Added Adan offloading optimizer, fp32 params, and 'cautious' updates

flux_train.py

@@ -330,7 +330,7 @@ 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']
+    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
@@ -458,8 +458,23 @@ def train(args):
         # 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:
-            flux.final_layer.linear.to(dtype=torch.float32)  # Loses lower bits from some saved files,
-            flux.img_in            .to(dtype=torch.float32)  # but most saved models aren't f32/f16 anyway.
+
+            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)
@@ -501,6 +516,7 @@ def train(args):
         # use fused optimizer for backward pass. Only some specific optimizers are supported.
         import library.adafactor_fused
         import library.adamw_fused
+        import library.adan_fused
 
         if args.optimizer_type.lower() == "adafactor":
             library.adafactor_fused.patch_adafactor_fused(optimizer)
@@ -508,6 +524,8 @@ def train(args):
             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")
 

library/adamw_fused.py

@@ -135,6 +135,12 @@ def adamw_offload_step_param(self, p, group):
 
     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)
 

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

@@ -5068,6 +5068,12 @@ def get_optimizer(args, trainable_params) -> tuple[str, str, object]:
         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