mx formats: create MXLinearConfig

test/prototype/mx_formats/test_mx_tensor.py

@@ -7,7 +7,6 @@
 import pytest
 import torch
 
-from torchao.prototype.mx_formats import config
 from torchao.prototype.mx_formats.constants import (
     DTYPE_FP4,
     DTYPE_FP6_E2M3,
@@ -139,8 +138,14 @@ def test_exponent_nan_out(elem_dtype):
     else:
         raise AssertionError("unsupported")
     block_size = 2
+    use_fp4_custom_triton_dequant_kernel = False
     tensor_mx = MXTensor(
-        scale_e8m0_bits, data_bits, elem_dtype, block_size, torch.float
+        scale_e8m0_bits,
+        data_bits,
+        elem_dtype,
+        block_size,
+        torch.float,
+        use_fp4_custom_triton_dequant_kernel,
     )
     tensor_hp = tensor_mx.to_dtype(torch.float)
     assert torch.all(torch.isnan(tensor_hp[0:1]))
@@ -188,15 +193,16 @@ def test_transpose(elem_dtype, fp4_triton):
     M, K = 128, 256
     block_size = 32
     tensor_hp = torch.randn(M, K, device="cuda", dtype=torch.bfloat16)
-    tensor_mx = MXTensor.to_mx(tensor_hp, elem_dtype, block_size)
-    config.use_fp4_custom_triton_dequant_kernel = fp4_triton
+    tensor_mx = MXTensor.to_mx(
+        tensor_hp,
+        elem_dtype,
+        block_size,
+        use_fp4_custom_triton_dequant_kernel=fp4_triton,
+    )
     tensor_mx_dq_t = tensor_mx.to_dtype(tensor_hp.dtype).t()
-    config.use_fp4_custom_triton_dequant_kernel = False
 
     tensor_mx_t = tensor_mx.t()
-    config.use_fp4_custom_triton_dequant_kernel = fp4_triton
     tensor_mx_t_dq = tensor_mx_t.to_dtype(tensor_hp.dtype)
-    config.use_fp4_custom_triton_dequant_kernel = False
 
     assert tensor_mx_dq_t.shape == tensor_mx_t_dq.shape
     torch.testing.assert_close(tensor_mx_dq_t, tensor_mx_t_dq, atol=0, rtol=0)
@@ -258,18 +264,21 @@ def test_to_mx_from_mx_compile_numerics(elem_dtype, hp_dtype, all_zeros):
 
     to_dtype_c = torch.compile(to_dtype, fullgraph=True)
 
+    use_fp4_custom_triton_dequant_kernel = False
     x_mx_dq = to_dtype(
         x_mx._data,
         x_mx._scale_e8m0,
         x_mx._elem_dtype,
         x_mx._block_size,
         hp_dtype,  # noqa: E501
+        use_fp4_custom_triton_dequant_kernel,
     )
     x_mx_c_dq = to_dtype_c(
         x_mx_c._data,
         x_mx_c._scale_e8m0,
         x_mx_c._elem_dtype,
         x_mx_c._block_size,
         hp_dtype,
+        use_fp4_custom_triton_dequant_kernel,
     )
     torch.testing.assert_close(x_mx_dq, x_mx_c_dq, atol=0, rtol=0)

torchao/prototype/mx_formats/config.py

@@ -1,2 +1,13 @@
-# If True, uses a custom triton kernel for fp4 dequantize
-use_fp4_custom_triton_dequant_kernel = False
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from dataclasses import dataclass
+
+
+@dataclass
+class MXLinearConfig:
+    # If True, uses a custom triton kernel for fp4 dequantize
+    use_fp4_custom_triton_dequant_kernel: bool = False

torchao/prototype/mx_formats/mx_linear.py

@@ -8,11 +8,12 @@
 Defines the prototype UX for converting a model to use mx weights
 """
 
-from typing import Any
+from typing import Any, Optional
 
 import torch
 import torch.nn.functional as F
 
+from torchao.prototype.mx_formats.config import MXLinearConfig
 from torchao.prototype.mx_formats.mx_tensor import MXTensor
 
