linear.py 46.4 KB
Newer Older
1
from abc import abstractmethod
2
from typing import Dict, List, Optional, Tuple
3
4
5

import torch
import torch.nn.functional as F
6
from torch.nn.parameter import Parameter, UninitializedParameter
7

8
9
10
11
12
from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                              get_tensor_model_parallel_world_size,
                              split_tensor_along_last_dim,
                              tensor_model_parallel_all_gather,
                              tensor_model_parallel_all_reduce)
13
from vllm.logger import init_logger
14
15
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig, QuantizeMethodBase)
16
from vllm.model_executor.parameter import (BasevLLMParameter,
17
18
                                           PackedvLLMParameter,
                                           PerTensorScaleParameter)
19
20
21
22
from vllm.model_executor.utils import set_weight_attrs

logger = init_logger(__name__)

23
WEIGHT_LOADER_V2_SUPPORTED = [
24
    "CompressedTensorsLinearMethod", "AWQMarlinLinearMethod",
25
    "AWQLinearMethod", "GPTQMarlinLinearMethod", "Fp8LinearMethod",
26
27
    "MarlinLinearMethod", "QQQLinearMethod", "GPTQMarlin24LinearMethod",
    "TPUInt8LinearMethod"
28
]
29

30

31
32
33
34
35
36
37
38
def adjust_marlin_shard(param, shard_size, shard_offset):
    marlin_tile_size = getattr(param, "marlin_tile_size", None)
    if marlin_tile_size is None:
        return shard_size, shard_offset

    return shard_size * marlin_tile_size, shard_offset * marlin_tile_size


39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
def adjust_bitsandbytes_shard(param: Parameter,
                              qkv_offsets: Dict[str, Tuple[int, int]],
                              loaded_shard_id: str) -> Tuple[int, int]:
    """Adjust the quantization offsets and sizes for BitsAndBytes sharding."""

    total, _ = qkv_offsets["total"]
    orig_offset, orig_size = qkv_offsets[loaded_shard_id]

    quantized_total = param.data.shape[0]
    quantized_offset = orig_offset * quantized_total // total
    quantized_size = orig_size * quantized_total // total

    return quantized_size, quantized_offset


54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
def adjust_scalar_to_fused_array(param, loaded_weight, shard_id):
    """For fused modules (QKV and MLP) we have an array of length
    N that holds 1 scale for each "logical" matrix. So the param
    is an array of length N. The loaded_weight corresponds to 
    one of the shards on disk. Here, we slice the param based on 
    the shard_id for loading.
    """
    qkv_idxs = {"q": 0, "k": 1, "v": 2}

    if isinstance(shard_id, str):
        shard_id = qkv_idxs[shard_id]
    elif not isinstance(shard_id, int):
        raise ValueError(f"Unknown Shard Id {shard_id}")

    # AutoFP8 scales do not have a shape
    # compressed-tensors scales do have a shape
    if len(loaded_weight.shape) != 0:
        assert loaded_weight.shape[0] == 1
        loaded_weight = loaded_weight[0]

    return param[shard_id], loaded_weight


77
class LinearMethodBase(QuantizeMethodBase):
78
79
80
    """Base class for different (maybe quantized) linear methods."""

    @abstractmethod
81
82
    def create_weights(self, layer: torch.nn.Module,
                       input_size_per_partition: int,
James Fleming's avatar
James Fleming committed
83
                       output_partition_sizes: List[int], input_size: int,
84
85
                       output_size: int, params_dtype: torch.dtype,
                       **extra_weight_attrs):
86
87
        """Create weights for a linear layer. 
           The weights will be set as attributes of the layer.
88

89
90
91
92
93
94
95
96
97
98
        Args:
            layer: The layer that is using the LinearMethodBase factory.
            input_size_per_partition: Size of the weight input dim on rank X.
            output_partition_sizes: Sizes of the output dim of each logical 
                weight on rank X. E.g., output_partition_sizes for QKVLinear
                is a list contains the width of Wq, Wk, Wv on rank X.
            input_size: Size of the input dim of the weight across all ranks.
            output_size: Size of the output dim of the weight across all ranks.
            params_dtype: Datatype of the parameters.
        """
99
100
101
        raise NotImplementedError

    @abstractmethod
102
103
104
105
    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
106
107
        """Apply the weights in layer to the input tensor.
        Expects create_weights to have been called before on the layer."""
108
109
110
111
        raise NotImplementedError


class UnquantizedLinearMethod(LinearMethodBase):
112
    """Linear method without quantization."""
113

114
115
    def create_weights(self, layer: torch.nn.Module,
                       input_size_per_partition: int,
James Fleming's avatar
James Fleming committed
116
                       output_partition_sizes: List[int], input_size: int,
117
118
                       output_size: int, params_dtype: torch.dtype,
                       **extra_weight_attrs):
119
        weight = Parameter(torch.empty(sum(output_partition_sizes),
CHU Tianxiang's avatar
CHU Tianxiang committed
120
                                       input_size_per_partition,
121
122
123
                                       dtype=params_dtype),
                           requires_grad=False)
        set_weight_attrs(weight, {"input_dim": 1, "output_dim": 0})
124
125
        layer.register_parameter("weight", weight)
        set_weight_attrs(weight, extra_weight_attrs)
126

