MX: move block_size and elem_dtype into MXLinearConfig

test/prototype/mx_formats/test_mx_linear.py

@@ -11,6 +11,7 @@
 import torch
 import torch.nn as nn
 
+from torchao.prototype.mx_formats.config import MXLinearConfig
 from torchao.prototype.mx_formats.constants import SUPPORTED_ELEM_DTYPES
 from torchao.prototype.mx_formats.mx_linear import (
     MXInferenceLinear,
@@ -59,8 +60,13 @@ def test_linear_eager(elem_dtype, bias, input_shape):
         nn.Linear(8, 6, bias=bias, device="cuda"),
     )
     m_mx = copy.deepcopy(m)
-    block_size = 2
-    swap_linear_with_mx_linear(m_mx, *elem_dtype, block_size=block_size)
+    config = MXLinearConfig(
+        block_size=2,
+        elem_dtype=elem_dtype[0],
+        elem_dtype_weight_override=elem_dtype[1],
+        elem_dtype_grad_output_override=elem_dtype[2],
+    )
+    swap_linear_with_mx_linear(m_mx, config=config)
 
     x_ref = torch.randn(*input_shape, device="cuda").requires_grad_()
     x = copy.deepcopy(x_ref)
@@ -97,8 +103,8 @@ def test_activation_checkpointing():
         nn.Linear(4, 6, bias=True, device="cuda"),
         nn.Linear(6, 6, bias=True, device="cuda"),
     )
-    block_size = 2
-    swap_linear_with_mx_linear(m, elem_dtype, block_size=block_size)
+    config = MXLinearConfig(block_size=2, elem_dtype=elem_dtype)
+    swap_linear_with_mx_linear(m, config=config)
 
     x = torch.randn(*input_shape, device="cuda").requires_grad_()
     g = torch.randn(*grad_shape, device="cuda")
@@ -133,8 +139,8 @@ def test_linear_compile(elem_dtype, bias, use_autocast):
     m_mx = nn.Sequential(
         nn.Linear(K, N, bias=bias, device="cuda"),
     )
-    block_size = 2
-    swap_linear_with_mx_linear(m_mx, elem_dtype, block_size=block_size)
+    config = MXLinearConfig(block_size=2, elem_dtype=elem_dtype)
+    swap_linear_with_mx_linear(m_mx, config=config)
     m_mx_c = copy.deepcopy(m_mx)
     m_mx_c = torch.compile(m_mx_c, fullgraph=True, backend="inductor")
 
@@ -181,8 +187,8 @@ def test_inference_linear(elem_dtype, bias, input_shape):
     m = nn.Sequential(nn.Linear(4, 6, bias=bias, dtype=torch.bfloat16))
     m = m.cuda()
     m_mx = copy.deepcopy(m)
-    block_size = 2
-    swap_linear_with_mx_inference_linear(m_mx, elem_dtype, block_size)
+    config = MXLinearConfig(block_size=2, elem_dtype=elem_dtype)
+    swap_linear_with_mx_inference_linear(m_mx, config=config)
 
     x = torch.randn(*input_shape, device="cuda", dtype=torch.bfloat16)
     y_ref = m(x)
@@ -209,8 +215,8 @@ def test_inference_compile_simple(elem_dtype):
     m = nn.Sequential(nn.Linear(4, 6, bias=False, dtype=torch.bfloat16))
     m = m.cuda()
     m_mx = copy.deepcopy(m)
-    block_size = 2
-    swap_linear_with_mx_inference_linear(m_mx, elem_dtype, block_size)
+    config = MXLinearConfig(block_size=2, elem_dtype=elem_dtype)
+    swap_linear_with_mx_inference_linear(m_mx, config=config)
     m_mx = torch.compile(m_mx, fullgraph="true")
 
     x = torch.randn(2, 4, device="cuda", dtype=torch.bfloat16)
@@ -223,20 +229,6 @@ def test_inference_compile_simple(elem_dtype):
         assert sqnr >= 13.5
 
