Text Generation
Transformers
PyTorch
Safetensors
English
stripedhyena
custom_code
File size: 17,037 Bytes
230c4b6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
# Copyright (c) Together
# This software is distributed under the terms of the Apache License, Version 2.0
# Author: Michael Poli
# Note: MP and PP utilities are removed for ease of use and editing.

import torch
import torch.nn as nn
import torch.nn.functional as F

from .utils import print_rank_0, column_split
from .cache import InferenceParams, RecurrentInferenceParams
from .engine import HyenaInferenceEngine
from .layers import (
    RMSNorm,
    ParallelGatedMLP,
    VocabParallelEmbedding,
)

try:
    from flash_attn.modules.mha import MHA
except ImportError:
    "flash_attn not installed"


class AttentionBlock(nn.Module):
    def __init__(self, config, layer_idx) -> None:
        super().__init__()
        self.config = config
        self.pre_norm, self.post_norm = RMSNorm(config), RMSNorm(config)
        self.layer_idx = layer_idx
        self.proj_groups = config.get("proj_groups", 1)
        dtype = config.get("attn_block_dtype", torch.bfloat16)
        mlp_dtype = config.get("mlp_dtype", torch.bfloat16)
        self.num_attention_heads = config.num_attention_heads
        self.hidden_size_per_attention_head = config.hidden_size // config.num_attention_heads

        self.counter = 0
        self.inner_mha_cls = MHA(
            embed_dim=config.hidden_size,
            num_heads=config.num_attention_heads,
            num_heads_kv=config.num_attention_heads // self.proj_groups,
            rotary_emb_dim=config.hidden_size // config.num_attention_heads,
            qkv_proj_bias=config.get("qkv_proj_bias", True),
            rotary_emb_base=config.get("rotary_emb_base", 10000),
            causal=True,
            layer_idx=layer_idx,
            out_proj_bias=config.get("mha_out_proj_bias", True),
            use_flash_attn=self.config.use_flash_attn,
        ).to(dtype=dtype)

        if self.config.get("smeared_gqa", False):
            self.inner_mha_cls.num_heads_kv = self.inner_mha_cls.num_heads
        self.inner_mha_cls.rotary_emb.register_buffer(
            "inv_freq", self.inner_mha_cls.rotary_emb.inv_freq
        )

        self.mlp = ParallelGatedMLP(config).to(dtype=mlp_dtype)

    def forward(self, u, inference_params=None, padding_mask=None, *args, **kwargs):
        if (
            type(padding_mask) == torch.Tensor
        ):  # workaround for masking bug in FA. This works because Wqkv does not have bias
            # and attention scores will be also automatically zeroed.
            u = u * padding_mask[..., None]

        u = (
            self.inner_mha_cls(
                self.pre_norm(u),
                inference_params=inference_params,
            )
            + u
        )
        if type(padding_mask) == torch.Tensor:  # guard against bias
            u = u * padding_mask[..., None]
        u = self.mlp(self.post_norm(u)) + u
        return u, None


class ParallelHyenaFilter(nn.Module):
    def __init__(self, config, layer_idx) -> None:
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hyena_filter_groups = config.get("hyena_filter_groups", self.config.hidden_size)

        self.use_flashfft = config.get("use_flashfft", False)
        self.state_size = config.state_size
        self.hidden_size = config.hidden_size
        self.num_filters = config.num_filters
        self.inference_mode = config.get("inference_mode", True)
        self.counter = 0
        self.column_split_hyena = config.get("column_split_hyena", True)

        assert self.hidden_size % self.num_filters == 0 and self.num_filters <= self.hidden_size

        self.D = nn.Parameter(torch.zeros(self.hidden_size))

        # attention heads are not used except to split post short_filter
        # projections in the same way as the checkpoint
        self.num_attention_heads = config.num_attention_heads
        self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads

