Merge da17be080e into 1dae34b0af

flux_train.py

@@ -330,6 +330,8 @@ def train(args):
     # 学習に必要なクラスを準備する
     accelerator.print("prepare optimizer, data loader etc.")
 
+    fused_optimizers_supported = ['adafactor', 'adamoffload', 'nadamoffload', 'adamwoffload', 'nadamwoffload', 'adanoffload']
+
     if args.blockwise_fused_optimizers:
         # fused backward pass: https://pytorch.org/tutorials/intermediate/optimizer_step_in_backward_tutorial.html
         # Instead of creating an optimizer for all parameters as in the tutorial, we create an optimizer for each block of parameters.
@@ -381,10 +383,25 @@ def train(args):
             raise ValueError("Schedule-free optimizer is not supported with blockwise fused optimizers")
         optimizer_train_fn = lambda: None  # dummy function
         optimizer_eval_fn = lambda: None  # dummy function
+
+        if (args.optimizer_type in fused_optimizers_supported) and args.full_bf16:
+            logger.warning("Use of --blockwise_fused_optimizers is preventing stochastic/Kahan weight updates.")
     else:
         _, _, optimizer = train_util.get_optimizer(args, trainable_params=params_to_optimize)
         optimizer_train_fn, optimizer_eval_fn = train_util.get_optimizer_train_eval_fn(optimizer, args)
 
+    # Pass any Kahan summation arg to the optimizer
+    if args.kahan_summation:
+        # Self check parameter compatibility
+        if args.optimizer_type.lower() not in fused_optimizers_supported:
+            logger.warning("Kahan summation has been requested, but this is not supported by the selected optimizer.")
+        if not args.full_bf16:
+            logger.warning("Kahan summation requires --full_bf16")
+        if args.blockwise_fused_optimizers:
+            logger.warning("Kahan summation has been requested, but these are not compatible with --blockwise_fused_optimizer. "\
+                           "Perhaps try --fused_backward_pass instead.")
+    optimizer.use_kahan_summation = args.kahan_summation
+
     # prepare dataloader
     # strategies are set here because they cannot be referenced in another process. Copy them with the dataset
     # some strategies can be None
@@ -437,6 +454,28 @@ def train(args):
         ), "full_bf16 requires mixed precision='bf16' / full_bf16を使う場合はmixed_precision='bf16'を指定してください。"
         accelerator.print("enable full bf16 training.")
         flux.to(weight_dtype)
+
+        # Experimental: some layers have very few weights, and training quality seems
+        # to increase significantly if these are left in f32 format while training.
+        if args.fused_backward_pass:
+
+            from library.flux_models import MixedLinear
+            from library.flux_models import RMSNorm
+
+            flux.final_layer.linear.to(dtype=torch.float32)
+            flux.img_in            .to(dtype=torch.float32)
+
+            for m in flux.modules():
+                num_params = sum(p.numel() for p in m.parameters())
+                
+                if isinstance(m, MixedLinear) and m.bias is not None:
+                    m.bias.data = m.bias.data.to(torch.float32)
+                    if m.weight.data.numel() < 20000000:  # Includes first Linear stage with 18m weights
+                        m.weight.data = m.weight.data.to(torch.float32)
+
+                if isinstance(m, RMSNorm):
+                    m.scale.data = m.scale.data.to(torch.float32)
+
         if clip_l is not None:
             clip_l.to(weight_dtype)
             t5xxl.to(weight_dtype)
@@ -474,10 +513,21 @@ def train(args):
     train_util.resume_from_local_or_hf_if_specified(accelerator, args)
 
     if args.fused_backward_pass:
-        # use fused optimizer for backward pass: other optimizers will be supported in the future
+        # use fused optimizer for backward pass. Only some specific optimizers are supported.
         import library.adafactor_fused
+        import library.adamw_fused
+        import library.adan_fused
 
