Copied over Analysis Adam.

bitsandbytes/optim/adam.py

@@ -26,3 +26,202 @@ class Adam32bit(Optimizer2State):
                 weight_decay, 32, args, min_8bit_size, percentile_clipping, block_wise)
 
 
+
+class AnalysisAdam(torch.optim.Optimizer):
+    """Implements 8-bit Adam and performs error analysis.
+
+    This implementation is modified from torch.optim.Adam based on:
+    `Fixed Weight Decay Regularization in Adam`
+    (see https://arxiv.org/abs/1711.05101)
+
+    It has been proposed in `Adam: A Method for Stochastic Optimization`_.
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+
+    .. _Adam\: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.999),
+        eps=1e-8,
+        weight_decay=0,
+        amsgrad=False,
+        bnb_analysis='dynamic-blockwise',
+        savedir=None
+    ):
+        defaults = dict(
+            lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad
+        )
+        super(AnalysisAdam, self).__init__(params, defaults)
+        self.analysis = bnb_analysis
+        self.savedir = savedir
+
+    @property
+    def supports_memory_efficient_fp16(self):
+        return True
+
+    @property
+    def supports_flat_params(self):
+        return True
+
+    def 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_id, p in enumerate(group["params"]):
+                if p.grad is None:
+                    continue
+                grad = p.grad.data
+                if grad.dtype in {torch.float16, torch.bfloat16}:
+                    grad = grad.float()
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        "Adam does not support sparse gradients, please consider SparseAdam instead"
+                    )
+                amsgrad = group.get("amsgrad", False)
+                assert not amsgrad
+
+                p_data_fp32 = p.data
+                if p.data.dtype in {torch.float16, torch.bfloat16}:
+                    p_data_fp32 = p_data_fp32.float()
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state["step"] = 0
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(p_data_fp32)
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+                    state['abserrors'] = torch.zeros((256, 256), device=p_data_fp32.device)
+                    state['relerrors'] = torch.zeros((256, 256), device=p_data_fp32.device)
+                    state['counts'] = torch.zeros((256, 256), device=p_data_fp32.device)
+                    if amsgrad:
+                        # Maintains max of all exp. moving avg. of sq. grad. values
+                        state["max_exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+                else:
+                    state["exp_avg"] = state["exp_avg"].to(p_data_fp32)
+                    state["exp_avg_sq"] = state["exp_avg_sq"].to(p_data_fp32)
+                    if amsgrad:
+                        state["max_exp_avg_sq"] = state["max_exp_avg_sq"].to(
+                            p_data_fp32
+                        )
+
+                state["step"] += 1
+                beta1, beta2 = group["betas"]
+                bias_correction1 = 1 - beta1 ** state["step"]
+                bias_correction2 = 1 - beta2 ** state["step"]
+                step_size = group["lr"] * math.sqrt(bias_correction2) / bias_correction1
+                e = state['abserrors']
+                rele = state['relerrors']
+                counts = state['counts']
+
+                if group["weight_decay"] != 0:
+                    p_data_fp32.add_(
+                        p_data_fp32, alpha=-group["weight_decay"] * group["lr"]
+                    )
+
+                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+                if amsgrad:
+                    max_exp_avg_sq = state["max_exp_avg_sq"]
+
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+                denom = exp_avg_sq.sqrt().add_(group["eps"])
+                update_fp32 = exp_avg/denom
+
+                if p_data_fp32.numel() <= 8192 or p_data_fp32.numel() > 50000*1000:
+                    # embedding layer or too small
+                    p_data_fp32 += -step_size*update_fp32
+                else:
+                    if self.analysis == 'dynamic-blockwise':
+                        code1 = F.create_dynamic_map(signed=True).to(p.device)
+                        code2 = F.create_dynamic_map(signed=False).to(p.device)
+                        C1, S1 = F.quantize_blockwise(exp_avg, code=code1)
+                        state1 = F.dequantize_blockwise(C1, S1)
+                        C2, S2 = F.quantize_blockwise(exp_avg_sq, code=code2)
+                        state2 = F.dequantize_blockwise(C2, S2)
+                    elif self.analysis == 'dynamic':
+                        code1 = F.create_dynamic_map(signed=True).to(p.device)
+                        code2 = F.create_dynamic_map(signed=False).to(p.device)
+                        C1, S1 = F.quantize(exp_avg, code=code1)
+                        state1 = F.dequantize(C1, S1)
+                        C2, S2 = F.quantize(exp_avg_sq, code=code2)
+                        state2 = F.dequantize(C2, S2)
+                    elif self.analysis == 'linear':
+                        code1 = F.create_linear_map(signed=True).to(p.device)
+                        code2 = F.create_linear_map(signed=False).to(p.device)
+                        C1, S1 = F.quantize(exp_avg, code=code1)
+                        state1 = F.dequantize(C1, S1)
+                        C2, S2 = F.quantize(exp_avg_sq, code=code2)
+                        state2 = F.dequantize(C2, S2)
+                    elif self.analysis == 'quantile':
+                        code1 = F.estimate_quantiles(exp_avg)
+                        code2 = F.estimate_quantiles(exp_avg_sq)
+                        C1 = F.quantize_no_absmax(exp_avg, code=code1)
+                        state1 = F.dequantize_no_absmax(C1, code1)
+                        C2 = F.quantize_no_absmax(exp_avg_sq, code=code2)
+                        state2 = F.dequantize_no_absmax(C2, code2)
+                    else:
+                        raise ValueError(f'Invalid analysis value: {self.analysis}!')
+
+                    denom = state2.sqrt().add_(group["eps"])
+                    update_8bit = state1/denom
+
+                    abserr = torch.abs(update_8bit-update_fp32)
+                    relerr = abserr/torch.abs(update_fp32+1e-6)
+
+                    C1, C2 = C1.int(), C2.int()
+
+                    F.histogram_scatter_add_2d(e, C1.int(), C2.int(), abserr)
+                    F.histogram_scatter_add_2d(rele, C1.int(), C2.int(), relerr)
+                    F.histogram_scatter_add_2d(counts, C1.int(), C2.int(), torch.ones_like(abserr))
+
+                    p_data_fp32 += -step_size*update_fp32
+
+
+                    if not dist.is_initialized() or dist.get_rank() == 0:
+                        if self.savedir != '' and state['step'] % 100 == 0:
+                            if not os.path.exists(self.savedir): os.makedirs(self.savedir)
+                            shapestr = '_'.join([str(dim) for dim in p_data_fp32.shape])
+                            pathe = join(self.savedir, f'{p_id}_{shapestr}_abserr.pkl')
+                            pathrele = join(self.savedir, f'{p_id}_{shapestr}_relerr.pkl')
+                            pathcounts = join(self.savedir, f'{p_id}_{shapestr}_counts.pkl')
+                            torch.save(e, pathe)
+                            torch.save(rele, pathrele)
+                            torch.save(counts, pathcounts)
+
+                if p.data.dtype in {torch.float16, torch.bfloat16}:
+                    p.data.copy_(p_data_fp32)
+
+
+
+        return loss