 
-def test_mx_linear_input_weight_gradient_dtypes():
-    m = nn.Sequential(nn.Linear(32, 32))
-    swap_linear_with_mx_linear(m, *SUPPORTED_ELEM_DTYPES[:3], block_size=32)
-    assert m[0].in_elem_dtype == SUPPORTED_ELEM_DTYPES[0]
-    assert m[0].w_elem_dtype == SUPPORTED_ELEM_DTYPES[1]
-    assert m[0].grad_elem_dtype == SUPPORTED_ELEM_DTYPES[2]
-
-    m = nn.Sequential(nn.Linear(32, 32))
-    swap_linear_with_mx_linear(m, torch.float8_e4m3fn, block_size=32)
-    assert m[0].in_elem_dtype == torch.float8_e4m3fn
-    assert m[0].w_elem_dtype == torch.float8_e4m3fn
-    assert m[0].grad_elem_dtype == torch.float8_e4m3fn
-
-
 def test_filter_fn():
     m1 = nn.Sequential(
         nn.Linear(32, 32),
@@ -245,12 +237,11 @@ def test_filter_fn():
     m2 = copy.deepcopy(m1)
     filter_fn = lambda mod, fqn: fqn != "1"  # noqa: E731
 
-    swap_linear_with_mx_linear(
-        m1, torch.float8_e4m3fn, block_size=32, filter_fn=filter_fn
-    )
+    config = MXLinearConfig(block_size=32)
+    swap_linear_with_mx_linear(m1, config=config, filter_fn=filter_fn)
     assert type(m1[0]) == MXLinear
     assert type(m1[1]) == torch.nn.Linear
 
-    swap_linear_with_mx_inference_linear(m2, torch.float8_e4m3fn, 32, filter_fn)  # noqa: E501
+    swap_linear_with_mx_inference_linear(m2, config=config, filter_fn=filter_fn)  # noqa: E501
     assert type(m2[0]) == MXInferenceLinear
     assert type(m2[1]) == torch.nn.Linear

torchao/prototype/mx_formats/README.md

@@ -41,10 +41,11 @@ This is a module to do MX training, the MX matmul is currently emulated.
 
 ```python
 from torchao.prototype.mx_formats.mx_linear import swap_linear_with_mx_linear
+from torchao.prototype.mx_formats.config import MXLinearConfig
 
 m = torch.nn.Sequential(torch.nn.Linear(32, 32)).cuda()
-elem_dtype = torch.float8_e4m3fn
-swap_linear_with_mx_linear(m, elem_dtype, block_size=32)
+config = MXLinearConfig(elem_dtype=torch.float8_e4m3fn, block_size=32)
+swap_linear_with_mx_linear(m, config=config)
 
 # training loop (not shown)
 ```
@@ -55,11 +56,11 @@ This is a module to do MX inference, weights are in MX and matmul is in high pre
 
 ```python
 from torchao.prototype.mx_formats.mx_linear import swap_linear_with_mx_inference_linear
+from torchao.prototype.mx_formats.config import MXLinearConfig
 
 m = torch.nn.Sequential(torch.nn.Linear(32, 32)).cuda()
-elem_dtype = torch.float8_e4m3fn
-block_size = 32
-swap_linear_with_mx_inference_linear(m, elem_dtype, block_size)
+config = MXLinearConfig(elem_dtype=torch.float8_e4m3fn, block_size=32)
+swap_linear_with_mx_inference_linear(m, config=config)
 
 # do inference (not shown)
 ```

torchao/prototype/mx_formats/config.py

@@ -5,9 +5,40 @@
 # LICENSE file in the root directory of this source tree.
 
 from dataclasses import dataclass
+from typing import Any, Optional
+
+import torch
+
+from torchao.prototype.mx_formats.constants import SUPPORTED_ELEM_DTYPES
 