-        library.adafactor_fused.patch_adafactor_fused(optimizer)
+        if args.optimizer_type.lower() == "adafactor":
+            library.adafactor_fused.patch_adafactor_fused(optimizer)
+        elif args.optimizer_type.lower() == "adamoffload" or args.optimizer_type.lower() == "adamwoffload":
+            library.adamw_fused.patch_adamw_offload_fused(optimizer, False)
+        elif args.optimizer_type.lower() == "nadamoffload" or args.optimizer_type.lower() == "nadamwoffload":
+            library.adamw_fused.patch_adamw_offload_fused(optimizer, True)  # Nesterov
+        elif args.optimizer_type.lower() == "adanoffload":
+            library.adan_fused.patch_adan_offload_fused(optimizer, False)  # Adan
+        else:
+            logger.error(f"Optimizer '{args.optimizer_type}' does not have a --fused_backward_pass implementation available")
 
         for param_group, param_name_group in zip(optimizer.param_groups, param_names):
             for parameter, param_name in zip(param_group["params"], param_name_group):
@@ -816,6 +866,12 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="enable blockwise optimizers for fused backward pass and optimizer step / fused backward passとoptimizer step のためブロック単位のoptimizerを有効にする",
     )
+    parser.add_argument(
+        "--kahan_summation",
+        action="store_true",
+        help="Offloads to CPU the float part lost during bf16 quantization, and re-adds it to the next step / "\
+            "bf16 量子化中に失われた浮動小数点部分を CPU にオフロードし、次のステップに再度追加します",
+    )
     parser.add_argument(
         "--skip_latents_validity_check",
         action="store_true",

library/adafactor_fused.py

@@ -28,6 +28,62 @@ def copy_stochastic_(target: torch.Tensor, source: torch.Tensor):
     del result
 
 
+# Kahan summation for bfloat16
+# The implementation was provided by araleza.
+# Based on paper "Revisiting BFloat16 Training": https://arxiv.org/pdf/2010.06192
+
+def copy_kahan_(target: torch.Tensor, source: torch.Tensor, state, update):
+    """
+    Copies source into target using Kahan summation.
+
+    The lower bits of the float32 weight that are lost on conversion to bfloat16
+    are sent to the CPU until the next step, where they are re-added onto the weights
+    before adding the gradient update.  This produces near float32-like weight behavior,
+    although the copies back and forth to main memory result in slower training steps.
+
+    Args:
+        target: the target tensor with dtype=bfloat16
+        source: the target tensor with dtype=float32
+        state:  the optimizer state, used to store kahan residuals
+        update: the change in weights due to the gradient
+    """
+
+    # Initialize residuals to 0 for first step
+    if state.get('kahan_residuals') is None:
+        state['kahan_residuals'] = torch.zeros_like(source, dtype=torch.int16)
+    
+    # Need this in 32 bit as PyTorch doesn't support mixed 32-bit and 16-bit math operations
+    state['kahan_residuals'] = state['kahan_residuals'].to(source.device).to(dtype=torch.int32)
+    
+    # Bring the previous step's lower bits of the weights back from the
+    # cpu device, and add them back to the weights of the current step.
+    source_i32 = source.view(dtype=torch.int32)  # Can't do math on uint32
+    source_i32.add_(state['kahan_residuals'])
+
+    # Reverse any rounding up during the cast to bf16 on the previous step
+    rounded_up = state['kahan_residuals'] >= 32768
+    source_i32[rounded_up] -= 65536
+
+    # Must add the gradient update after the bottom bits are restored in case
+    # the exponent is changed by the update, or the -65536 on the line above
+    # would drop the uint32 value below zero, which is invalid.
+    source.add_(-update)
+
+    # Get the lower bits into the residual
+    torch.bitwise_and(source_i32, 0x0000FFFF, out=state['kahan_residuals'])
+
+    # Ensure rounding to bfloat16 matches expectations. These lines may not be
+    # necessary as target.copy_ should do this rounding anyway.
+    source_i32.add_(32768)  # Add offset so clipping bits performs round-to-nearest
+    source_i32.bitwise_and_(-65536)  # -65536 = FFFF0000 as a signed int32. Leaves only upper bits in source
+
+    # Move the 16-bit Kahan bits from VRAM to main memory
+    state['kahan_residuals'] = state['kahan_residuals'].to(dtype=torch.uint16).to("cpu")
+
+    # Copy the quantized floats into the target tensor
+    target.copy_(source)
+
+
 @torch.no_grad()
 def adafactor_step_param(self, p, group):
     if p.grad is None:
@@ -102,13 +158,19 @@ def adafactor_step_param(self, p, group):
     if group["weight_decay"] != 0:
         p_data_fp32.add_(p_data_fp32, alpha=(-group["weight_decay"] * lr))
 
-    p_data_fp32.add_(-update)
+    # Add on gradient update, but not if using kahan summation as the bottom
+    # bits must be restored first. (This update occurs in copy_kahan_() instead)
+    if not self.optimizer.use_kahan_summation:
+        p_data_fp32.add_(-update)
 