 
@@ -110,13 +111,19 @@ def from_float(
         elem_dtype_weight_override=None,
         elem_dtype_grad_output_override=None,
         *,
+        # TODO(next PR): move elem_dtype* and block size into config
+        config: MXLinearConfig = None,
         block_size=32,
     ):
         mod.__class__ = MXLinear
         mod.in_elem_dtype = elem_dtype
         mod.w_elem_dtype = elem_dtype_weight_override or elem_dtype
         mod.grad_elem_dtype = elem_dtype_grad_output_override or elem_dtype
         mod.block_size = block_size
+        # TODO(next PR): fix this
+        if config is None:
+            config = MXLinearConfig()
+        mod.config = config
         return mod
 
     def forward(self, x):
@@ -151,7 +158,9 @@ class MXInferenceLinear(torch.nn.Linear):
 
     @classmethod
     @torch.no_grad()
-    def from_float(cls, mod, elem_dtype, block_size):
+    def from_float(cls, mod, elem_dtype, block_size, config: MXLinearConfig):
+        # TODO(next PR): move elem_dtype and block_size into config
+
         with torch.device("meta"):
             super_kwargs = {
                 "in_features": mod.in_features,
@@ -166,6 +175,7 @@ def from_float(cls, mod, elem_dtype, block_size):
         )
         new_mod.bias = mod.bias
         new_mod.elem_dtype = elem_dtype
+        new_mod.config = config
         return new_mod
 
     @torch.no_grad()
@@ -207,6 +217,8 @@ def swap_linear_with_mx_linear(
     elem_dtype_weight_override=None,
     elem_dtype_grad_output_override=None,
     *,
+    # TODO(next PR): move elem_dtype* and block_size into config
+    config: Optional[MXLinearConfig] = None,
     block_size=32,
     filter_fn=None,
 ):
@@ -225,6 +237,7 @@ def __fn(mod, fqn):
             elem_dtype,
             elem_dtype_weight_override,
             elem_dtype_grad_output_override,
+            config=config,
             block_size=block_size,
         ),
         combined_filter_fn,
@@ -236,6 +249,7 @@ def swap_linear_with_mx_inference_linear(
     elem_dtype,
     block_size,
     filter_fn=None,
+    config: Optional[MXLinearConfig] = None,
 ):
     if filter_fn is None:
         combined_filter_fn = _is_linear
@@ -247,6 +261,8 @@ def __fn(mod, fqn):
         combined_filter_fn = __fn
     replace_with_custom_fn_if_matches_filter(
         model,
-        lambda mod: MXInferenceLinear.from_float(mod, elem_dtype, block_size),
+        lambda mod: MXInferenceLinear.from_float(
+            mod, elem_dtype, block_size, config=config
+        ),
         combined_filter_fn,
     )

torchao/prototype/mx_formats/mx_ops.py

@@ -54,6 +54,7 @@ def mx_desugar_op(aten_op, args, kwargs=None):
         old._elem_dtype,
         old._block_size,
         old._orig_dtype,
+        old._use_fp4_custom_triton_dequant_kernel,
     )
     return new
 
@@ -82,6 +83,7 @@ def mx_t(aten_op, args, kwargs=None):
         old._elem_dtype,
         old._block_size,
         old._orig_dtype,
+        old._use_fp4_custom_triton_dequant_kernel,
     )
     return new
 
@@ -120,6 +122,7 @@ def mx_view_op(aten_op, args, kwargs=None):
         args[0]._elem_dtype,
         args[0]._block_size,
         args[0]._orig_dtype,
+        args[0]._use_fp4_custom_triton_dequant_kernel,
     )
 
 
@@ -130,7 +133,6 @@ def autocast_to_copy(aten_op, args, kwargs=None):
     tensor.
     """
     assert isinstance(args[0], MXTensor)
-    # print('before', args[0], args[0].dtype, args[0]._orig_dtype)
     assert (
         len(kwargs) == 1 and "dtype" in kwargs
     ), "Only support dtype kwarg for autocast"
@@ -144,6 +146,6 @@ def autocast_to_copy(aten_op, args, kwargs=None):
         args[0]._elem_dtype,
         args[0]._block_size,
         kwargs["dtype"],
+        args[0]._use_fp4_custom_triton_dequant_kernel,
     )
-    # print('after', res, res.dtype, res._orig_dtype)
     return res

torchao/prototype/mx_formats/mx_tensor.py

@@ -21,7 +21,6 @@
 
 import torch
 
-import torchao.prototype.mx_formats.config as config
 from torchao.prototype.mx_formats.constants import (
     BLOCK_SIZE_DEFAULT,
     DTYPE_FP4,
@@ -239,7 +238,14 @@ def get_fp_scale(scale_e8m0):
     return s_fp
 