        # after preprocessing here we can save the new checkpoint
        self.short_filter_length = config.short_filter_length
        self.short_filter_weight = nn.Parameter(
            torch.randn(3 * config.hidden_size, 1, config.short_filter_length)
        )
        self.short_filter_bias = (
            nn.Parameter(torch.randn(3 * config.hidden_size)) if config.short_filter_bias else None
        )

        self.engine = HyenaInferenceEngine(layer_idx=layer_idx)
        self.use_flash_depthwise = config.get("use_flash_depthwise", False)
        self.data_dtype = None

        if self.use_flash_depthwise:
            self.fir_fn = FlashDepthwiseConv1d(
                channels=3 * self.hidden_size,
                kernel_size=self.short_filter_length,
                padding=self.short_filter_length - 1,
                weights=self.short_filter_weight,
                bias=self.short_filter_bias,
                device=None,
                dtype=self.config.get("depthwise_dtype", torch.bfloat16),
            )
        else:
            self.fir_fn = F.conv1d

        self.fftconv_fn = None
        self.long_fir_threshold = config.get("long_fir_threshold", None)
        if self.long_fir_threshold is not None:
            assert (
                self.use_flashfft is False
            ), "long_fir_threshold not compatible with fused flashfft"

        self.num_systems = self.hidden_size // self.hyena_filter_groups
        self.poles = nn.Parameter(torch.randn(self.num_systems, self.state_size, 1, 2))
        self.residues = nn.Parameter(torch.randn(self.num_systems, self.state_size, 1, 2))
        self.h = None

    def forward(self, u, inference_params=None, padding_mask=None, *args, **kwargs):
        if (
            inference_params is not None
            and self.layer_idx in inference_params.fir_state_dict.keys()
        ):
            return self.sequential_forward(u, inference_params)

        else:
            return self.parallel_forward(u, inference_params, padding_mask)

    def parallel_forward(self, u, inference_params=None, padding_mask=None):
        L = u.shape[1]
        z_pre, fir_state = self.engine.parallel_fir(
            self.fir_fn,
            u,
            self.short_filter_weight,
            self.short_filter_bias,
            L,
            fir_length=self.short_filter_length,
            inference_params=inference_params,
            padding_mask=padding_mask,
        )
        if inference_params:
            inference_params.fir_state_dict[self.layer_idx] = fir_state

        if self.h is None:
            h, filter_dtype, poles, residues = self.compute_filter(L, u.device)
        else:
            h = self.h
            filter_dtype = self.h.dtype

        if self.hyena_filter_groups > 1:
            h = h.repeat_interleave(self.hidden_size // self.hyena_filter_groups, 1)

        # if inference_params is not None, we plan to perform generation:
        # prefilling for the IIR portion of the filter is handled by the engine.
        dims = (
            self.hidden_size,
            self.num_attention_heads,
            self.hidden_size_per_attention_head,
            self.state_size,
            self.hyena_filter_groups,
        )
        y = self.engine.parallel_iir(
            z_pre,
            h,
            self.D,
            L,
            t=self.t,
            poles=self.poles,
            dims=dims,
            inference_params=inference_params,
            layer_idx=self.layer_idx,
            prefill_style=self.config.get("prefill_style", "fft"),
            use_flashfft=self.use_flashfft,
            fftconv_fn=self.fftconv_fn,
            column_split_hyena=self.column_split_hyena,
            long_fir_threshold=self.long_fir_threshold,
            padding_mask=padding_mask,
        )

        return y, inference_params

    def sequential_forward(self, u, inference_params):
        if self.data_dtype is None:
            self.data_dtype = u.dtype
        if len(u.shape) > 2:
            u = u[:, -1]

        fir_state, iir_state = (
            inference_params.fir_state_dict[self.layer_idx],
            inference_params.state_dict[self.layer_idx],
        )

