make float8 a1x128_w128x128 granularity serializeable

test/core/test_config.py

@@ -23,6 +23,7 @@
     AWQConfig,
     AWQStep,
 )
+from torchao.quantization import PerBlock
 from torchao.quantization.quant_api import (
     Float8DynamicActivationFloat8WeightConfig,
     Float8DynamicActivationInt4WeightConfig,
@@ -45,6 +46,9 @@
     Float8DynamicActivationFloat8WeightConfig(),
     Float8DynamicActivationFloat8WeightConfig(granularity=PerRow()),
     Float8DynamicActivationFloat8WeightConfig(granularity=[PerRow(), PerRow()]),
+    Float8DynamicActivationFloat8WeightConfig(
+        granularity=[PerBlock([1, 128]), PerBlock([128, 128])]
+    ),
     Float8WeightOnlyConfig(
         weight_dtype=torch.float8_e4m3fn,
     ),

test/quantization/quantize_/workflows/float8/test_float8_tensor.py

@@ -91,7 +91,7 @@ def setUp(self):
     @common_utils.parametrize("compile", [True, False])
     @common_utils.parametrize(
         "granularity",
-        [PerTensor(), PerRow(), (PerBlock((1, 128)), PerBlock((128, 128)))],
+        [PerTensor(), PerRow(), (PerBlock([1, 128]), PerBlock([128, 128]))],
     )
     @common_utils.parametrize(
         "kernel_preference",
@@ -125,7 +125,7 @@ def test_fp8_linear_variants(
             elif mode == "weight-only":
                 return unittest.skip("unimplemented")
 
-        elif granularity == (PerBlock((1, 128)), PerBlock((128, 128))):
+        elif granularity == (PerBlock([1, 128]), PerBlock([128, 128])):
             if dtype is not torch.bfloat16:
                 return unittest.skip("unimplemented")
             elif mode != "dynamic":
@@ -199,7 +199,7 @@ def test_fp8_linear_variants(
                 assert qs1.shape == (N, 1)
                 assert qs2.shape == (K, 1)
             else:
-                assert granularity == (PerBlock((1, 128)), PerBlock((128, 128)))
+                assert granularity == (PerBlock([1, 128]), PerBlock([128, 128]))
                 assert qs1.shape == (N // 128, K // 128)
                 assert qs2.shape == (K // 128, N // 128)
 

torchao/_models/llama/eval.py

@@ -173,7 +173,7 @@ def run_evaluation(
             )
         if quantization == "float8_a1x128_w128x128":
             config = Float8DynamicActivationFloat8WeightConfig(
-                granularity=(PerBlock((1, 128)), PerBlock((128, 128))),
+                granularity=(PerBlock([1, 128]), PerBlock([128, 128])),
                 activation_value_lb=1e-12,
             )
             # TODO(future): all workflows in this file should be skipping quantization

torchao/float8/inference.py

@@ -225,7 +225,7 @@ def _granularity_is_a_1_128_w_128_128(
         list[FP8Granularity],
     ],
 ) -> bool:
-    return len(g) == 2 and g[0] == PerBlock((1, 128)) and g[1] == PerBlock((128, 128))
+    return len(g) == 2 and g[0] == PerBlock([1, 128]) and g[1] == PerBlock([128, 128])
 
 
 def _normalize_granularity(

torchao/quantization/granularity.py

@@ -126,4 +126,8 @@ class PerBlock(Granularity):
     # 1. `block_size` in this class can support tensors of multiple ranks
     # 2. `block_size` in other places in the codebase has rank equal to the
     #    corresponding tensor
+    # TODO(future PR): change to list or support serialization with tuples,
+    # currently serialization only works when `block_size` is specified as a
+    # list. Example error:
+    # https://gist.github.com/vkuzo/ab4d6aec83cb98ad9417898d2c024a2c
     block_size: tuple[int, ...]

torchao/quantization/quant_api.py

@@ -1782,7 +1782,6 @@ def __post_init__(self):
                 KernelPreference.AUTO,
                 KernelPreference.TORCH,
             ), "unimplemented"
-            assert self.mm_config is None, "unimplemented"
             assert self.version >= 2, "unimplemented"
             default_use_fast_accum = False