = self.offsets[i]
+                data_end = data_start + numel
+            else:
+                # All same shape
+                data_start = i * numel
+                data_end = data_start + numel
+
+            # Special shape handling for NVFP4.
+            nvfp4 = self.quantizer._get_compatible_recipe().nvfp4()
+            if nvfp4:
+                data_start = data_start // 2
+                data_end = data_end // 2
+
+            # Extract rowwise and columnwise data
+            rowwise_data = None
+            columnwise_data = None
+
+            if self.has_data():
+                if nvfp4:
+                    rowwise_tensor_shape = self.quantizer.convert_shape_for_fp4(tensor_shape)
+                else:
+                    rowwise_tensor_shape = tensor_shape
+                rowwise_data = self.data[data_start:data_end].view(rowwise_tensor_shape)
+
+            if self.has_columnwise_data():
+                columnwise_tensor_shape = self.quantizer.get_columnwise_shape(tensor_shape)
+                if nvfp4:
+                    columnwise_tensor_shape = self.quantizer.convert_shape_for_fp4(
+                        columnwise_tensor_shape
+                    )
+                columnwise_data = self.columnwise_data[data_start:data_end].view(
+                    columnwise_tensor_shape
+                )
+
+            # MXFP8 format
+            if recipe.mxfp8():
+                # Extract scale_inv data
+                rowwise_scale_inv = None
+                columnwise_scale_inv = None
+
+                if self.scale_inv is not None and self.scale_inv_offsets is not None:
+                    scale_start = self.scale_inv_offsets[i]
+                    if i < self.num_tensors - 1:
+                        scale_end = self.scale_inv_offsets[i + 1]
+                    else:
+                        scale_end = self.scale_inv.numel()
+
+                    # Calculate expected scale shape for MXFP8
+                    scale_shape = self.quantizer.get_scale_shape(tensor_shape, False)
+                    rowwise_scale_inv = self.scale_inv[scale_start:scale_end].view(scale_shape)
+
+                if (
+                    self.columnwise_scale_inv is not None
+                    and self.columnwise_scale_inv_offsets is not None
+                ):
+                    cscale_start = self.columnwise_scale_inv_offsets[i]
+                    if i < self.num_tensors - 1:
+                        cscale_end = self.columnwise_scale_inv_offsets[i + 1]
+                    else:
+                        cscale_end = self.columnwise_scale_inv.numel()
+
+                    cscale_shape = self.quantizer.get_scale_shape(tensor_shape, True)
+                    columnwise_scale_inv = self.columnwise_scale_inv[cscale_start:cscale_end].view(
+                        cscale_shape
+                    )
+
+                if self.quantizer.internal:
+                    mxfp8_tensor_class = MXFP8TensorStorage
+                else:
+                    mxfp8_tensor_class = MXFP8Tensor
+                tensor = mxfp8_tensor_class(
+                    shape=tensor_shape,
+                    dtype=self.dtype,
+                    rowwise_data=rowwise_data,
+                    rowwise_scale_inv=rowwise_scale_inv,
+                    columnwise_data=columnwise_data,
+                    columnwise_scale_inv=columnwise_scale_inv,
+                    fp8_dtype=self.quantizer.dtype,
+                    quantizer=self.quantizer,
+                    with_gemm_swizzled_scales=self.quantizer.optimize_for_gemm,
+                )
+                result.append(tensor)
+
+            # Delayed scaling or current scaling (both use Float8TensorStorage)
+            elif recipe.delayed() or recipe.float8_current_scaling():
+                # Scale inverse - one per tensor
+                scale_inv = None
+                if self.scale_inv is not None:
+                    scale_inv = self.scale_inv[i : i + 1]
+
+                if self.quantizer.internal:
+                    float8_tensor_class = Float8TensorStorage
+                else:
+                    float8_tensor_class = Float8Tensor
+
+                tensor = float8_tensor_class(
+                    shape=tensor_shape,
+                    dtype=self.dtype,
+                    data=rowwise_data,
+                    fp8_scale_inv=scale_inv,
+                    fp8_dtype=self.quantizer.dtype,
+                    quantizer=self.quantizer,
+                    data_transpose=columnwise_data,
+                )
+                result.append(tensor)
+
+            # Float8 block scaling
+            elif recipe.float8_block_scaling():
+                # Extract scale_inv data
+                rowwise_scale_inv = None
+                columnwise_scale_inv = None
+
+                if self.scale_inv is not None and self.scale_inv_offsets is not None:
+                    scale_start = self.scale_inv_offsets[i]
+                    if i < self.num_tensors - 1:
+                        scale_end = self.scale_inv_offsets[i + 1]
+                    else:
+                        scale_end = self.scale_inv.numel()
+
+                    # Get scale shape from quantizer
+                    scale_shape = self.quantizer.get_scale_shape(tensor_shape, False)
+                    rowwise_scale_inv = self.scale_inv[scale_start:scale_end].view(scale_shape)
+
+                if (
+                    self.columnwise_scale_inv is not None
+                    and self.columnwise_scale_inv_offsets is not None
+                ):
+                    cscale_start = self.columnwise_scale_inv_offsets[i]