        z_pre, fir_state = self.engine.step_fir(
            u, fir_state, weight=self.short_filter_weight, bias=self.short_filter_bias
        )
        x2, x1, v = (
            column_split(z_pre, self.num_attention_heads, self.hidden_size_per_attention_head)
            if self.column_split_hyena
            else z_pre.split([self.hidden_size, self.hidden_size, self.hidden_size], dim=1)
        )

        y, iir_state = self.engine.step_iir(
            x2,
            x1,
            v,
            self.D,
            self.residues,
            self.poles,
            iir_state,
            iir_groups=self.hyena_filter_groups,
        )

        inference_params.fir_state_dict[self.layer_idx] = fir_state
        inference_params.state_dict[self.layer_idx] = iir_state
        y = y.to(dtype=self.data_dtype)
        return y[:, None], inference_params

    def update_time(self, L, device):
        """
        Set [0, 1, ..., L-1] where L is the length of the current batch of inputs.
        If L is greater than the length of the previous batch, then the time vector is
        reinitialized. Otherwise, the time vector is truncated from cache.
        """
        if not hasattr(self, "t"):
            self.t = torch.arange(L, device=device)[None, None]
        elif self.t.shape[-1] < L:
            self.t = torch.arange(L, device=device)[None, None]
        else:
            self.t = self.t[..., :L]

    def compute_filter(self, L, device):
        self.update_time(L, device)
        filter_dtype = torch.float32
        residues, log_poles = (
            torch.view_as_complex(self.residues.to(filter_dtype)),
            torch.view_as_complex(self.poles.to(filter_dtype)).log(),
        )
        h = (residues * (log_poles * self.t).exp()).real.sum(1)[None]
        return h, filter_dtype, log_poles, residues


class ParallelGatedConvBlock(nn.Module):
    def __init__(self, config, layer_idx) -> None:
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        dtype = config.get("hyena_block_dtype", torch.float32)
        mlp_dtype = config.get("mlp_dtype", torch.bfloat16)
        self.pre_norm, self.post_norm = RMSNorm(config).to(dtype=dtype), RMSNorm(config).to(
            dtype=dtype
        )
        self.filter = ParallelHyenaFilter(config, layer_idx).to(dtype=dtype)
        self.projections = nn.Linear(config.hidden_size, 3 * config.hidden_size)
        self.out_filter_dense = nn.Linear(config.hidden_size, config.hidden_size).to(dtype)
        self.mlp = ParallelGatedMLP(config).to(dtype=mlp_dtype)

    def forward(self, u, inference_params=None, padding_mask=None, *args, **kwargs):
        z = self.projections(self.pre_norm(u))
        if type(padding_mask) == torch.Tensor:  # guard against bias
            z = z * padding_mask[..., None]

        z, inference_params = self.filter(
            z, inference_params=inference_params, padding_mask=padding_mask
        )

        u = self.out_filter_dense(z) + u
        if type(padding_mask) == torch.Tensor:  # guard against bias
            u = u * padding_mask[..., None]
        u = self.mlp(self.post_norm(u)) + u
        return u, inference_params


def get_block(config, layer_idx, flash_fft=None):
    if layer_idx in config.attn_layer_idxs:
        return AttentionBlock(config, layer_idx)
    elif layer_idx in config.hyena_layer_idxs:
        block = ParallelGatedConvBlock(config, layer_idx)
        if config.get("use_flashfft", "False"):
            block.filter.fftconv_fn = flash_fft
        return block
    else:
        raise NotImplementedError


class StripedHyena(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.embedding_layer = VocabParallelEmbedding(config)
        self.norm = RMSNorm(config) if config.get("final_norm", True) else None
        self.unembed = self.emb if config.tie_embeddings else VocabParallelEmbedding(config)
        self.gradient_checkpointing = False
        
        if config.get("use_flashfft", "False"):
            raise NotImplementedError("Please use standalone SH code for other custom kernels")
        else:
            self.flash_fft = None

        self.blocks = nn.ModuleList(
            get_block(config, layer_idx, flash_fft=self.flash_fft)
            for layer_idx in range(config.num_layers)
        )

