Merge 5ba2d28b7f into 5ac3b26a7d

QUANTIZATION.md

@@ -124,6 +124,10 @@ We define 4 possible scaling parameters that should cover most recipes in the ne
 | Format | Storage dtype | weight_scale | weight_scale_2 | pre_quant_scale | input_scale |
 |--------|---------------|--------------|----------------|-----------------|-------------|
 | float8_e4m3fn | float32 | float32 (scalar) | - | - | float32 (scalar) |
+| int8_blockwise | int8 | float32 (per-block) | - | - | - |
+
+For int8_blockwise with block_size=128 and weight shape (N, K):
+- weight_scale shape: (N//128, K//128)
 
 You can find the defined formats in `comfy/quant_ops.py` (QUANT_ALGOS).
 
@@ -131,7 +135,9 @@ You can find the defined formats in `comfy/quant_ops.py` (QUANT_ALGOS).
 
 The metadata stored alongside the checkpoint contains:
 - **format_version**: String to define a version of the standard
-- **layers**: A dictionary mapping layer names to their quantization format. The format string maps to the definitions found in `QUANT_ALGOS`. 
+- **layers**: A dictionary mapping layer names to their quantization configuration. Each layer's config is a dictionary with:
+  - **format**: Quantization format string that maps to the definitions found in `QUANT_ALGOS`
+  - **group_size** (optional): Block size for block-wise quantization schemes (e.g., int8_blockwise) 
 
 Example:
 ```json
@@ -139,9 +145,9 @@ Example:
   "_quantization_metadata": {
     "format_version": "1.0",
     "layers": {
-      "model.layers.0.mlp.up_proj": "float8_e4m3fn",
-      "model.layers.0.mlp.down_proj": "float8_e4m3fn",
-      "model.layers.1.mlp.up_proj": "float8_e4m3fn"
+      "model.layers.0.mlp.up_proj": {"format": "float8_e4m3fn"},
+      "model.layers.0.mlp.down_proj": {"format": "int8_blockwise", "group_size": 128},
+      "model.layers.1.mlp.up_proj": {"format": "int8_blockwise", "group_size": 256}
     }
   }
 }

comfy/float.py

@@ -54,6 +54,8 @@ def stochastic_rounding(value, dtype, seed=0):
         return value.to(dtype=torch.float16)
     if dtype == torch.bfloat16:
         return value.to(dtype=torch.bfloat16)
+    if dtype == torch.int8:
+        return value.to(dtype=torch.int8)
     if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
         generator = torch.Generator(device=value.device)
         generator.manual_seed(seed)

comfy/ops.py

@@ -552,12 +552,20 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     scale = state_dict.pop(weight_scale_key, None)
                     if scale is not None:
                         scale = scale.to(device)
+
+                    # Check for per-layer group_size override, otherwise use default from QUANT_ALGOS
+                    layer_config = MixedPrecisionOps._layer_quant_config[layer_name]
+                    group_size = layer_config.get("group_size", qconfig.get("group_size", None))
+
                     layout_params = {
                         'scale': scale,
                         'orig_dtype': MixedPrecisionOps._compute_dtype,
-                        'block_size': qconfig.get("group_size", None),
+                        'block_size': group_size,
                     }
 
+                    if qconfig.get("asymmetric_layout", False):
+                        layout_params['is_weight'] = True
+
                     if scale is not None:
                         manually_loaded_keys.append(weight_scale_key)