GGUF Q4 works

model_loading.py

@@ -206,7 +206,7 @@ class DownloadAndLoadCogVideoModel:
         if fp8_transformer == "enabled" or fp8_transformer == "fastmode":
             params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding"}
             if "1.5" in model:
-                    params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding", "norm","ofs_embedding", "norm_final", "norm_out", "proj_out"}
+                    params_to_keep.update({"norm1.linear.weight", "norm_k", "norm_q","ofs_embedding", "norm_final", "norm_out", "proj_out"}) 
             for name, param in transformer.named_parameters():
                 if not any(keyword in name for keyword in params_to_keep):
                     param.data = param.data.to(torch.float8_e4m3fn)
@@ -214,7 +214,7 @@ class DownloadAndLoadCogVideoModel:
             if fp8_transformer == "fastmode":
                 from .fp8_optimization import convert_fp8_linear
                 if "1.5" in model:
-                    params_to_keep.update({"ff"})
+                    params_to_keep.update({"ff"}) #otherwise NaNs
                 convert_fp8_linear(transformer, dtype, params_to_keep=params_to_keep)
 
         with open(scheduler_path) as f:
@@ -422,11 +422,11 @@ class DownloadAndLoadCogVideoGGUFModel:
                 params_to_keep = {"patch_embed", "pos_embedding", "time_embedding"}
                 cast_dtype = torch.float16
             elif "1_5" in model:
-                params_to_keep = {"patch_embed", "time_embedding", "ofs_embedding", "norm_final", "norm_out", "proj_out", "norm"}
+                params_to_keep = {"norm1.linear.weight", "patch_embed", "time_embedding", "ofs_embedding", "norm_final", "norm_out", "proj_out"}
                 cast_dtype = torch.bfloat16
             for name, param in transformer.named_parameters():
                 if not any(keyword in name for keyword in params_to_keep):
-                    param.data = param.data.to(torch.bfloat16)
+                    param.data = param.data.to(torch.float8_e4m3fn)
                 else:
                     param.data = param.data.to(cast_dtype)
             #for name, param in transformer.named_parameters():
@@ -438,8 +438,11 @@ class DownloadAndLoadCogVideoGGUFModel:
         transformer.attention_mode = attention_mode
         
         if fp8_fastmode:
+           params_to_keep = {"patch_embed", "lora", "pos_embedding", "time_embedding"}
+           if "1.5" in model:
+                params_to_keep.update({"ff","norm1.linear.weight", "norm_k", "norm_q","ofs_embedding", "norm_final", "norm_out", "proj_out"})   
            from .fp8_optimization import convert_fp8_linear
-           convert_fp8_linear(transformer, vae_dtype)
+           convert_fp8_linear(transformer, vae_dtype, params_to_keep=params_to_keep)
 
         if compile == "torch":
             # compilation

mz_gguf_loader.py

@@ -19,17 +19,21 @@ class quantize_lazy_load():
 
 
 def quantize_load_state_dict(model, state_dict, device="cpu"):
-    Q4_0_qkey = []
+    quant_keys = []
     for key in state_dict.keys():
         if key.endswith(".Q4_0_qweight"):
-            Q4_0_qkey.append(key.replace(".Q4_0_qweight", ""))
+            quant_keys.append(key.replace(".Q4_0_qweight", ""))
+            qtype = "Q4_0"
+        elif key.endswith(".Q8_0_qweight"):
+            quant_keys.append(key.replace(".Q8_0_qweight", ""))
+            qtype = "Q8_0"
 
     for name, module in model.named_modules():
-        if name in Q4_0_qkey:
+        if name in quant_keys:
             q_linear = WQLinear_GGUF.from_linear(
                 linear=module,
                 device=device,
-                qtype="Q4_0",
+                qtype=qtype,
             )
             set_op_by_name(model, name, q_linear)
 
@@ -117,14 +121,14 @@ class WQLinear_GGUF(nn.Module):
 
     @torch.no_grad()
     def forward(self, x):
-        # x = torch.matmul(x, dequantize_blocks_Q4_0(self.qweight))
         if self.qtype == "Q4_0":
-            x = F.linear(x, dequantize_blocks_Q4_0(
-                self.Q4_0_qweight, x.dtype), self.bias.to(x.dtype) if self.bias is not None else None)
+            dequant = dequantize_blocks_Q4_0(self.Q4_0_qweight, x.dtype)
+        elif self.qtype == "Q8_0":
+            dequant = dequantize_blocks_Q8_0(self.Q8_0_qweight, x.dtype)
         else:
             raise ValueError(f"Unknown qtype: {self.qtype}")
-
-        return x
+        
+        return F.linear(x, dequant, bias=self.bias.to(x.dtype) if self.bias is not None else None)
 
 
 def split_block_dims(blocks, *args):
@@ -153,6 +157,7 @@ def quant_shape_from_byte_shape(shape, qtype) -> tuple[int, ...]:
 
 GGML_QUANT_SIZES = {
     "Q4_0": (32, 2 + 16),
+    "Q8_0": (32, 2 + 32),
 }
 
 
@@ -186,3 +191,31 @@ def dequantize_blocks_Q4_0(data, dtype=torch.float16):
     )).to(dtype)
     return out
 
+def dequantize_blocks_Q8_0(data, dtype=torch.float16):
+    block_size, type_size = GGML_QUANT_SIZES["Q8_0"]
+
+    data = data.to(torch.uint8)
+    shape = data.shape
+
+    rows = data.reshape(
+        (-1, data.shape[-1])
+    ).view(torch.uint8)
+
+    n_blocks = rows.numel() // type_size
+    blocks = data.reshape((n_blocks, type_size))
+
+    n_blocks = blocks.shape[0]
+
+    d, qs = split_block_dims(blocks, 2)
+    d = d.view(torch.float16).to(torch.float32)
+
+    qs = qs.view(torch.int8).to(torch.float32)
+
+    out = (d * qs)
+
+    out = out.reshape(quant_shape_from_byte_shape(
+        shape,
+        qtype="Q8_0",
+    )).to(dtype)
+    return out
+