127
128
129
130
    def apply(self,
              layer: torch.nn.Module,
              x: torch.Tensor,
              bias: Optional[torch.Tensor] = None) -> torch.Tensor:
131
132

        return F.linear(x, layer.weight, bias)
133
134


135
136
class LinearBase(torch.nn.Module):
    """Base linear layer.
137
138
139
140
141
142
143

    Args:
        input_size: input dimension of the linear layer.
        output_size: output dimension of the linear layer.
        bias: If true, add bias.
        skip_bias_add: If true, skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
144
        quant_config: Quantization configure.
145
146
147
148
149
150
151
152
    """

    def __init__(
        self,
        input_size: int,
        output_size: int,
        skip_bias_add: bool = False,
        params_dtype: Optional[torch.dtype] = None,
153
        quant_config: Optional[QuantizationConfig] = None,
154
        prefix: str = "",
155
156
157
158
159
160
161
162
163
164
    ):
        super().__init__()

        # Keep input parameters
        self.input_size = input_size
        self.output_size = output_size
        self.skip_bias_add = skip_bias_add
        if params_dtype is None:
            params_dtype = torch.get_default_dtype()
        self.params_dtype = params_dtype
165
        if quant_config is None:
166
167
            self.quant_method: Optional[
                QuantizeMethodBase] = UnquantizedLinearMethod()
168
        else:
169
170
            self.quant_method = quant_config.get_quant_method(self,
                                                              prefix=prefix)
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        raise NotImplementedError


class ReplicatedLinear(LinearBase):
    """Replicated linear layer.

    Args:
        input_size: input dimension of the linear layer.
        output_size: output dimension of the linear layer.
        bias: If true, add bias.
        skip_bias_add: If true, skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
        quant_config: Quantization configure.
186
187
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj)
188
189
    """

190
191
192
193
194
195
    def __init__(self,
                 input_size: int,
                 output_size: int,
                 bias: bool = True,
                 skip_bias_add: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
196
                 quant_config: Optional[QuantizationConfig] = None,
197
198
199
200
201
202
203
                 prefix: str = ""):
        super().__init__(input_size,
                         output_size,
                         skip_bias_add,
                         params_dtype,
                         quant_config,
                         prefix=prefix)
204

205
206
        # All the linear layer supports quant method.
        assert self.quant_method is not None
207
208
209
210
211
        self.quant_method.create_weights(self,
                                         self.input_size, [self.output_size],
                                         self.input_size,
                                         self.output_size,
                                         self.params_dtype,
212
                                         weight_loader=self.weight_loader)
213

214
215
        if bias:
            self.bias = Parameter(
216
                torch.empty(self.output_size, dtype=self.params_dtype))
217
218
219
220
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
221
222
223
        else:
            self.register_parameter("bias", None)

224
225
226
227
228
229
230
231
232
    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
        # If the weight on disk does not have a shape, give it one
        # (such scales for AutoFp8).
        if len(loaded_weight.shape) == 0:
            loaded_weight = loaded_weight.reshape(1)

        assert param.size() == loaded_weight.size()
        param.data.copy_(loaded_weight)

233
234
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        bias = self.bias if not self.skip_bias_add else None
235
        assert self.quant_method is not None
236
        output = self.quant_method.apply(self, x, bias)
237
238
239
        output_bias = self.bias if self.skip_bias_add else None
        return output, output_bias

240
241
242
243
244
245
    def extra_repr(self) -> str:
        s = f"in_features={self.input_size}"
        s += f", output_features={self.output_size}"
        s += f", bias={self.bias is not None}"
        return s

246

247
class ColumnParallelLinear(LinearBase):
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
    """Linear layer with column parallelism.

    The linear layer is defined as Y = XA + b. A is parallelized along
    its second dimension as A = [A_1, ..., A_p].

    Args:
        input_size: first dimension of matrix A.
        output_size: second dimension of matrix A.
        bias: If true, add bias.
        gather_output: If true, call all-gather on output and make Y available
                       to all GPUs, otherwise, every GPU will have its output
                       which is Y_i = XA_i
        skip_bias_add: This was added to enable performance optimizations where
                       bias can be fused with other element-wise operations. we
                       skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
264
        quant_config: Quantization configure.
James Fleming's avatar
James Fleming committed
265
266
        output_sizes: list of output sizes packed into one output, like for QKV
                       the list would be size 3.
267
268
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj) 
269
270
    """

271
272
273
274
275
276
277
278
    def __init__(self,
                 input_size: int,
                 output_size: int,
                 bias: bool = True,
                 gather_output: bool = False,
                 skip_bias_add: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
                 quant_config: Optional[QuantizationConfig] = None,
279
                 output_sizes: Optional[List[int]] = None,
280
                 prefix: str = ""):
281
        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
282
                         quant_config, prefix)
283
284

        self.gather_output = gather_output
285

286
287
        # Divide the weight matrix along the last dimension.
        tp_size = get_tensor_model_parallel_world_size()
288
289
290
291
292
293
294
295
296
297
        assert self.quant_method is not None
        self.output_size_per_partition = divide(self.output_size, tp_size)
        self.output_partition_sizes = [self.output_size_per_partition]
        # If QKV or MergedColumn, use output size of each partition.
        if hasattr(self, "output_sizes"):
            self.output_partition_sizes = [
                divide(output_size, tp_size)
                for output_size in self.output_sizes
            ]