+                    if i < self.num_tensors - 1:
+                        cscale_end = self.columnwise_scale_inv_offsets[i + 1]
+                    else:
+                        cscale_end = self.columnwise_scale_inv.numel()
+
+                    # Get columnwise scale shape from quantizer
+                    cscale_shape = self.quantizer.get_scale_shape(tensor_shape, True)
+                    columnwise_scale_inv = self.columnwise_scale_inv[cscale_start:cscale_end].view(
+                        cscale_shape
+                    )
+
+                # Compute is_2D_scaled and data_format from quantizer attributes
+                is_2D_scaled = self.quantizer.block_scaling_dim == 2
+
+                if self.quantizer.internal:
+                    float8_blockwise_q_tensor_class = Float8BlockwiseQTensorStorage
+                else:
+                    float8_blockwise_q_tensor_class = Float8BlockwiseQTensor
+
+                tensor = float8_blockwise_q_tensor_class(
+                    shape=tensor_shape,
+                    dtype=self.dtype,
+                    rowwise_data=rowwise_data,
+                    rowwise_scale_inv=rowwise_scale_inv,
+                    columnwise_data=columnwise_data,
+                    columnwise_scale_inv=columnwise_scale_inv,
+                    fp8_dtype=self.quantizer.dtype,
+                    quantizer=self.quantizer,
+                    is_2D_scaled=is_2D_scaled,
+                )
+                result.append(tensor)
+
+            # NVFP4 format
+            elif recipe.nvfp4():
+                # Extract scale_inv data
+                rowwise_scale_inv = None
+                columnwise_scale_inv = None
+                amax_rowwise = None
+                amax_columnwise = None
+
+                if self.scale_inv is not None and self.scale_inv_offsets is not None:
+                    scale_start = self.scale_inv_offsets[i]
+                    if i < self.num_tensors - 1:
+                        scale_end = self.scale_inv_offsets[i + 1]
+                    else:
+                        scale_end = self.scale_inv.numel()
+
+                    # Get scale shape from quantizer
+                    scale_shape = self.quantizer.get_scale_shape(tensor_shape, False)
+                    rowwise_scale_inv = self.scale_inv[scale_start:scale_end].view(scale_shape)
+
+                if (
+                    self.columnwise_scale_inv is not None
+                    and self.columnwise_scale_inv_offsets is not None
+                ):
+                    cscale_start = self.columnwise_scale_inv_offsets[i]
+                    if i < self.num_tensors - 1:
+                        cscale_end = self.columnwise_scale_inv_offsets[i + 1]
+                    else:
+                        cscale_end = self.columnwise_scale_inv.numel()
+
+                    # Get columnwise scale shape from quantizer
+                    cscale_shape = self.quantizer.get_scale_shape(tensor_shape, True)
+                    columnwise_scale_inv = self.columnwise_scale_inv[cscale_start:cscale_end].view(
+                        cscale_shape
+                    )
+
+                # Extract amax - one per tensor
+                if self.amax is not None:
+                    amax_rowwise = self.amax[i : i + 1]
+
+                if self.columnwise_amax is not None:
+                    amax_columnwise = self.columnwise_amax[i : i + 1]
+
+                if self.quantizer.internal:
+                    nvfp4_tensor_class = NVFP4TensorStorage
+                else:
+                    nvfp4_tensor_class = NVFP4Tensor
+
+                tensor = nvfp4_tensor_class(
+                    shape=tensor_shape,
+                    dtype=self.dtype,
+                    rowwise_data=rowwise_data,
+                    rowwise_scale_inv=rowwise_scale_inv,
+                    columnwise_data=columnwise_data,
+                    columnwise_scale_inv=columnwise_scale_inv,
+                    amax_rowwise=amax_rowwise,
+                    amax_columnwise=amax_columnwise,
+                    fp4_dtype=self.quantizer.dtype,
+                    quantizer=self.quantizer,
+                    with_gemm_swizzled_scales=self.quantizer.optimize_for_gemm,
+                )
+                result.append(tensor)
+
+            else:
+                raise ValueError(f"Unsupported quantization recipe: {recipe}")
+
+        return result
+
+    @staticmethod
+    def create_and_quantize(
+        tensors: int,
+        quantizer: None | Quantizer,
+        *,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+        noop_flag: Optional[torch.Tensor] = None,
+    ) -> Tuple[QuantizedTensorStorage, ...]:
+        """
+        Quantize given tensors into quantized tensors with underlying
+        storage allocated in a GroupedTensor.
+        """
+
+        grouped_tensor = GroupedTensor.make_grouped_tensor_with_shapes(
+            num_tensors=len(tensors),
+            shape=[t.shape for t in tensors],
+            quantizer=quantizer,
+            device=device,
+            dtype=dtype,
+        )
+
+        grouped_tensor.quantize(tensors, noop_flag=noop_flag)
+
+        return grouped_tensor
+
+    def quantize(
+        self,
+        tensors: List[torch.Tensor],
+        noop_flag: Optional[torch.Tensor] = None,
+    ) -> Tuple[QuantizedTensorStorage, ...]:
+        """
+        Quantize the GroupedTensor inplace.
+        """
+
+        quantized_tensors = self.split_into_quantized_tensors()
+        for i in range(self.num_tensors):
+            self.quantizer.update_quantized(tensors[i], quantized_tensors[i], noop_flag=noop_flag)
+        return quantized_tensors

