Create fp8_optimization_utils.py

utils/fp8_optimization_utils.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from tqdm import tqdm
+
+
+def calculate_fp8_maxval(exp_bits=4, mantissa_bits=3, sign_bits=1):
+    """
+    Calculate the maximum representable value in FP8 format.
+    Default is E4M3 format (4-bit exponent, 3-bit mantissa, 1-bit sign).
+
+    Args:
+        exp_bits (int): Number of exponent bits
+        mantissa_bits (int): Number of mantissa bits
+        sign_bits (int): Number of sign bits (0 or 1)
+
+    Returns:
+        float: Maximum value representable in FP8 format
+    """
+    assert exp_bits + mantissa_bits + sign_bits == 8, "Total bits must be 8"
+
+    # Calculate exponent bias
+    bias = 2 ** (exp_bits - 1) - 1
+
+    # Calculate maximum mantissa value
+    mantissa_max = 1.0
+    for i in range(mantissa_bits - 1):
+        mantissa_max += 2 ** -(i + 1)
+
+    # Calculate maximum value
+    max_value = mantissa_max * (2 ** (2**exp_bits - 1 - bias))
+
+    return max_value
+
+
+def quantize_tensor_to_fp8(tensor, scale, exp_bits=4, mantissa_bits=3, sign_bits=1, max_value=None, min_value=None):
+    """
+    Quantize a tensor to FP8 format.
+
+    Args:
+        tensor (torch.Tensor): Tensor to quantize
+        scale (float or torch.Tensor): Scale factor
+        exp_bits (int): Number of exponent bits
+        mantissa_bits (int): Number of mantissa bits
+        sign_bits (int): Number of sign bits
+
+    Returns:
+        tuple: (quantized_tensor, scale_factor)
+    """
+    # Create scaled tensor
+    scaled_tensor = tensor / scale
+
+    # Calculate FP8 parameters
+    bias = 2 ** (exp_bits - 1) - 1
+
+    if max_value is None:
+        # Calculate max and min values
+        max_value = calculate_fp8_maxval(exp_bits, mantissa_bits, sign_bits)
+        min_value = -max_value if sign_bits > 0 else 0.0
+
+    # Clamp tensor to range
+    clamped_tensor = torch.clamp(scaled_tensor, min_value, max_value)
+
+    # Quantization process
+    abs_values = torch.abs(clamped_tensor)
+    nonzero_mask = abs_values > 0
+
+    # Calculate logF scales (only for non-zero elements)
+    log_scales = torch.zeros_like(clamped_tensor)
+    if nonzero_mask.any():
+        log_scales[nonzero_mask] = torch.floor(torch.log2(abs_values[nonzero_mask]) + bias).detach()
+
+    # Limit log scales and calculate quantization factor
+    log_scales = torch.clamp(log_scales, min=1.0)
+    quant_factor = 2.0 ** (log_scales - mantissa_bits - bias)
+
+    # Quantize and dequantize
+    quantized = torch.round(clamped_tensor / quant_factor) * quant_factor
+
+    return quantized, scale
+
+
+def optimize_state_dict_with_fp8(
+    state_dict, calc_device, target_layer_keys=None, exclude_layer_keys=None, exp_bits=4, mantissa_bits=3, move_to_device=False
+):
+    """
+    Optimize Linear layer weights in a model's state dict to FP8 format.
+
+    Args:
+        state_dict (dict): State dict to optimize, replaced in-place
+        calc_device (str): Device to quantize tensors on
+        target_layer_keys (list, optional): Layer key patterns to target (None for all Linear layers)
+        exclude_layer_keys (list, optional): Layer key patterns to exclude
+        exp_bits (int): Number of exponent bits
+        mantissa_bits (int): Number of mantissa bits
+        move_to_device (bool): Move optimized tensors to the calculating device
+
+    Returns:
+        dict: FP8 optimized state dict
+    """
+    if exp_bits == 4 and mantissa_bits == 3:
+        fp8_dtype = torch.float8_e4m3fn
+    elif exp_bits == 5 and mantissa_bits == 2:
+        fp8_dtype = torch.float8_e5m2
+    else:
+        raise ValueError(f"Unsupported FP8 format: E{exp_bits}M{mantissa_bits}")
+
+    # Calculate FP8 max value
+    max_value = calculate_fp8_maxval(exp_bits, mantissa_bits)
+    min_value = -max_value  # this function supports only signed FP8
+
+    # Create optimized state dict
+    optimized_count = 0
+
+    # Enumerate tarket keys
+    target_state_dict_keys = []
+    for key in state_dict.keys():
+        # Check if it's a weight key and matches target patterns
+        is_target = (target_layer_keys is None or any(pattern in key for pattern in target_layer_keys)) and key.endswith(".weight")
+        is_excluded = exclude_layer_keys is not None and any(pattern in key for pattern in exclude_layer_keys)
+        is_target = is_target and not is_excluded
+
+        if is_target and isinstance(state_dict[key], torch.Tensor):
+            target_state_dict_keys.append(key)
+
+    # Process each key
+    for key in tqdm(target_state_dict_keys):
+        value = state_dict[key]
+
+        # Save original device and dtype
+        original_device = value.device
+        original_dtype = value.dtype
+
+        # Move to calculation device
+        if calc_device is not None:
+            value = value.to(calc_device)
+
+        # Calculate scale factor
+        scale = torch.max(torch.abs(value.flatten())) / max_value
+        # print(f"Optimizing {key} with scale: {scale}")
+
+        # Quantize weight to FP8
+        quantized_weight, _ = quantize_tensor_to_fp8(value, scale, exp_bits, mantissa_bits, 1, max_value, min_value)
+
+        # Add to state dict using original key for weight and new key for scale
+        fp8_key = key  # Maintain original key
+        scale_key = key.replace(".weight", ".scale_weight")
+
+        quantized_weight = quantized_weight.to(fp8_dtype)
+
+        if not move_to_device:
+            quantized_weight = quantized_weight.to(original_device)
+
+        scale_tensor = torch.tensor([scale], dtype=original_dtype, device=quantized_weight.device)
+
+        state_dict[fp8_key] = quantized_weight
+        state_dict[scale_key] = scale_tensor
+
+        optimized_count += 1
+
+        if calc_device is not None:  # optimized_count % 10 == 0 and
+            # free memory on calculation device
+            torch.cuda.empty_cache()  # TODO check device typ
+
+    print(f"Number of optimized Linear layers: {optimized_count}")
+    return state_dict
+
+
+def fp8_linear_forward_patch(self: nn.Linear, x, use_scaled_mm=False, max_value=None):
+    """
+    Patched forward method for Linear layers with FP8 weights.