James Fleming's avatar
James Fleming committed
298
299
        if output_sizes is None:
            output_sizes = [output_size]
300

301
302
303
304
305
306
307
        self.quant_method.create_weights(
            layer=self,
            input_size_per_partition=self.input_size,
            output_partition_sizes=self.output_partition_sizes,
            input_size=self.input_size,
            output_size=self.output_size,
            params_dtype=self.params_dtype,
308
309
            weight_loader=(
                self.weight_loader_v2 if self.quant_method.__class__.__name__
310
                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
311
312
313
314
315
316
317
318
319
320
321
322
323
324
        if bias:
            self.bias = Parameter(
                torch.empty(self.output_size_per_partition,
                            dtype=params_dtype))
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
        else:
            self.register_parameter("bias", None)

    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
        tp_rank = get_tensor_model_parallel_rank()
        output_dim = getattr(param, "output_dim", None)
325
326
327
328
329
330
331
332
333
334
335

        # Special case for GGUF
        is_gguf_weight = getattr(param, "is_gguf_weight", False)
        is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
        if is_gguf_weight_type:
            param.weight_type = loaded_weight.item()

        # Materialize GGUF UninitializedParameter
        if is_gguf_weight and isinstance(param, UninitializedParameter):
            param.materialize(loaded_weight.shape, dtype=loaded_weight.dtype)

336
337
338
339
340
341
        param_data = param.data
        if output_dim is not None:
            shard_size = param_data.shape[output_dim]
            start_idx = tp_rank * shard_size
            loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                 shard_size)
342
343
344
345
346

        # Special case for loading scales off disk, which often do not
        # have a shape (such as in the case of AutoFP8).
        if len(loaded_weight.shape) == 0:
            loaded_weight = loaded_weight.reshape(1)
347

348
349
350
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

351
    def weight_loader_v2(self, param: Parameter, loaded_weight: torch.Tensor):
352
353
354
355
356
        # Special case for loading scales off disk, which often do not
        # have a shape (such as in the case of AutoFP8).
        if len(loaded_weight.shape) == 0:
            assert loaded_weight.numel() == 1
            loaded_weight = loaded_weight.reshape(1)
357
358
        param.load_column_parallel_weight(loaded_weight=loaded_weight)

359
360
361
362
    def forward(self, input_):
        bias = self.bias if not self.skip_bias_add else None

        # Matrix multiply.
363
        assert self.quant_method is not None
364
        output_parallel = self.quant_method.apply(self, input_, bias)
365
366
367
368
369
370
371
372
        if self.gather_output:
            # All-gather across the partitions.
            output = tensor_model_parallel_all_gather(output_parallel)
        else:
            output = output_parallel
        output_bias = self.bias if self.skip_bias_add else None
        return output, output_bias

373
374
375
376
377
378
379
380
    def extra_repr(self) -> str:
        s = f"in_features={self.input_size}"
        s += f", output_features={self.output_size_per_partition}"
        s += f", bias={self.bias is not None}"
        s += f", tp_size={get_tensor_model_parallel_world_size()}"
        s += f", gather_output={self.gather_output}"
        return s

381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399

class MergedColumnParallelLinear(ColumnParallelLinear):
    """Packed linear layers with column parallelism.

    Similar to ColumnParallelLinear, but the weight matrix is concatenated
    along the output dimension. When the weight matrix is loaded, the
    different partitions are sharded separately.

    Args:
        input_size: input dimension of the linear layer.
        output_sizes: list of output dimensions of the linear layer.
        bias: If true, add bias.
        gather_output: If true, call all-gather on output and make the output
                       available to all GPUs, otherwise, every GPU will have
                       its own output.
        skip_bias_add: This was added to enable performance optimizations where
                       bias can be fused with other element-wise operations. we
                       skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
400
        quant_config: Quantization configure.
401
402
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj)
403
404
    """

405
406
407
408
409
410
411
    def __init__(self,
                 input_size: int,
                 output_sizes: List[int],
                 bias: bool = True,
                 gather_output: bool = False,
                 skip_bias_add: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
412
                 quant_config: Optional[QuantizationConfig] = None,
413
                 prefix: str = ""):
414
415
416
        self.output_sizes = output_sizes
        tp_size = get_tensor_model_parallel_world_size()
        assert all(output_size % tp_size == 0 for output_size in output_sizes)
417
418
419
420
421
422
        super().__init__(input_size=input_size,
                         output_size=sum(output_sizes),
                         bias=bias,
                         gather_output=gather_output,
                         skip_bias_add=skip_bias_add,
                         params_dtype=params_dtype,
423
424
                         quant_config=quant_config,
                         prefix=prefix)
425
426
427
428
429

    def weight_loader(self,
                      param: Parameter,
                      loaded_weight: torch.Tensor,
                      loaded_shard_id: Optional[int] = None):
James Fleming's avatar
James Fleming committed
430

431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
        # Special case for GGUF
        # initialize GGUF param after we know the quantize type
        is_gguf_weight = getattr(param, "is_gguf_weight", False)
        is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
        if is_gguf_weight_type:
            param.data[loaded_shard_id].copy_(loaded_weight)
            param.shard_weight_type[loaded_shard_id] = loaded_weight.item()
            return

        if is_gguf_weight and isinstance(param, UninitializedParameter):
            from gguf.constants import GGML_QUANT_SIZES

            ori_shape = param.tensor_shape
            weight_types = self.qweight_type.shard_weight_type.values()
            row_size = []
            for weight_type in weight_types:
                block_size, type_size = GGML_QUANT_SIZES[weight_type]
                row_size.append(ori_shape[1] // block_size * type_size)
            q_shape = (ori_shape[0], max(row_size))
            param.materialize(q_shape, dtype=loaded_weight.dtype)