 
 @dataclass
 class MXLinearConfig:
+    # block size for scaling, default is 32 to match
+    # https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf,
+    # section 5.2
+    block_size: int = 32
+
+    # element dtype, used for activations, weights and gradients
+    elem_dtype: Any = torch.float8_e4m3fn
+
+    # overrides for element dtype for weights and gradients
+    # TODO(future PR): refactor to make this cleaner
+    elem_dtype_weight_override: Optional[Any] = None
+    elem_dtype_grad_output_override: Optional[Any] = None
+
     # If True, uses a custom triton kernel for fp4 dequantize
     use_fp4_custom_triton_dequant_kernel: bool = False
+
+    def __post_init__(self):
+        assert (
+            self.elem_dtype in SUPPORTED_ELEM_DTYPES
+        ), f"elem_dtype: expected one of {SUPPORTED_ELEM_DTYPES}, got {self.elem_dtype}"
+        if self.elem_dtype_weight_override is not None:
+            assert (
+                self.elem_dtype_weight_override in SUPPORTED_ELEM_DTYPES
+            ), f"elem_dtype_weight_override: expected one of {SUPPORTED_ELEM_DTYPES}, got {self.elem_dtype}"
+        if self.elem_dtype_grad_output_override is not None:
+            assert (
+                self.elem_dtype_grad_output_override in SUPPORTED_ELEM_DTYPES
+            ), f"elem_dtype_grad_output_override: expected one of {SUPPORTED_ELEM_DTYPES}, got {self.elem_dtype}"

torchao/prototype/mx_formats/mx_linear.py

@@ -107,22 +107,11 @@ class MXLinear(torch.nn.Linear):
     def from_float(
         cls,
         mod,
-        elem_dtype,
-        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,
+        config: Optional[MXLinearConfig] = MXLinearConfig(),
     ):
+        # TODO(before land): remove this
+        assert isinstance(config, MXLinearConfig)
         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
 
@@ -135,13 +124,14 @@ def forward(self, x):
         else:
             w = self.weight
 
+        config = self.config
         y = mx_mm.apply(
             x,
             w,
-            self.in_elem_dtype,
-            self.w_elem_dtype,
-            self.grad_elem_dtype,
-            self.block_size,
+            config.elem_dtype,
+            config.elem_dtype_weight_override or config.elem_dtype,
+            config.elem_dtype_grad_output_override or config.elem_dtype,
+            config.block_size,
         )
         if self.bias is not None:
             y = y + self.bias
@@ -158,9 +148,11 @@ class MXInferenceLinear(torch.nn.Linear):
 
     @classmethod
     @torch.no_grad()
-    def from_float(cls, mod, elem_dtype, block_size, config: MXLinearConfig):
-        # TODO(next PR): move elem_dtype and block_size into config
-
+    def from_float(
+        cls,
+        mod,
+        config: Optional[MXLinearConfig] = MXLinearConfig(),
+    ):
         with torch.device("meta"):
             super_kwargs = {
                 "in_features": mod.in_features,
@@ -171,10 +163,9 @@ def from_float(cls, mod, elem_dtype, block_size, config: MXLinearConfig):
         # TODO(future PR): set to new_mod.weight directly, will need to work
         # through some errors
         new_mod.weight_mx = MXTensor.to_mx(
-            mod.weight, elem_dtype, block_size=block_size
+            mod.weight, config.elem_dtype, block_size=config.block_size
         )
         new_mod.bias = mod.bias
-        new_mod.elem_dtype = elem_dtype
         new_mod.config = config
         return new_mod
 
@@ -213,13 +204,8 @@ def _is_linear(mod, fqn):
 
 def swap_linear_with_mx_linear(
     model,
-    elem_dtype,
-    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,
 ):
     if filter_fn is None:
@@ -232,24 +218,16 @@ def __fn(mod, fqn):
         combined_filter_fn = __fn
     replace_with_custom_fn_if_matches_filter(
         model,
-        lambda mod: MXLinear.from_float(
-            mod,
-            elem_dtype,
-            elem_dtype_weight_override,
-            elem_dtype_grad_output_override,
-            config=config,
-            block_size=block_size,
-        ),
+        lambda mod: MXLinear.from_float(mod, config=config),
         combined_filter_fn,
     )
 
 
 def swap_linear_with_mx_inference_linear(
     model,
-    elem_dtype,
-    block_size,
-    filter_fn=None,
+    *,
     config: Optional[MXLinearConfig] = None,
+    filter_fn=None,
 ):
     if filter_fn is None:
         combined_filter_fn = _is_linear
@@ -261,8 +239,6 @@ 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, config=config
-        ),
+        lambda mod: MXInferenceLinear.from_float(mod, config=config),
         combined_filter_fn,
     )