     # if p.dtype in {torch.float16, torch.bfloat16}:
     #    p.copy_(p_data_fp32)
 
     if p.dtype == torch.bfloat16:
-        copy_stochastic_(p, p_data_fp32)
+        if self.optimizer.use_kahan_summation:
+            copy_kahan_(p, p_data_fp32, state, update)
+        else:
+            copy_stochastic_(p, p_data_fp32)
     elif p.dtype == torch.float16:
         p.copy_(p_data_fp32)
 

library/adamw_fused.py

@@ -0,0 +1,198 @@
+import math
+import torch
+
+from library.adafactor_fused import copy_stochastic_
+from library.adafactor_fused import copy_kahan_
+
+
+def to_float24_bytes(tensor_f32: torch.Tensor) -> torch.Tensor:
+    """
+    Converts a float32 tensor to a 'float24' representation for storage.
+
+    This is done by taking the 3 most significant bytes of each float32 element.
+    On a little-endian system, these are the last 3 bytes.
+    # TODO - Check this works on Mac, which is a big-endian system
+
+    Args:
+        tensor_f32: The input tensor with dtype torch.float32.
+
+    Returns:
+        A 1D tensor of dtype torch.uint8 containing the packed 'float24' data.
+    """
+    if tensor_f32.dtype != torch.float32:
+        raise TypeError("Input tensor must be of dtype torch.float32")
+
+    tensor_u8 = tensor_f32.view(torch.uint8)
+    tensor_u8_reshaped = tensor_u8.view(-1, 4)
+    tensor_f24_bytes = tensor_u8_reshaped[:, 1:]
+    return tensor_f24_bytes.flatten()
+
+
+def from_float24_bytes(tensor_f24_u8: torch.Tensor, original_shape: torch.Size) -> torch.Tensor:
+    """
+    Restores a 'float24' byte tensor back to a float32 tensor.
+
+    Args:
+        tensor_f24_u8: A 1D tensor of dtype torch.uint8 from to_float24_bytes.
+        original_shape: The shape of the original float32 tensor.
+        device: The device to create the restored tensor on.
+
+    Returns:
+        The restored tensor with dtype torch.float32 and the original shape.
+    """
+    if tensor_f24_u8.dtype != torch.uint8:
+        raise TypeError("Input byte tensor must be of dtype torch.uint8")
+    if tensor_f24_u8.numel() % 3 != 0:
+        raise ValueError("Input byte tensor size must be a multiple of 3")
+    
+    tensor_u8_3bytes = tensor_f24_u8.view(-1, 3)
+    padding = torch.zeros(tensor_u8_3bytes.shape[0], 1, dtype=torch.uint8, device=tensor_u8_3bytes.device)
+    tensor_u8_4bytes = torch.cat([padding, tensor_u8_3bytes], dim=1)
+    tensor_f32_flat = tensor_u8_4bytes.flatten().view(torch.float32)
+    return tensor_f32_flat.view(original_shape)
+
+
+@torch.no_grad()
+def adamw_offload_step_param(self, p, group):
+
+    if p.grad is None:
+        return
+    grad = p.grad
+    if grad.dtype in {torch.float16, torch.bfloat16}:
+        grad = grad.float()
+    if grad.is_sparse:
+        raise RuntimeError("This (N)AdamW implementation does not support sparse gradients.")
+
+    state = self.state[p]
+    grad_shape = grad.shape
+
+    p_data_fp32 = p
+    if p.dtype in {torch.float16, torch.bfloat16}:
+        p_data_fp32 = p_data_fp32.float()
+    
+    # Tensors with few elements may be more sensitive to quantization
+    # errors, so keep them in float32
+    high_quality = torch.numel(p) <= 4096
+
+    # State Initialization
+    if len(state) == 0:
+        state["step"] = 0
+
+        data_type = torch.float32 if high_quality else torch.uint16
+
+        state['exp_avg']    = torch.zeros_like(p, dtype=data_type)
+        state['exp_avg_sq'] = torch.zeros_like(p, dtype=data_type)
+
+    state["step"] += 1
+
+    # NAdam
+
+    beta1, beta2 = group['betas']
+    eps = group['eps']  # 1e-8
+    weight_decay = group.get('weight_decay', 0.0)
+
+    # Bias correction terms
+    bias_correction1 = 1.0 - math.pow(beta1, state['step'])
+    bias_correction2 = 1.0 - math.pow(beta2, state['step'])
+        
+    eps_p2: float = math.pow(eps, 2)
+