452
453
        param_data = param.data
        output_dim = getattr(param, "output_dim", None)
454
        # Special case for AQLM codebooks.
James Fleming's avatar
James Fleming committed
455
        is_metadata = getattr(param, "is_metadata", False)
456
457
        # Special case for per-tensor scale to load scalar into fused array.
        needs_scalar_to_array = getattr(param, "needs_scalar_to_array", False)
458

459
        if loaded_shard_id is None:
460
            # Loaded weight is already fused on disk (qkv/mlp).
461
            if output_dim is None:
462
                if needs_scalar_to_array:
463
464
                    param_data, loaded_weight = adjust_scalar_to_fused_array(
                        param_data, loaded_weight, 0)
465

466
467
468
469
                assert param_data.shape == loaded_weight.shape
                param_data.copy_(loaded_weight)
                return
            current_shard_offset = 0
470
            shard_offsets: List[Tuple[int, int, int]] = []
471
472
473
474
475
            for i, output_size in enumerate(self.output_sizes):
                shard_offsets.append((i, current_shard_offset, output_size))
                current_shard_offset += output_size
            packed_dim = getattr(param, "packed_dim", None)
            for shard_id, shard_offset, shard_size in shard_offsets:
476
                # Special case for Quantization.
477
478
479
480
481
                # If quantized, we need to adjust the offset and size to account
                # for the packing.
                if packed_dim == output_dim:
                    shard_size = shard_size // param.pack_factor
                    shard_offset = shard_offset // param.pack_factor
482
                    # Special case for Marlin.
483
484
485
                    shard_size, shard_offset = adjust_marlin_shard(
                        param, shard_size, shard_offset)

486
487
488
489
490
491
492
493
494
495
496
                loaded_weight_shard = loaded_weight.narrow(
                    output_dim, shard_offset, shard_size)
                self.weight_loader(param, loaded_weight_shard, shard_id)
            return

        assert loaded_shard_id < len(self.output_sizes)
        tp_rank = get_tensor_model_parallel_rank()
        tp_size = get_tensor_model_parallel_world_size()
        if output_dim is not None:
            shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
            shard_size = self.output_sizes[loaded_shard_id] // tp_size
497
            # Special case for quantization.
498
499
500
501
502
503
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            packed_dim = getattr(param, "packed_dim", None)
            if packed_dim == output_dim:
                shard_size = shard_size // param.pack_factor
                shard_offset = shard_offset // param.pack_factor
504
                # Special case for Marlin.
505
506
507
                shard_size, shard_offset = adjust_marlin_shard(
                    param, shard_size, shard_offset)

508
509
510
511
512
513
            use_bitsandbytes = getattr(param, "use_bitsandbytes", False)
            if use_bitsandbytes:
                shard_size = loaded_weight.shape[output_dim]
                shard_offset = loaded_weight.shape[output_dim] * \
                    loaded_shard_id

514
            if is_gguf_weight:
515
516
517
518
                tp_size = get_tensor_model_parallel_world_size()
                output_dim = getattr(param, "output_dim", None)
                shard_shape = list(loaded_weight.shape)
                shard_shape[output_dim] = shard_shape[output_dim] // tp_size
519
                param.shard_id.append(loaded_shard_id)
520
521
522
523
524
                param.shard_size[loaded_shard_id] = shard_shape

                input_dim = getattr(param, "input_dim", None)
                input_size = loaded_weight.shape[input_dim]
                param_data = param_data.narrow(input_dim, 0, input_size)
525

526
527
528
529
530
            param_data = param_data.narrow(output_dim, shard_offset,
                                           shard_size)
            start_idx = tp_rank * shard_size
            loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                 shard_size)
531
        # Special case for AQLM codebooks.
James Fleming's avatar
James Fleming committed
532
533
534
535
536
        elif is_metadata:
            # metadata indicates fixed size concatenated along dim 0
            shard_size = loaded_weight.shape[0]
            shard_offset = loaded_shard_id * shard_size
            param_data = param_data.narrow(0, shard_offset, shard_size)
537

538
539
540
        # Special case for per-tensor scales in fused case.
        elif needs_scalar_to_array:
            param_data, loaded_weight = adjust_scalar_to_fused_array(
541
542
                param_data, loaded_weight, loaded_shard_id)

543
        else:
CHU Tianxiang's avatar
CHU Tianxiang committed
544
545
546
547
548
549
            ignore_warning = getattr(param, "ignore_warning", False)
            if not ignore_warning:
                logger.warning(
                    "Loading a weight without `output_dim` attribute in "
                    "MergedColumnParallelLinear, assume the weight is "
                    "the same for all partitions.")
550

551
552
553
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
    def _load_fused_module_from_checkpoint(self, param: BasevLLMParameter,
                                           loaded_weight: torch.Tensor):
        """
        Handle special case for models where MLP layers are already
        fused on disk. In this case, we have no shard id. This function
        determmines the shard id by splitting these layers and then calls
        the weight loader using the shard id.

