Fix FP8 MM

comfy/ops.py

@@ -390,19 +390,7 @@ def fp8_linear(self, input):
         
         # Wrap weight in QuantizedTensorFP8 - this enables unified dispatch
         quantized_weight = QuantizedTensorFP8(w, scale_weight, orig_dtype=input_dtype)
-        
+        quantized_input = QuantizedTensorFP8.quantize(input.reshape(-1, input_shape[2]), scale_input, fp8_dtype=dtype)
-        # Handle input quantization and wrapping
-        if self.scale_input is None:
-            # Clamp input to FP8 range and quantize
-            input = torch.clamp(input, min=-448, max=448, out=input)
-            input_fp8 = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
-        else:
-            # Apply inverse scale and quantize
-            input_fp8 = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous()
-        
-        # Wrap input in QuantizedTensorFP8
-        quantized_input = QuantizedTensorFP8(input_fp8, scale_input, orig_dtype=input_dtype)
-        
         # Call F.linear - __torch_dispatch__ routes to handle_linear_fp8 in quant_ops.py!
         # This is the key unification: all FP8 computation goes through one path
         o = torch.nn.functional.linear(quantized_input, quantized_weight, bias)

comfy/quant_ops.py

@@ -79,18 +79,47 @@ class QuantizedTensorFP8(torch.Tensor):
         self._scale = scale
         self._orig_dtype = orig_dtype
         # Store a reference to prevent infinite recursion in dequantize
-        self._raw_data = tensor
+        self._raw_data = tensor.contiguous()
     
     def __repr__(self):
         return (f"QuantizedTensorFP8(shape={self.shape}, "
                 f"scale={self._scale:.4f}, dtype={self._orig_dtype})")
     
+    @classmethod
+    def quantize(cls, tensor, scale, fp8_dtype=torch.float8_e4m3fn):
+        orig_dtype = tensor.dtype
+        
+        if not isinstance(scale, torch.Tensor):
+            scale = torch.tensor(scale, device=tensor.device, dtype=torch.float32)
+        
+        tensor_fp8 = None
+        if _CK_AVAILABLE:
+            try:
+                tensor_fp8 = ck.quantize_per_tensor_fp8(tensor, scale, fp8_dtype)
+            except Exception as e:
+                logging.debug(f"comfy_kitchen quantization failed, using PyTorch: {e}")
+        
+        if tensor_fp8 is None:
+            lp_amax = torch.finfo(fp8_dtype).max
+            tensor_scaled = tensor.float() / scale
+            torch.clamp(tensor_scaled, min=-lp_amax, max=lp_amax, out=tensor_scaled)
+            tensor_fp8 = tensor_scaled.to(fp8_dtype, memory_format=torch.contiguous_format)
+        
+        return cls(tensor_fp8, scale, orig_dtype=orig_dtype)
+    
+    @classmethod
+    def quantize_dynamic(cls, tensor, strategy="amax", fp8_dtype=torch.float8_e4m3fn):
+        if strategy == "amax":
+            scale = torch.amax(tensor) / torch.finfo(fp8_dtype).max
+            scale = scale.to(tensor.device, dtype=torch.float32)
+        else:
+            raise ValueError(f"Unknown quantization strategy: {strategy}. "
+                           f"Supported: 'amax'")
+
+        return cls.quantize(tensor, scale, fp8_dtype=fp8_dtype)
+    
     @classmethod
     def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
-        """
-        Intercept ALL torch operations.
-        Routes to registered handlers or falls back to dequantization.
-        """
         kwargs = kwargs or {}
         
         # Special case: skip dispatch for internal tensor operations
@@ -134,16 +163,11 @@ class QuantizedTensorFP8(torch.Tensor):
         return func(*new_args, **new_kwargs)
     
     def dequantize(self) -> torch.Tensor:
-        """Explicit dequantization"""
-        # Use the raw data and convert directly
-        # Call aten ops directly to minimize dispatch interference
         plain_tensor = torch.ops.aten._to_copy.default(self._raw_data, dtype=self._orig_dtype)
-        # Multiply by scale
         return plain_tensor * self._scale
     
     def detach(self):
         """Detach returns a new QuantizedTensorFP8 (required for Parameter)"""
-        # Detach the raw data and create a new QuantizedTensorFP8
         detached_data = self._raw_data.detach()
         return QuantizedTensorFP8(detached_data, self._scale, self._orig_dtype)
 
@@ -165,47 +189,34 @@ def handle_linear_fp8(func, args, kwargs):
     input_tensor = args[0]
     weight = args[1]
     bias = args[2] if len(args) > 2 else None
+    out_dtype = kwargs.get("out_dtype", input_tensor._orig_dtype)
 
     # Case 1: Both input and weight are FP8
     if isinstance(input_tensor, QuantizedTensorFP8) and isinstance(weight, QuantizedTensorFP8):
-        # Use _scaled_mm for FP8×FP8 matmul
         # Get plain tensors to avoid dispatch recursion
         plain_input = input_tensor._raw_data
         plain_weight = weight._raw_data
-        weight_t = plain_weight.t().contiguous()
+        weight_t = plain_weight.t()  # Keep as column-major for cuBLASLt
         
         try:
-            if bias is not None:
             output = torch._scaled_mm(
                 plain_input,
                 weight_t,
-                    out_dtype=input_tensor._orig_dtype,
                 bias=bias,
                 scale_a=input_tensor._scale,
-                    scale_b=weight._scale
+                scale_b=weight._scale,
+                out_dtype=out_dtype,
             )
-            else:
-                output = torch._scaled_mm(
-                    plain_input,
-                    weight_t,
-                    out_dtype=input_tensor._orig_dtype,
-                    scale_a=input_tensor._scale,
-                    scale_b=weight._scale
-                )
-            
             if isinstance(output, tuple):
                 output = output[0]
             