+    # Bring state back (from CPU, if necessary)
+
+    # Recover the exp avg states from however they're stored
+    def unpack_tensor(state, key, target_device):
+
+        # Stored as f24 format?
+        if state[f'{key}'].dtype == torch.uint8:
+            return from_float24_bytes(state[f'{key}'].to(target_device), state[f'{key}_shape'])
+
+        # bf16 / u16 / f32
+        return state[f'{key}'].to(target_device).to(dtype=torch.float32)
+
+    state['exp_avg']    = unpack_tensor(state, 'exp_avg',    p.device)
+    state['exp_avg_sq'] = unpack_tensor(state, 'exp_avg_sq', p.device)
+    exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
+
+    # Update biased first and second moment estimates
+    exp_avg   .mul_(beta1).add_    (grad,       alpha=1.0 - beta1)
+    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1.0 - beta2)
+
+    # Compute bias-corrected second moment for denominator
+    exp_avg_sq_corrected = exp_avg_sq / bias_correction2
+
+    # Compute update based on whether Nesterov momentum (NAdam) is being used
+    if self.use_nesterov:
+        # The next step's bias correction for momentum is needed
+        bias_correction1_next = 1.0 - math.pow(beta1, state['step'] + 1)
+
+        # NAdam update: combines current gradient with momentum look-ahead
+        momentum_cache = exp_avg / bias_correction1_next
+        update = (beta1 * momentum_cache + (1.0 - beta1) * grad / bias_correction1) / (exp_avg_sq_corrected.sqrt() + eps)
+    else:
+        # Standard Adam update: use bias-corrected first moment directly
+        exp_avg_corrected = exp_avg / bias_correction1
+        update = exp_avg_corrected / (exp_avg_sq_corrected.sqrt() + eps)
+
+    lr: float = group['lr']
+    
+    # Implement 'cautious optimizer' from https://arxiv.org/pdf/2411.16085
+    # The scaling factor - dividing by m.mean() - does not seem to work with parameter
+    # groups, but it also appears to be an optional step, so it has been removed.
+    m = (update * grad >= 0).to(grad.dtype)
+    update = update * m #/ (m.mean() + eps)
+
+    # Apply learning rate
+    update.mul_(lr)
+
+    # Apply weight decay
+    if weight_decay != 0:
+        p_data_fp32.mul_(1 - lr * weight_decay)
+
+    # Reduce the size of large exp_avg and exp_avg_sq tensors to 24-bit,
+    # and then move them to cpu memory
+    if not high_quality:
+        state[f'exp_avg_shape'] = state[f'exp_avg'].shape
+        state[f'exp_avg'] = to_float24_bytes(state[f'exp_avg']).to('cpu')
+
+        state[f'exp_avg_sq_shape'] = state[f'exp_avg_sq'].shape
+        state[f'exp_avg_sq'] = to_float24_bytes(state[f'exp_avg_sq']).to('cpu')
+
+    # Add on gradient update, but not if using kahan summation as the bottom
+    # bits must be restored first. (This update occurs in copy_kahan_() instead)
+    if not self.optimizer.use_kahan_summation:
+        p_data_fp32.add_(-update)
+
+    if p.dtype == torch.bfloat16:
+        if self.optimizer.use_kahan_summation:
+            copy_kahan_(p, p_data_fp32, state, update)
+        else:
+            copy_stochastic_(p, p_data_fp32)
+    elif p.dtype == torch.float16:
+        p.copy_(p_data_fp32)
+
+
+@torch.no_grad()
+def adamw_offload_step(self, closure=None):
+    """
+    Performs a single optimization step
+
+    Arguments:
+        closure (callable, optional): A closure that reevaluates the model
+            and returns the loss.
+    """
+    loss = None
+    if closure is not None:
+        loss = closure()
+
+    for group in self.param_groups:
+        for p in group["params"]:
+            adamw_offload_step_param(self, p, group)
+
+    return loss
+
+
+def patch_adamw_offload_fused(optimizer, use_nesterov):
+    optimizer.use_nesterov = use_nesterov
+
+    optimizer.step_param = adamw_offload_step_param.__get__(optimizer)
+    optimizer.step = adamw_offload_step.__get__(optimizer)