    def forward(self, x, inference_params_dict=None, padding_mask=None):
        L = x.shape[1]
        x = self.embedding_layer.embed(x)
        if inference_params_dict is not None:
            x, inference_params_dict_out = self.stateful_forward(
                x,
                inference_params_dict=inference_params_dict,
            )
        else:
            x, inference_params_dict_out = self.stateless_forward(x, padding_mask=padding_mask)
        x = self.norm(x)
        x = self.unembed.unembed(x)
        return x, inference_params_dict_out

    def stateful_forward(self, x, inference_params_dict=None):
        for block_idx, block in enumerate(self.blocks):
            block_name = "mha" if block_idx in self.config.attn_layer_idxs else "hyena"
            inference_params = inference_params_dict[block_name]
            x, _ = block(x, inference_params=inference_params)

        return x, inference_params_dict

    def stateless_forward(self, x, padding_mask=None):
        if type(padding_mask) == torch.Tensor:
            x = x * padding_mask[..., None]

        for block_idx, block in enumerate(self.blocks):
            if self.gradient_checkpointing and self.training:
                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, inference_params=None, padding_mask=padding_mask)

                    return custom_forward

                x, _ = checkpoint(create_custom_forward(block), x, use_reentrant=False)
            else:
                x, _ = block(x, inference_params=None, padding_mask=padding_mask)
        return x, None

    def initialize_inference_params(self):
        print_rank_0("Initializing inference params...")
        inference_params_dict = {
            "mha": InferenceParams(
                max_seqlen=self.config.get("max_seqlen", 8192),
                max_batch_size=self.config.get("max_batch_size", 1),
                seqlen_offset=0,
            ),
            "hyena": RecurrentInferenceParams(
                fir_filter_length=self.config.short_filter_length,
                state_dim=self.config.state_size,
                seqlen_offset=0,
            ),
        }
        return inference_params_dict

    def precompute_filters(self, L, device):
        for block_idx, block in enumerate(self.blocks):
            if type(block) == ParallelGatedConvBlock:
                if type(block.filter) == ParallelHyenaFilter:
                    L = block.filter.long_fir_threshold or L
                    print_rank_0(f"Precomputing filters, L={L}...")

                    filter_dtype = torch.float16 if L >= 2048 else torch.float32

                    block.filter._set_time(L, device)
                    residues, poles = (
                        torch.view_as_complex(block.filter.residues.to(torch.float16)),
                        torch.view_as_complex(block.filter.poles.to(torch.float16)),
                    )

                    block.filter.h = (residues * poles**block.filter.t).real.sum(1)[None]
                    block.filter.h = block.filter.h.to(dtype=filter_dtype)

    def load_poles_residues(self, path):
        "Load different poles and residues for each layer."
        for block_idx, block in enumerate(self.blocks):
            if type(block) == ParallelGatedConvBlock:
                if type(block.filter) == ParallelHyenaFilter:
                    print(f"Loading poles and residues for block {block_idx}")
                    poles = torch.load(path + f"/approx_poles_{block_idx+1}.pt", map_location="cpu")
                    poles = torch.view_as_real(poles)
                    residues = torch.load(
                        path + f"/approx_residues_{block_idx+1}.pt", map_location="cpu"
                    )
                    residues = torch.view_as_real(residues)
                    poles = poles.permute(1, 0, 2).unsqueeze(-2)
                    residues = residues.permute(1, 0, 2).unsqueeze(-2)

                    block.filter.poles = nn.Parameter(poles)
                    block.filter.residues = nn.Parameter(residues)

    def to_bfloat16_except_poles_residues(self):
        """Convert all parameters to bfloat16 except for the poles and residues.

        Particularly important for longer prompts.
        """
        for k, p in self.named_parameters():
            if "poles" not in k and "residues" not in k:
                p.data = p.data.to(torch.bfloat16)