transformer_engine/pytorch/tensor/storage/mxfp8_tensor_storage.py

@@ -111,6 +111,24 @@ class MXFP8TensorStorage(QuantizedTensorStorage):
             if t is not None:
                 t.data = _empty_tensor()
 
+    def copy_from_storage(self, src: QuantizedTensorStorage) -> None:
+        """Copy data buffers from another MXFP8TensorStorage."""
+        if not isinstance(src, MXFP8TensorStorage):
+            raise TypeError("copy_from_storage expects MXFP8TensorStorage")
+        if self._fp8_dtype != src._fp8_dtype:
+            raise RuntimeError("FP8 dtype mismatch in copy_from_storage")
+        if self._with_gemm_swizzled_scales != src._with_gemm_swizzled_scales:
+            raise RuntimeError("Scale layout mismatch in copy_from_storage")
+
+        def _copy_optional(dst: Optional[torch.Tensor], src_tensor: Optional[torch.Tensor]):
+            if dst is not None and src_tensor is not None:
+                dst.copy_(src_tensor)
+
+        _copy_optional(self._rowwise_data, src._rowwise_data)
+        _copy_optional(self._columnwise_data, src._columnwise_data)
+        _copy_optional(self._rowwise_scale_inv, src._rowwise_scale_inv)
+        _copy_optional(self._columnwise_scale_inv, src._columnwise_scale_inv)
+
     def get_metadata(self) -> Dict[str, Any]:
         """Get this tensor's metadata."""
         return {

transformer_engine/pytorch/tensor/storage/nvfp4_tensor_storage.py

@@ -136,6 +136,26 @@ class NVFP4TensorStorage(QuantizedTensorStorage):
             if t is not None:
                 t.data = _empty_tensor()
 
+    def copy_from_storage(self, src: QuantizedTensorStorage) -> None:
+        """Copy data buffers from another NVFP4TensorStorage."""
+        if not isinstance(src, NVFP4TensorStorage):
+            raise TypeError("copy_from_storage expects NVFP4TensorStorage")
+        if self._fp4_dtype != src._fp4_dtype:
+            raise RuntimeError("FP4 dtype mismatch in copy_from_storage")
+        if self._with_gemm_swizzled_scales != src._with_gemm_swizzled_scales:
+            raise RuntimeError("Scale layout mismatch in copy_from_storage")
+
+        def _copy_optional(dst: Optional[torch.Tensor], src_tensor: Optional[torch.Tensor]):
+            if dst is not None and src_tensor is not None:
+                dst.copy_(src_tensor)
+
+        _copy_optional(self._rowwise_data, src._rowwise_data)
+        _copy_optional(self._columnwise_data, src._columnwise_data)
+        _copy_optional(self._rowwise_scale_inv, src._rowwise_scale_inv)
+        _copy_optional(self._columnwise_scale_inv, src._columnwise_scale_inv)
+        _copy_optional(self._amax_rowwise, src._amax_rowwise)
+        _copy_optional(self._amax_columnwise, src._amax_columnwise)
+
     def get_metadata(self) -> Dict[str, Any]:
         """Get this tensor's metadata."""
         return {