library/adan_fused.py

@@ -0,0 +1,218 @@
+import math
+import torch
+
+from library.adafactor_fused import copy_stochastic_
+from library.adafactor_fused import copy_kahan_
+
+
+# Pack floating point tensors into uint16. Their float32 bytes are interpreted as uint32
+# bytes (not cast to uint32). Since positive floats are in sequential order when interpreted
+# as uint32s, the groups of positive and negative floats appear as small ranges in uint32
+# format. The three clumps (negative floats, zeros, postive floats) then have their min/max
+# positions noted, and stretched to cover a uint16 range.
+def pack_tensor(state, key, support_neg):
+
+    k = state[f'{key}']
+    k_uint32_f = torch.abs(k).view(torch.uint32).to(torch.float32)
+    
+    min_val, max_val = torch.aminmax(k_uint32_f[k_uint32_f != 0.0])
+
+    # No support_neg (i.e. input floats are only zero or positive). Outputs values in these uint16 ranges:
+    #            0 <-- 0.0s
+    #     1..65535 <-- positive floats
+
+    # support_neg (i.e. input floats can be zero or +/-). Outputs values in these uint16 ranges:
+    #            0 <-- 0.0s
+    #     1..32767 <-- positive floats
+    #        32768 <-- -0.0 ?  Not used.
+    # 32769..65535 <-- negative floats
+
+    range = 32768 if support_neg else 65536
+
+    k_int32_scale = (k_uint32_f - min_val) * (range - 2) / (max_val - min_val) + 1  # Scale into [1..range]
+
+    packed = torch.where(k > 0, k_int32_scale, 0)  # Positive floats and zero
+    if support_neg:
+        packed = torch.where(k < 0, k_int32_scale + 32768, packed)  # Negative floats
+    del k_int32_scale
+
+    k_uint16_scale = packed.to(torch.uint16)
+
+    state[f'{key}']     = k_uint16_scale
+    state[f'{key}_min'] = min_val
+    state[f'{key}_max'] = max_val
+
+    pass
+
+
+# Recover adan state tensors packed wtih pack_tensor()
+def unpack_tensor(state, key, support_neg):
+
+    # uint16 format = packed floats
+    if state[f'{key}'].dtype == torch.uint16:
+        packed  = state[f'{key}'].to('cuda').to(dtype=torch.float32)
+        min_val = state[f'{key}_min']
+        max_val = state[f'{key}_max']
+        
+        range = 32768.0 if support_neg else 65536.0
+
+        if support_neg:
+            pack_merge_signs = torch.where(packed >= 32768, packed - 32768, packed)
+        else:
+            pack_merge_signs = packed
+        upck = (pack_merge_signs - 1) / (range - 2) * (max_val - min_val) + min_val
+        upck = torch.where(pack_merge_signs == 0, 0, upck)  # 0's are special cased
+        upck = upck.to(torch.uint32)
+        upck_final_but_no_negs = upck.view(torch.float32)
+        if support_neg:
+            upck_final = torch.where(packed >= 32768, -upck_final_but_no_negs, upck_final_but_no_negs)
+        else:
+            upck_final = upck_final_but_no_negs
+
+        return upck_final
+
+    # bf16 / f32
+    return state[f'{key}'].to('cuda').to(dtype=torch.float32)
+
+
+@torch.no_grad()
+def adan_offload_step_param(self, p, group):
+
+    if p.grad is None:
+        return
+    grad = p.grad
+    if grad.dtype in {torch.float16, torch.bfloat16}:
+        grad = grad.float()
+    if grad.is_sparse:
+        raise RuntimeError("This Adan implementation does not support sparse gradients.")
+
+    state = self.state[p]
+    grad_shape = grad.shape
+
+    p_data_fp32 = p
+    if p.dtype in {torch.float16, torch.bfloat16}:
+        p_data_fp32 = p_data_fp32.float()
+    
+    # Tensors with few elements may be more sensitive to quantization
+    # errors, so keep them in float32
+    #global tot_4096, tot_all
+    high_quality = torch.numel(p) <= 2000000
+
+    # State Initialization
+    if len(state) == 0:
+        state["step"] = 0
+
+        state['exp_avg']      = torch.zeros_like(p, dtype=torch.float32 if high_quality else torch.bfloat16)
+        state['exp_avg_sq']   = torch.zeros_like(p, dtype=torch.float32 if high_quality else torch.bfloat16)
+        state['exp_avg_diff'] = torch.zeros_like(p, dtype=torch.float32 if high_quality else torch.bfloat16)
+        state['neg_grad_or_diff'] = torch.zeros_like(p, dtype=torch.float32 if high_quality else torch.bfloat16)