        An example of a model with these fused layers:
        https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
        """

        current_shard_offset = 0
        shard_offsets: List[Tuple[int, int, int]] = []
        for i, output_size in enumerate(self.output_sizes):
            shard_offsets.append((i, current_shard_offset, output_size))
            current_shard_offset += output_size

        for shard_id, shard_offset, shard_size in shard_offsets:
            # Special case for Quantization.
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            if isinstance(param, PackedvLLMParameter
                          ) and param.packed_dim == param.output_dim:
578
579
                shard_size, shard_offset = \
                    param.adjust_shard_indexes_for_packing(
580
581
582
583
584
585
586
587
588
589
590
591
                    shard_size=shard_size, shard_offset=shard_offset)

            loaded_weight_shard = loaded_weight.narrow(param.output_dim,
                                                       shard_offset,
                                                       shard_size)
            self.weight_loader_v2(param, loaded_weight_shard, shard_id)

    def weight_loader_v2(self,
                         param: BasevLLMParameter,
                         loaded_weight: torch.Tensor,
                         loaded_shard_id: Optional[int] = None):
        if loaded_shard_id is None:
592
593
594
595
596
597
            if isinstance(param, PerTensorScaleParameter):
                param.load_merged_column_weight(loaded_weight=loaded_weight,
                                                shard_id=0)
                return
            elif type(param) is BasevLLMParameter:
                param.load_merged_column_weight(loaded_weight=loaded_weight)
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
                return
            self._load_fused_module_from_checkpoint(param, loaded_weight)
            return

        assert loaded_shard_id < len(self.output_sizes)

        tp_size = get_tensor_model_parallel_world_size()
        shard_offset = sum(self.output_sizes[:loaded_shard_id]) // tp_size
        shard_size = self.output_sizes[loaded_shard_id] // tp_size

        param.load_merged_column_weight(loaded_weight=loaded_weight,
                                        shard_id=loaded_shard_id,
                                        shard_offset=shard_offset,
                                        shard_size=shard_size)

613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634

class QKVParallelLinear(ColumnParallelLinear):
    """Linear layers for the attention's QKV transformation.

    Linear layers for the linear transformation of the query, key, and value
    vectors in the attention layer. The weight matrix is concatenated along
    the output dimension. The layer is parallelized along the head dimension.
    When the number of key/value heads is smaller than the number of query
    heads (e.g., multi-query/grouped-query attention), the key/value head may
    be replicated while the query heads are partitioned.

    Args:
        hidden_size: input hidden state size of the transformer.
        head_size: size of each attention head.
        total_num_heads: total number of attention query heads.
        total_num_kv_heads: total number of attention key/value heads. If
                            None, assume total_num_kv_heads = total_num_heads.
        bias: If true, add bias.
        skip_bias_add: This was added to enable performance optimizations where
                       bias can be fused with other element-wise operations. we
                       skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
635
        quant_config: Quantization configure.
636
637
        prefix: The name of the layer in the state dict, including all parents
                        (e.g. model.layers.0.qkv_proj)
638
639
    """

640
641
642
643
644
645
646
647
    def __init__(self,
                 hidden_size: int,
                 head_size: int,
                 total_num_heads: int,
                 total_num_kv_heads: Optional[int] = None,
                 bias: bool = True,
                 skip_bias_add: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
648
                 quant_config: Optional[QuantizationConfig] = None,
649
                 prefix: str = ""):
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
        self.hidden_size = hidden_size
        self.head_size = head_size
        self.total_num_heads = total_num_heads
        if total_num_kv_heads is None:
            total_num_kv_heads = total_num_heads
        self.total_num_kv_heads = total_num_kv_heads
        # Divide the weight matrix along the last dimension.
        tp_size = get_tensor_model_parallel_world_size()
        self.num_heads = divide(self.total_num_heads, tp_size)
        if tp_size >= self.total_num_kv_heads:
            self.num_kv_heads = 1
            self.num_kv_head_replicas = divide(tp_size,
                                               self.total_num_kv_heads)
        else:
            self.num_kv_heads = divide(self.total_num_kv_heads, tp_size)
            self.num_kv_head_replicas = 1
        input_size = self.hidden_size
        output_size = (self.num_heads +
                       2 * self.num_kv_heads) * tp_size * self.head_size
669
670
671
672
        self.output_sizes = [
            self.num_heads * self.head_size * tp_size,  # q_proj
            self.num_kv_heads * self.head_size * tp_size,  # k_proj
            self.num_kv_heads * self.head_size * tp_size,  # v_proj 
James Fleming's avatar
James Fleming committed
673
674
        ]

675
676
677
678
679
680
        super().__init__(input_size=input_size,
                         output_size=output_size,
                         bias=bias,
                         gather_output=False,
                         skip_bias_add=skip_bias_add,
                         params_dtype=params_dtype,
681
682
                         quant_config=quant_config,
                         prefix=prefix)
683

684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
    def _get_shard_offset_mapping(self, loaded_shard_id: str):
        shard_offset_mapping = {
            "q": 0,
            "k": self.num_heads * self.head_size,
            "v": (self.num_heads + self.num_kv_heads) * self.head_size,
            "total": (self.num_heads + 2 * self.num_kv_heads) * self.head_size
        }
        return shard_offset_mapping.get(loaded_shard_id)

    def _get_shard_size_mapping(self, loaded_shard_id: str):
        shard_size_mapping = {
            "q": self.num_heads * self.head_size,
            "k": self.num_kv_heads * self.head_size,
            "v": self.num_kv_heads * self.head_size,
        }
        return shard_size_mapping.get(loaded_shard_id)

    def _load_fused_module_from_checkpoint(self, param: BasevLLMParameter,
                                           loaded_weight: torch.Tensor):
        """
        Handle special case for models where QKV layers are already 
        fused on disk. In this case, we have no shard id. This function
        determmines the shard id by splitting these layers and then calls
        the weight loader using the shard id.