 
-def to_dtype(data_lp, scale_e8m0, elem_dtype, block_size, target_dtype):
+def to_dtype(
+    data_lp,
+    scale_e8m0,
+    elem_dtype,
+    block_size,
+    target_dtype,
+    use_fp4_custom_triton_dequant_kernel,
+):
     orig_shape = data_lp.shape
     is_transposed = not data_lp.is_contiguous()
     # if the underlying data is transposed, convert to row major before
@@ -258,7 +264,7 @@ def to_dtype(data_lp, scale_e8m0, elem_dtype, block_size, target_dtype):
         data_hp = f6_e3m2_unpacked_to_f32(data_lp)
         data_hp = data_hp.to(target_dtype)
     elif elem_dtype == DTYPE_FP4:
-        if config.use_fp4_custom_triton_dequant_kernel:
+        if use_fp4_custom_triton_dequant_kernel:
             data_hp_rescaled = triton_f4_to_scaled_bf16(
                 data_lp,
                 scale_e8m0,
@@ -318,17 +324,29 @@ class ToMXConstrFunc(torch.autograd.Function):
     """
 
     @staticmethod
-    def forward(ctx, data_hp, elem_dtype, block_size, scaling_mode):
+    def forward(
+        ctx,
+        data_hp,
+        elem_dtype,
+        block_size,
+        scaling_mode,
+        use_fp4_custom_triton_dequant_kernel,
+    ):
         scale_e8m0_biased, data_lp = to_mx(
             data_hp, elem_dtype, block_size, scaling_mode
         )
         return MXTensor(
-            scale_e8m0_biased, data_lp, elem_dtype, block_size, data_hp.dtype
+            scale_e8m0_biased,
+            data_lp,
+            elem_dtype,
+            block_size,
+            data_hp.dtype,
+            use_fp4_custom_triton_dequant_kernel,
         )
 
     @staticmethod
     def backward(ctx, g):
-        return g, None, None, None
+        return g, None, None, None, None
 
 
 @torch._dynamo.allow_in_graph
@@ -345,6 +363,7 @@ def forward(ctx, tensor_lp, target_dtype):
             tensor_lp._elem_dtype,
             tensor_lp._block_size,
             target_dtype,
+            tensor_lp._use_fp4_custom_triton_dequant_kernel,
         )
 
     @staticmethod
@@ -360,6 +379,7 @@ def __new__(
         elem_dtype,
         block_size,
         orig_dtype,
+        use_fp4_custom_triton_dequant_kernel,
     ):
         new_size = data_bits.size()
         if elem_dtype == DTYPE_FP4:
@@ -417,6 +437,9 @@ def __new__(
         self._elem_dtype = elem_dtype
         self._block_size = block_size
         self._orig_dtype = orig_dtype
+        self._use_fp4_custom_triton_dequant_kernel = (
+            use_fp4_custom_triton_dequant_kernel
+        )
         return self
 
     def __repr__(self):
@@ -443,14 +466,22 @@ def to_mx(
         elem_dtype: Union[torch.dtype, str],
         block_size: int = BLOCK_SIZE_DEFAULT,
         scaling_mode: ScaleCalculationMode = ScaleCalculationMode.FLOOR,
+        use_fp4_custom_triton_dequant_kernel: bool = False,
     ):
-        return ToMXConstrFunc.apply(data_hp, elem_dtype, block_size, scaling_mode)
+        return ToMXConstrFunc.apply(
+            data_hp,
+            elem_dtype,
+            block_size,
+            scaling_mode,
+            use_fp4_custom_triton_dequant_kernel,
+        )
 
     def __tensor_flatten__(self):
         ctx = {
             "_elem_dtype": self._elem_dtype,
             "_block_size": self._block_size,
             "_orig_dtype": self._orig_dtype,
+            "_use_fp4_custom_triton_dequant_kernel": self._use_fp4_custom_triton_dequant_kernel,
         }
         return ["_scale_e8m0", "_data"], ctx
 
@@ -467,6 +498,7 @@ def __tensor_unflatten__(
             metadata["_elem_dtype"],
             metadata["_block_size"],
             metadata["_orig_dtype"],
+            metadata["_use_fp4_custom_triton_dequant_kernel"],
         )
 
     # Do not force the MXTensor type on the returned tensor