+    else:
+        pass
+
+    state["step"] += 1
+
+    #beta1, beta2, beta3 = group['betas']  # Don't have custom class, so beta3 not available
+    beta1, beta2, beta3 = (0.98, 0.92, 0.99)  # Hard coded betas for now
+    eps = group['eps']  # 1e-8
+    weight_decay = group.get('weight_decay', 0.0)  # Not currently implemented
+
+    # Bias correction terms
+    bias_correction1 = 1.0 - math.pow(beta1, state['step'])
+    bias_correction2 = 1.0 - math.pow(beta2, state['step'])
+    bias_correction3 = 1.0 - math.pow(beta3, state['step'])
+    bias_correction3_sqrt = math.sqrt(bias_correction3)
+        
+    eps_p2: float = math.pow(eps, 2)
+
+    # Recover the exp avg states from however they're stored
+    state['exp_avg']      = unpack_tensor(state, 'exp_avg',      True)
+    state['exp_avg_sq']   = unpack_tensor(state, 'exp_avg_sq',   False)
+    state['exp_avg_diff'] = unpack_tensor(state, 'exp_avg_diff', True)
+    state['neg_grad_or_diff'] = unpack_tensor(state, 'neg_grad_or_diff', True)
+
+    exp_avg          = state['exp_avg']
+    exp_avg_sq       = state['exp_avg_sq']
+    exp_avg_diff     = state['exp_avg_diff']
+    neg_grad_or_diff = state['neg_grad_or_diff']
+
+    # for memory saving, we use `neg_grad_or_diff`
+    # to get some temp variable in a inplace way
+    neg_grad_or_diff.add_(grad)
+
+    exp_avg     .mul_(beta1).add_(grad,             alpha= 1 - beta1)  # m_t
+    exp_avg_diff.mul_(beta2).add_(neg_grad_or_diff, alpha= 1 - beta2)  # diff_t
+
+    neg_grad_or_diff.mul_(beta2).add_(grad)
+    exp_avg_sq      .mul_(beta3).addcmul_(neg_grad_or_diff, neg_grad_or_diff, value= 1 - beta3)  # n_t
+
+    lr: float = group['lr']
+
+    denom = (exp_avg_sq.sqrt() / bias_correction3_sqrt).add_(eps)
+    step_size      = lr         / bias_correction1
+    step_size_diff = lr * beta2 / bias_correction2
+
+    # todo: weight decay not supported
+    update  = (exp_avg      * step_size     ) / denom
+    update += (exp_avg_diff * step_size_diff) / denom
+
+    neg_grad_or_diff.zero_().add_(grad, alpha=-1.0)
+
+    # Just build momentum for first few steps
+    if state['step'] <= 3:
+        update.mul_(0.0)
+
+    # Move the optimizer state tensors to main memory
+    if not high_quality:
+
+        # float32 to uint16 compression, hopefully provides more precision
+        pack_tensor(state, 'exp_avg',      True)
+        pack_tensor(state, 'exp_avg_sq',   False)  # Only positive floats
+        pack_tensor(state, 'exp_avg_diff', True)
+
+        state[f'exp_avg']      = state[f'exp_avg']     .to('cpu')
+        state[f'exp_avg_sq']   = state[f'exp_avg_sq']  .to('cpu')
+        state[f'exp_avg_diff'] = state[f'exp_avg_diff'].to('cpu')
+
+        # Neg_grad is always a bfloat16 (stored in a float32) already apparently! So
+        # can be stored as a bfloat16 exactly.
+        state[f'neg_grad_or_diff'] = state[f'neg_grad_or_diff'].to(torch.bfloat16).to('cpu')
+
+    # Add on gradient update, but not if using kahan summation as the bottom
+    # bits must be restored first. (This update occurs in copy_kahan_() instead)
+    if not self.optimizer.use_kahan_summation:
+        p_data_fp32.add_(-update)
+
+    if p.dtype == torch.bfloat16:
+        if self.optimizer.use_kahan_summation:
+            copy_kahan_(p, p_data_fp32, state, update)
+        else:
+            copy_stochastic_(p, p_data_fp32)
+    elif p.dtype == torch.float16:
+        p.copy_(p_data_fp32)
+
+
+@torch.no_grad()
+def adan_offload_step(self, closure=None):
+    """
+    Performs a single optimization step
+
+    Arguments:
+        closure (callable, optional): A closure that reevaluates the model
+            and returns the loss.
+    """
+    loss = None
+    if closure is not None:
+        loss = closure()
+
+    for group in self.param_groups:
+        for p in group["params"]:
+            adan_offload_step_param(self, p, group)
+
+    return loss
+
+
+def patch_adan_offload_fused(optimizer, use_nesterov):
+    optimizer.use_nesterov = use_nesterov
+
+    optimizer.step_param = adan_offload_step_param.__get__(optimizer)
+    optimizer.step = adan_offload_step.__get__(optimizer)