        An example of a model with these fused layers:
        https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
        """
        shard_offsets = [
            # (shard_id, shard_offset, shard_size)
            ("q", 0, self.total_num_heads * self.head_size),
            ("k", self.total_num_heads * self.head_size,
             self.total_num_kv_heads * self.head_size),
            ("v",
             (self.total_num_heads + self.total_num_kv_heads) * self.head_size,
             self.total_num_kv_heads * self.head_size),
        ]

        for shard_id, shard_offset, shard_size in shard_offsets:
            # Special case for Quantization.
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            if isinstance(param, PackedvLLMParameter
                          ) and param.packed_dim == param.output_dim:
728
729
                shard_size, shard_offset = \
                    param.adjust_shard_indexes_for_packing(
730
731
732
733
734
735
736
737
738
739
740
741
                    shard_size=shard_size, shard_offset=shard_offset)

            loaded_weight_shard = loaded_weight.narrow(param.output_dim,
                                                       shard_offset,
                                                       shard_size)
            self.weight_loader_v2(param, loaded_weight_shard, shard_id)

    def weight_loader_v2(self,
                         param: BasevLLMParameter,
                         loaded_weight: torch.Tensor,
                         loaded_shard_id: Optional[str] = None):
        if loaded_shard_id is None:  # special case for certain models
742
743
744
745
746
747
            if isinstance(param, PerTensorScaleParameter):
                param.load_merged_column_weight(loaded_weight=loaded_weight,
                                                shard_id=0)
                return
            elif type(param) is BasevLLMParameter:
                param.load_merged_column_weight(loaded_weight=loaded_weight)
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
                return
            self._load_fused_module_from_checkpoint(param, loaded_weight)
            return

        assert loaded_shard_id in ["q", "k", "v"]

        shard_offset = self._get_shard_offset_mapping(loaded_shard_id)
        shard_size = self._get_shard_size_mapping(loaded_shard_id)

        param.load_qkv_weight(loaded_weight=loaded_weight,
                              num_heads=self.num_kv_head_replicas,
                              shard_id=loaded_shard_id,
                              shard_offset=shard_offset,
                              shard_size=shard_size)

763
764
765
766
    def weight_loader(self,
                      param: Parameter,
                      loaded_weight: torch.Tensor,
                      loaded_shard_id: Optional[str] = None):
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789

        # Special case for GGUF
        # initialize GGUF param after we know the quantize type
        is_gguf_weight = getattr(param, "is_gguf_weight", False)
        is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
        if is_gguf_weight_type and loaded_shard_id is not None:
            idx_map = {"q": 0, "k": 1, "v": 2}
            param.data[idx_map[loaded_shard_id]].copy_(loaded_weight)
            param.shard_weight_type[loaded_shard_id] = loaded_weight.item()
            return

        if is_gguf_weight and isinstance(param, UninitializedParameter):
            from gguf.constants import GGML_QUANT_SIZES

            ori_shape = param.tensor_shape
            weight_types = self.qweight_type.shard_weight_type.values()
            row_size = []
            for weight_type in weight_types:
                block_size, type_size = GGML_QUANT_SIZES[weight_type]
                row_size.append(ori_shape[1] // block_size * type_size)
            q_shape = (ori_shape[0], max(row_size))
            param.materialize(q_shape, dtype=loaded_weight.dtype)

790
791
        param_data = param.data
        output_dim = getattr(param, "output_dim", None)
792
        # Special case for AQLM codebooks.
James Fleming's avatar
James Fleming committed
793
        is_metadata = getattr(param, "is_metadata", False)
794

795
796
        # Special case for per-tensor scales in fused case.
        needs_scalar_to_array = getattr(param, "needs_scalar_to_array", False)
797

798
        if loaded_shard_id is None:
799
            # Loaded weight is already fused on disk (qkv/mlp).
800
            if output_dim is None:
801
                if needs_scalar_to_array:
802
803
                    param_data, loaded_weight = adjust_scalar_to_fused_array(
                        param_data, loaded_weight, 0)
804

805
806
807
808
809
810
811
812
813
814
815
816
817
                assert param_data.shape == loaded_weight.shape
                param_data.copy_(loaded_weight)
                return
            shard_offsets = [
                # (shard_id, shard_offset, shard_size)
                ("q", 0, self.total_num_heads * self.head_size),
                ("k", self.total_num_heads * self.head_size,
                 self.total_num_kv_heads * self.head_size),
                ("v", (self.total_num_heads + self.total_num_kv_heads) *
                 self.head_size, self.total_num_kv_heads * self.head_size),
            ]
            packed_dim = getattr(param, "packed_dim", None)
            for shard_id, shard_offset, shard_size in shard_offsets:
818
                # Special case for Quantized Weights.
819
820
821
822
823
                # If quantized, we need to adjust the offset and size to account
                # for the packing.
                if packed_dim == output_dim:
                    shard_size = shard_size // param.pack_factor
                    shard_offset = shard_offset // param.pack_factor
824