+
+    Args:
+        self: Linear layer instance
+        x (torch.Tensor): Input tensor
+        use_scaled_mm (bool): Use scaled_mm for FP8 Linear layers, requires SM 8.9+ (RTX 40 series)
+        max_value (float): Maximum value for FP8 quantization. If None, no quantization is applied for input tensor.
+
+    Returns:
+        torch.Tensor: Result of linear transformation
+    """
+    if use_scaled_mm:
+        input_dtype = x.dtype
+        original_weight_dtype = self.scale_weight.dtype
+        weight_dtype = self.weight.dtype
+        target_dtype = torch.float8_e5m2
+        assert weight_dtype == torch.float8_e4m3fn, "Only FP8 E4M3FN format is supported"
+        assert x.ndim == 3, "Input tensor must be 3D (batch_size, seq_len, hidden_dim)"
+
+        if max_value is None:
+            # no input quantization
+            scale_x = torch.tensor(1.0, dtype=torch.float32, device=x.device)
+        else:
+            # calculate scale factor for input tensor
+            scale_x = (torch.max(torch.abs(x.flatten())) / max_value).to(torch.float32)
+
+            # quantize input tensor to FP8: this seems to consume a lot of memory
+            x, _ = quantize_tensor_to_fp8(x, scale_x, 5, 2, 1, max_value, -max_value)
+
+        original_shape = x.shape
+        x = x.reshape(-1, x.shape[2]).to(target_dtype)
+
+        weight = self.weight.t()
+        scale_weight = self.scale_weight.to(torch.float32)
+
+        if self.bias is not None:
+            # float32 is not supported with bias in scaled_mm
+            o = torch._scaled_mm(x, weight, out_dtype=original_weight_dtype, bias=self.bias, scale_a=scale_x, scale_b=scale_weight)
+        else:
+            o = torch._scaled_mm(x, weight, out_dtype=input_dtype, scale_a=scale_x, scale_b=scale_weight)
+
+        return o.reshape(original_shape[0], original_shape[1], -1).to(input_dtype)
+
+    else:
+        # Dequantize the weight
+        original_dtype = self.scale_weight.dtype
+        dequantized_weight = self.weight.to(original_dtype) * self.scale_weight
+
+        # Perform linear transformation
+        if self.bias is not None:
+            output = F.linear(x, dequantized_weight, self.bias)
+        else:
+            output = F.linear(x, dequantized_weight)
+
+        return output
+
+
+def apply_fp8_monkey_patch(model, optimized_state_dict, use_scaled_mm=False):
+    """
+    Apply monkey patching to a model using FP8 optimized state dict.
+
+    Args:
+        model (nn.Module): Model instance to patch
+        optimized_state_dict (dict): FP8 optimized state dict
+        use_scaled_mm (bool): Use scaled_mm for FP8 Linear layers, requires SM 8.9+ (RTX 40 series)
+
+    Returns:
+        nn.Module: The patched model (same instance, modified in-place)
+    """
+    # # Calculate FP8 float8_e5m2 max value
+    # max_value = calculate_fp8_maxval(5, 2)
+    max_value = None  # do not quantize input tensor
+
+    # Find all scale keys to identify FP8-optimized layers
+    scale_keys = [k for k in optimized_state_dict.keys() if k.endswith(".scale_weight")]
+
+    # Enumerate patched layers
+    patched_module_paths = set()
+    for scale_key in scale_keys:
+        # Extract module path from scale key (remove .scale_weight)
+        module_path = scale_key.rsplit(".scale_weight", 1)[0]
+        patched_module_paths.add(module_path)
+
+    patched_count = 0
+
+    # Apply monkey patch to each layer with FP8 weights
+    for name, module in model.named_modules():
+        # Check if this module has a corresponding scale_weight
+        has_scale = name in patched_module_paths
+
+        # Apply patch if it's a Linear layer with FP8 scale
+        if isinstance(module, nn.Linear) and has_scale:
+            # register the scale_weight as a buffer to load the state_dict
+            module.register_buffer("scale_weight", torch.tensor(1.0, dtype=module.weight.dtype))
+
+            # Create a new forward method with the patched version.
+            def new_forward(self, x):
+                return fp8_linear_forward_patch(self, x, use_scaled_mm, max_value)
+
+            # Bind method to module
+            module.forward = new_forward.__get__(module, type(module))
+
+            patched_count += 1
+
+    print(f"Number of monkey-patched Linear layers: {patched_count}")
+    return model