[PyTorch] GroupedTensor integration

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 +606,17 @@ 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)]
+    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 +1075,13 @@ 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/CMakeLists.txt

@@ -172,10 +172,12 @@ list(APPEND transformer_engine_cuda_arch_specific_sources
      cast/cast.cu
      gemm/cutlass_grouped_gemm.cu
      hadamard_transform/group_hadamard_transform.cu
+     hadamard_transform/graph_safe_group_hadamard_transform.cu
      hadamard_transform/hadamard_transform.cu
      hadamard_transform/hadamard_transform_cast_fusion.cu
      hadamard_transform/group_hadamard_transform_cast_fusion.cu
      hadamard_transform/group_row_cast_col_hadamard_transform_cast_fusion.cu
+     hadamard_transform/graph_safe_group_row_cast_col_hadamard_transform_cast_fusion.cu
      multi_tensor/compute_scale.cu
      recipe/mxfp8_scaling.cu
      transpose/quantize_transpose_square_blockwise.cu