825
                    # Special case for Marlin.
826
827
828
                    shard_size, shard_offset = adjust_marlin_shard(
                        param, shard_size, shard_offset)

829
830
831
832
833
834
835
                loaded_weight_shard = loaded_weight.narrow(
                    output_dim, shard_offset, shard_size)
                self.weight_loader(param, loaded_weight_shard, shard_id)
            return

        tp_rank = get_tensor_model_parallel_rank()
        assert loaded_shard_id in ["q", "k", "v"]
836
837

        # If output dim is defined, use the default loading process.
838
839
840
841
842
843
844
845
846
847
848
        if output_dim is not None:
            if loaded_shard_id == "q":
                shard_offset = 0
                shard_size = self.num_heads * self.head_size
            elif loaded_shard_id == "k":
                shard_offset = self.num_heads * self.head_size
                shard_size = self.num_kv_heads * self.head_size
            elif loaded_shard_id == "v":
                shard_offset = (self.num_heads +
                                self.num_kv_heads) * self.head_size
                shard_size = self.num_kv_heads * self.head_size
849
            # Special case for Quantized Weights.
850
851
852
853
854
855
            # If quantized, we need to adjust the offset and size to account
            # for the packing.
            packed_dim = getattr(param, "packed_dim", None)
            if packed_dim == output_dim:
                shard_size = shard_size // param.pack_factor
                shard_offset = shard_offset // param.pack_factor
856

857
                # Special case for Marlin.
858
859
860
                shard_size, shard_offset = adjust_marlin_shard(
                    param, shard_size, shard_offset)

861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
            use_bitsandbytes = getattr(param, "use_bitsandbytes", False)
            if use_bitsandbytes:
                orig_qkv_offsets = {
                    "q": (0, self.num_heads * self.head_size),
                    "k": (self.num_heads * self.head_size,
                          self.num_kv_heads * self.head_size),
                    "v":
                    ((self.num_heads + self.num_kv_heads) * self.head_size,
                     self.num_kv_heads * self.head_size),
                    "total":
                    ((self.num_heads + 2 * self.num_kv_heads) * self.head_size,
                     0)
                }
                shard_size, shard_offset = adjust_bitsandbytes_shard(
                    param, orig_qkv_offsets, loaded_shard_id)

877
            if is_gguf_weight:
878
879
880
881
                tp_size = get_tensor_model_parallel_world_size()
                output_dim = getattr(param, "output_dim", None)
                shard_shape = list(loaded_weight.shape)
                shard_shape[output_dim] = shard_shape[output_dim] // tp_size
882
                param.shard_id.append(loaded_shard_id)
883
884
                param.shard_size[loaded_shard_id] = shard_shape

885
886
887
888
                input_dim = getattr(param, "input_dim", None)
                input_size = loaded_weight.shape[input_dim]
                param_data = param_data.narrow(input_dim, 0, input_size)

889
890
            param_data = param_data.narrow(output_dim, shard_offset,
                                           shard_size)
891
892
893
894
            if loaded_shard_id == "q":
                shard_id = tp_rank
            else:
                shard_id = tp_rank // self.num_kv_head_replicas
895
896
897
            start_idx = shard_id * shard_size
            loaded_weight = loaded_weight.narrow(output_dim, start_idx,
                                                 shard_size)
898
        # Special case for for AQLM codebooks.
James Fleming's avatar
James Fleming committed
899
900
901
902
903
904
        elif is_metadata:
            # metadata indicates fixed size concatenated along dim 0
            shard_size = loaded_weight.shape[0]
            shard_index = ["q", "k", "v"].index(loaded_shard_id)
            param_data = param_data.narrow(0, shard_index * shard_size,
                                           shard_size)
905
906
907
        # Special case for per-tensor scales in fused case.
        elif needs_scalar_to_array:
            param_data, loaded_weight = adjust_scalar_to_fused_array(
908
                param_data, loaded_weight, loaded_shard_id)
909
        else:
CHU Tianxiang's avatar
CHU Tianxiang committed
910
911
912
913
914
915
            ignore_warning = getattr(param, "ignore_warning", False)
            if not ignore_warning:
                logger.warning(
                    "Loading a weight without `output_dim` attribute in "
                    "QKVParallelLinear, assume the weight is the same "
                    "for all partitions.")
916

917
918
919
920
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)


921
class RowParallelLinear(LinearBase):
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
    """Linear layer with row parallelism.