library/flux_models.py

@@ -543,12 +543,43 @@ def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 10
     return embedding
 
 
+import torch.nn.functional as F
+
+# A class that supports having the biases have a dtype of float32
+# while the more numerous weights are still in bfloat16 format.
+
+class MixedLinear(nn.Module):
+    def __init__(self, in_features, out_features, bias=True):
+        super().__init__()
+        # Initialize weights in float32 first, then cast to bfloat16
+        weight = torch.empty(out_features, in_features, dtype=torch.float32)
+        nn.init.kaiming_uniform_(weight, a=5**0.5)
+        self.weight = nn.Parameter(weight.to(torch.bfloat16))
+
+        if bias:
+            bias_param = torch.empty(out_features, dtype=torch.float32)  # High precision
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(weight)
+            bound = 1 / fan_in**0.5
+            nn.init.uniform_(bias_param, -bound, bound)
+            self.bias = nn.Parameter(bias_param)
+        else:
+            self.bias = None
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        if self.weight.dtype == torch.bfloat16:
+            weight_fp32 = self.weight.to(torch.float32)
+        else:
+            weight_fp32 = self.weight
+
+        return F.linear(input, weight_fp32, self.bias)
+
+
 class MLPEmbedder(nn.Module):
     def __init__(self, in_dim: int, hidden_dim: int):
         super().__init__()
-        self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+        self.in_layer = MixedLinear(in_dim, hidden_dim, bias=True)
         self.silu = nn.SiLU()
-        self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+        self.out_layer = MixedLinear(hidden_dim, hidden_dim, bias=True)
 
         self.gradient_checkpointing = False
 
@@ -609,9 +640,9 @@ class SelfAttention(nn.Module):
         self.num_heads = num_heads
         head_dim = dim // num_heads
 
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.qkv = MixedLinear(dim, dim * 3, bias=qkv_bias)
         self.norm = QKNorm(head_dim)
-        self.proj = nn.Linear(dim, dim)
+        self.proj = MixedLinear(dim, dim)
 
     # this is not called from DoubleStreamBlock/SingleStreamBlock because they uses attention function directly
     def forward(self, x: Tensor, pe: Tensor) -> Tensor:
@@ -635,7 +666,7 @@ class Modulation(nn.Module):
         super().__init__()
         self.is_double = double
         self.multiplier = 6 if double else 3
-        self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+        self.lin = MixedLinear(dim, self.multiplier * dim, bias=True)
 
     def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
         out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
@@ -659,9 +690,9 @@ class DoubleStreamBlock(nn.Module):
 
         self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.img_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            MixedLinear(hidden_size, mlp_hidden_dim, bias=True),
             nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+            MixedLinear(mlp_hidden_dim, hidden_size, bias=True),
         )
 
         self.txt_mod = Modulation(hidden_size, double=True)
@@ -670,9 +701,9 @@ class DoubleStreamBlock(nn.Module):
 
         self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
         self.txt_mlp = nn.Sequential(
-            nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+            MixedLinear(hidden_size, mlp_hidden_dim, bias=True),
             nn.GELU(approximate="tanh"),
-            nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+            MixedLinear(mlp_hidden_dim, hidden_size, bias=True),
         )
 
         self.gradient_checkpointing = False
@@ -780,9 +811,9 @@ class SingleStreamBlock(nn.Module):
 
         self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
         # qkv and mlp_in
-        self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+        self.linear1 = MixedLinear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
         # proj and mlp_out
-        self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+        self.linear2 = MixedLinear(hidden_size + self.mlp_hidden_dim, hidden_size)
 
         self.norm = QKNorm(head_dim)
 
