summaryrefslogtreecommitdiff
path: root/bitsandbytes/optim/adam.py
blob: 363497171239bc40e7e783266e18e88fe11b5924 (plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import math
import os

import torch
import torch.distributed as dist

import bitsandbytes.functional as F
from bitsandbytes.optim.optimizer import Optimizer2State


class Adam(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        optim_bits=32,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
    ):
        super(Adam, self).__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            optim_bits,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
        )


class Adam8bit(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
    ):
        super(Adam8bit, self).__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            8,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
        )


class Adam32bit(Optimizer2State):
    def __init__(
        self,
        params,
        lr=1e-3,
        betas=(0.9, 0.999),
        eps=1e-8,
        weight_decay=0,
        amsgrad=False,
        args=None,
        min_8bit_size=4096,
        percentile_clipping=100,
        block_wise=True,
    ):
        super(Adam32bit, self).__init__(
            "adam",
            params,
            lr,
            betas,
            eps,
            weight_decay,
            32,
            args,
            min_8bit_size,
            percentile_clipping,
            block_wise,
        )


class AnalysisAdam(torch.optim.Optimizer):
    """Adam that performs 8-bit vs 32-bit 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)
                    elif self.analysis == "my-quantization-routine":
                        pass
                        # 1. get code
                        # 2. quantize
                        # 3. dequantize
                        # Error will be calculated automatically!
                    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 = os.path.join(
                                self.savedir, f"{p_id}_{shapestr}_abserr.pkl"
                            )
                            pathrele = os.path.join(
                                self.savedir, f"{p_id}_{shapestr}_relerr.pkl"
                            )
                            pathcounts = os.path.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