-            # Check if output is FP8 (some architectures support this)
             if output.dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
-                # Keep quantized!
                 output_scale = input_tensor._scale * weight._scale
-                return QuantizedTensorFP8(output, output_scale, input_tensor._orig_dtype)
+                return QuantizedTensorFP8(output, output_scale, input_tensor._orig_dtype) # TODO is this correct? Can't cuBLAS return it
             else:
                 return output
         except Exception as e:
             logging.debug(f"FP8 _scaled_mm failed, falling back to dequantization: {e}")
-            # Fall through to dequantization path
 
     # Case 2: Only weight is quantized
     if isinstance(weight, QuantizedTensorFP8):
@@ -222,125 +233,3 @@ def handle_linear_fp8(func, args, kwargs):
     else:
         return torch.nn.functional.linear(input_tensor, weight, bias)
 
-
-@register_quant_op(torch.ops.aten.silu.default)
-def handle_silu_fp8(func, args, kwargs):
-    """
-    SiLU can be computed approximately on FP8.
-    Keeps activations quantized for next layer.
-    """
-    input_q = args[0]
-    
-    if not isinstance(input_q, QuantizedTensorFP8):
-        # Not quantized, use standard path
-        return torch.nn.functional.silu(input_q)
-    
-    # Compute SiLU while keeping quantized
-    # SiLU(x) = x * sigmoid(x)
-    
-    # Get plain tensor to avoid dispatch recursion
-    plain_tensor = input_q._raw_data
-    
-    # Upcast to FP16 for sigmoid stability
-    x_fp16 = plain_tensor.to(torch.float16)
-    sigmoid_fp16 = torch.sigmoid(x_fp16 * input_q._scale)
-    result_fp16 = x_fp16 * sigmoid_fp16
-    
-    # Convert back to FP8
-    result_fp8 = result_fp16.to(plain_tensor.dtype)
-    
-    # Return quantized (scale approximately preserved)
-    return QuantizedTensorFP8(result_fp8, input_q._scale, input_q._orig_dtype)
-
-
-@register_quant_op(torch.ops.aten.layer_norm.default)
-def handle_layernorm_fp8(func, args, kwargs):
-    """
-    LayerNorm requires high precision.
-    Dequantizes input and returns standard tensor.
-    """
-    input_q = args[0]
-    normalized_shape = args[1]
-    weight = args[2] if len(args) > 2 else None
-    bias = args[3] if len(args) > 3 else None
-    eps = args[4] if len(args) > 4 else 1e-5
-    
-    # Dequantize if needed
-    if isinstance(input_q, QuantizedTensorFP8):
-        x = input_q.dequantize()
-    else:
-        x = input_q
-    
-    # Standard LayerNorm
-    result = torch.nn.functional.layer_norm(x, normalized_shape, weight, bias, eps)
-    
-    # Return dequantized (next layer will quantize if needed)
-    return result
-
-
-@register_quant_op(torch.ops.aten.group_norm.default)
-def handle_groupnorm_fp8(func, args, kwargs):
-    """
-    GroupNorm requires high precision.
-    Dequantizes input and returns standard tensor.
-    """
-    input_q = args[0]
-    num_groups = args[1]
-    weight = args[2] if len(args) > 2 else None
-    bias = args[3] if len(args) > 3 else None
-    eps = args[4] if len(args) > 4 else 1e-5
-    
-    # Dequantize if needed
-    if isinstance(input_q, QuantizedTensorFP8):
-        x = input_q.dequantize()
-    else:
-        x = input_q
-    
-    # Standard GroupNorm
-    result = torch.nn.functional.group_norm(x, num_groups, weight, bias, eps)
-    
-    # Return dequantized
-    return result
-
-
-@register_quant_op(torch.ops.aten.add.Tensor)
-def handle_add_fp8(func, args, kwargs):
-    """
-    Handle addition with mixed quantized/non-quantized tensors.
-    """
-    a = args[0]
-    b = args[1]
-    
-    # If both are quantized, dequantize both
-    if isinstance(a, QuantizedTensorFP8) and isinstance(b, QuantizedTensorFP8):
-        return a.dequantize() + b.dequantize()
-    # If only one is quantized, dequantize it
-    elif isinstance(a, QuantizedTensorFP8):
-        return a.dequantize() + b
-    elif isinstance(b, QuantizedTensorFP8):
-        return a + b.dequantize()
-    # Neither is quantized
-    else:
-        return a + b
-
-
-@register_quant_op(torch.ops.aten.mul.Tensor)
-def handle_mul_fp8(func, args, kwargs):
-    """
-    Handle multiplication with mixed quantized/non-quantized tensors.
-    """
-    a = args[0]
-    b = args[1]
-    
-    # If both are quantized, dequantize both
-    if isinstance(a, QuantizedTensorFP8) and isinstance(b, QuantizedTensorFP8):
-        return a.dequantize() * b.dequantize()
-    # If only one is quantized, dequantize it
-    elif isinstance(a, QuantizedTensorFP8):
-        return a.dequantize() * b
-    elif isinstance(b, QuantizedTensorFP8):
-        return a * b.dequantize()
-    # Neither is quantized
-    else:
-        return a * b
-