@@ -862,8 +893,8 @@ class LastLayer(nn.Module):
     def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
         super().__init__()
         self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True)
-        self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True))
+        self.linear = MixedLinear(hidden_size, patch_size * patch_size * out_channels, bias=True)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), MixedLinear(hidden_size, 2 * hidden_size, bias=True))
 
     def forward(self, x: Tensor, vec: Tensor) -> Tensor:
         shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
@@ -894,11 +925,11 @@ class Flux(nn.Module):
         self.hidden_size = params.hidden_size
         self.num_heads = params.num_heads
         self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.img_in = MixedLinear(self.in_channels, self.hidden_size, bias=True)
         self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
         self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
         self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
-        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+        self.txt_in = MixedLinear(params.context_in_dim, self.hidden_size)
 
         self.double_blocks = nn.ModuleList(
             [
@@ -1114,11 +1145,11 @@ class ControlNetFlux(nn.Module):
         self.hidden_size = params.hidden_size
         self.num_heads = params.num_heads
         self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
-        self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.img_in = MixedLinear(self.in_channels, self.hidden_size, bias=True)
         self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
         self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
         self.guidance_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if params.guidance_embed else nn.Identity()
-        self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
+        self.txt_in = MixedLinear(params.context_in_dim, self.hidden_size)
 
         self.double_blocks = nn.ModuleList(
             [
@@ -1151,15 +1182,15 @@ class ControlNetFlux(nn.Module):
         # add ControlNet blocks
         self.controlnet_blocks = nn.ModuleList([])
         for _ in range(controlnet_depth):
-            controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
+            controlnet_block = MixedLinear(self.hidden_size, self.hidden_size)
             controlnet_block = zero_module(controlnet_block)
             self.controlnet_blocks.append(controlnet_block)
         self.controlnet_blocks_for_single = nn.ModuleList([])
         for _ in range(controlnet_single_depth):
-            controlnet_block = nn.Linear(self.hidden_size, self.hidden_size)
+            controlnet_block = MixedLinear(self.hidden_size, self.hidden_size)
             controlnet_block = zero_module(controlnet_block)
             self.controlnet_blocks_for_single.append(controlnet_block)
-        self.pos_embed_input = nn.Linear(self.in_channels, self.hidden_size, bias=True)
+        self.pos_embed_input = MixedLinear(self.in_channels, self.hidden_size, bias=True)
         self.gradient_checkpointing = False
         self.input_hint_block = nn.Sequential(
             nn.Conv2d(3, 16, 3, padding=1),

library/train_util.py

@@ -4909,9 +4909,6 @@ def get_optimizer(args, trainable_params) -> tuple[str, str, object]:
     optimizer_type = optimizer_type.lower()
 
     if args.fused_backward_pass:
-        assert (
-            optimizer_type == "Adafactor".lower()
-        ), "fused_backward_pass currently only works with optimizer_type Adafactor / fused_backward_passは現在optimizer_type Adafactorでのみ機能します"
         assert (
             args.gradient_accumulation_steps == 1
         ), "fused_backward_pass does not work with gradient_accumulation_steps > 1 / fused_backward_passはgradient_accumulation_steps>1では機能しません"
@@ -5155,6 +5152,24 @@ def get_optimizer(args, trainable_params) -> tuple[str, str, object]:
         optimizer_class = transformers.optimization.Adafactor
         optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
 
+    elif optimizer_type.lower() == "adamoffload" or optimizer_type.lower() == "nadamoffload":
+        logger.info(f"use [N]AdamOffload optimizer | {optimizer_kwargs}")
+
+        optimizer_class = torch.optim.Adam
+        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+
+    elif optimizer_type.lower() == "adamwoffload" or optimizer_type.lower() == "nadamwoffload":
+        logger.info(f"use [N]AdamWOffload optimizer | {optimizer_kwargs}")
+
+        optimizer_class = torch.optim.AdamW  # default weight_decay seems to be 0.01
+        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+
+    elif optimizer_type.lower() == "adanoffload":
+        logger.info(f"use AdanOffload optimizer | {optimizer_kwargs}")
+
+        optimizer_class = torch.optim.AdamW  # todo: can't set beta3 here yet, need a custom Adan class
+        optimizer = optimizer_class(trainable_params, lr=lr, **optimizer_kwargs)
+
     elif optimizer_type == "AdamW".lower():
         logger.info(f"use AdamW optimizer | {optimizer_kwargs}")
         optimizer_class = torch.optim.AdamW