    The linear layer is defined as Y = XA + b. A is parallelized along
    its first dimension and X along its second dimension as:
               -   -
              | A_1 |
              | .   |
          A = | .   |        X = [X_1, ..., X_p]
              | .   |
              | A_p |
               -   -
    Arguments:
        input_size: first dimension of matrix A.
        output_size: second dimension of matrix A.
        bias: If true, add bias. Note that bias is not parallelized.
        input_is_parallel: If true, we assume that the input is already
                           split across the GPUs and we do not split
                           again.
        skip_bias_add: This was added to enable performance optimization where
                       bias can be fused with other element-wise operations.
                       We skip adding bias but instead return it.
        params_dtype: Data type for the parameters.
944
        quant_config: Quantization configure.
945
946
    """

947
948
949
950
951
952
953
954
    def __init__(self,
                 input_size: int,
                 output_size: int,
                 bias: bool = True,
                 input_is_parallel: bool = True,
                 skip_bias_add: bool = False,
                 params_dtype: Optional[torch.dtype] = None,
                 reduce_results: bool = True,
955
                 quant_config: Optional[QuantizationConfig] = None,
956
                 prefix: str = ""):
957
        super().__init__(input_size, output_size, skip_bias_add, params_dtype,
958
                         quant_config, prefix)
959

960
961
962
963
        self.input_is_parallel = input_is_parallel
        self.reduce_results = reduce_results

        # Divide the weight matrix along the last dimension.
964
        self.tp_rank = get_tensor_model_parallel_rank()
965
966
        self.tp_size = get_tensor_model_parallel_world_size()
        self.input_size_per_partition = divide(input_size, self.tp_size)
967
        assert self.quant_method is not None
968

969
970
971
972
973
974
975
        self.quant_method.create_weights(
            layer=self,
            input_size_per_partition=self.input_size_per_partition,
            output_partition_sizes=[self.output_size],
            input_size=self.input_size,
            output_size=self.output_size,
            params_dtype=self.params_dtype,
976
977
            weight_loader=(
                self.weight_loader_v2 if self.quant_method.__class__.__name__
978
                in WEIGHT_LOADER_V2_SUPPORTED else self.weight_loader))
979
980
981
982
983
984
        if not reduce_results and (bias and not skip_bias_add):
            raise ValueError("When not reduce the results, adding bias to the "
                             "results can lead to incorrect results")

        if bias:
            self.bias = Parameter(
985
                torch.empty(self.output_size, dtype=params_dtype))
986
987
988
989
990
991
992
993
994
            set_weight_attrs(self.bias, {
                "output_dim": 0,
                "weight_loader": self.weight_loader,
            })
        else:
            self.register_parameter("bias", None)

    def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor):
        tp_rank = get_tensor_model_parallel_rank()
995
        tp_size = get_tensor_model_parallel_world_size()
996
        input_dim = getattr(param, "input_dim", None)
997
998
999
1000
1001
1002
1003
1004
1005

        # Special case for GGUF
        is_gguf_weight = getattr(param, "is_gguf_weight", False)
        is_gguf_weight_type = getattr(param, "is_gguf_weight_type", False)
        if is_gguf_weight_type:
            param.weight_type = loaded_weight.item()

        # Materialize GGUF UninitializedParameter
        if is_gguf_weight and isinstance(param, UninitializedParameter):
1006
1007
1008
1009
            weight_shape = list(loaded_weight.shape)
            if input_dim:
                weight_shape[input_dim] = weight_shape[input_dim] // tp_size
            param.materialize(tuple(weight_shape), dtype=loaded_weight.dtype)
1010

1011
1012
1013
1014
1015
1016
        param_data = param.data
        if input_dim is not None:
            shard_size = param_data.shape[input_dim]
            start_idx = tp_rank * shard_size
            loaded_weight = loaded_weight.narrow(input_dim, start_idx,
                                                 shard_size)
1017

1018
1019
1020
        # Special case for loading scales off disk, which often do not
        # have a shape (such as in the case of AutoFP8).
        if len(loaded_weight.shape) == 0:
1021
1022
            loaded_weight = loaded_weight.reshape(1)

1023
1024
1025
        assert param_data.shape == loaded_weight.shape
        param_data.copy_(loaded_weight)

1026
1027
    def weight_loader_v2(self, param: BasevLLMParameter,
                         loaded_weight: torch.Tensor):
1028
1029
1030
1031
1032
1033
1034

        # Special case for loading scales off disk, which often do not
        # have a shape (such as in the case of AutoFP8).
        if len(loaded_weight.shape) == 0:
            assert loaded_weight.numel() == 1
            loaded_weight = loaded_weight.reshape(1)

1035
1036
        param.load_row_parallel_weight(loaded_weight=loaded_weight)

1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
    def forward(self, input_):
        if self.input_is_parallel:
            input_parallel = input_
        else:
            tp_rank = get_tensor_model_parallel_rank()
            splitted_input = split_tensor_along_last_dim(
                input_, num_partitions=self.tp_size)
            input_parallel = splitted_input[tp_rank].contiguous()

        # Matrix multiply.
1047
        assert self.quant_method is not None
1048
1049
1050
1051
1052
1053
        # Only fuse bias add into GEMM for rank 0 (this ensures that
        # bias will not get added more than once in TP>1 case)
        bias_ = None if (self.tp_rank > 0 or self.skip_bias_add) else self.bias
        output_parallel = self.quant_method.apply(self,
                                                  input_parallel,
                                                  bias=bias_)
1054
        if self.reduce_results and self.tp_size > 1:
1055
            output = tensor_model_parallel_all_reduce(output_parallel)
1056
        else:
1057
1058
1059
            output = output_parallel

        output_bias = self.bias if self.skip_bias_add else None
1060
1061

        return output, output_bias
1062
1063
1064
1065
1066
1067
1068
1069

    def extra_repr(self) -> str:
        s = f"input_features={self.input_size_per_partition}"
        s += f", output_features={self.output_size}"
        s += f", bias={self.bias is not None}"
        s += f", tp_size={self.tp_size}"
        s += f", reduce_results={self.reduce_results}"
        return s