linear.py 71.9 KB
Newer Older
1
# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
2
3
4
5
#
# See LICENSE for license information.

"""Linear API"""
6
from typing import Callable, Dict, Optional, Tuple, Union, List
7
8
from functools import reduce
from operator import mul as multiply_op
9
import warnings
10
11
12

import torch

13
import transformer_engine_torch as tex
14

15
from transformer_engine.common.recipe import Recipe
16
from transformer_engine.pytorch import torch_version
17

18
from .base import (
19
20
    fill_userbuffers_buffer_for_all_gather,
    get_dummy_wgrad,
21
    get_ub,
22
    get_workspace,
23
24
25
26
27
    TransformerEngineBaseModule,
    _2X_ACC_FPROP,
    _2X_ACC_DGRAD,
    _2X_ACC_WGRAD,
)
28
from ._common import noop_cat, WeightGradStore
29
from ..fp8 import FP8GlobalStateManager
30
31
from ..utils import (
    cast_if_needed,
32
    clear_tensor_data,
33
    divide,
34
    init_method_constant,
35
36
    requires_grad,
    needs_quantized_gemm,
37
    assert_dim_for_fp8_exec,
38
39
    nvtx_range_pop,
    nvtx_range_push,
40
41
42
43
44
)
from ..distributed import (
    set_tensor_model_parallel_attributes,
    get_distributed_world_size,
    allreduce,
45
    symmetric_all_reduce,
46
47
    reduce_scatter_along_first_dim,
    gather_along_first_dim,
48
    is_fp8_activation_recompute_enabled,
49
    in_fp8_activation_recompute_phase,
50
51
    _fsdp_scatter_tensors,
    _fsdp_gather_tensors,
52
53
)
from ..cpp_extensions import (
54
    general_gemm,
55
)
56
from ..constants import GemmParallelModes, dist_group_type
57
from ..jit import no_torch_dynamo
58
from ..graph import is_graph_capturing
59
60
from ..tensor.quantized_tensor import (
    QuantizedTensor,
61
    QuantizedTensorBase,
62
63
64
65
    Quantizer,
    prepare_for_saving,
    restore_from_saved,
)
66
from ..tensor.float8_tensor import Float8CurrentScalingQuantizer, Float8Quantizer
67
from ..tensor.mxfp8_tensor import MXFP8Quantizer
68
from ..export import is_in_onnx_export_mode, assert_warmed_up
69
from ..cpu_offload import is_cpu_offload_enabled, mark_activation_offload
70
from ...debug.pytorch.debug_state import TEDebugState
71

72
73
74
75
76
77
78
79
80
81
82
__all__ = ["Linear"]


class _Linear(torch.autograd.Function):
    """Linear semi-top level module
    Calls custom cuda extensions.
    """

    @staticmethod
    def forward(
        ctx,
83
        weight: torch.Tensor,
84
        inp: torch.Tensor,
85
        bias: Optional[torch.Tensor],
86
87
88
        is_first_microbatch: Union[bool, None],
        fp8: bool,
        fp8_calibration: bool,
89
        wgrad_store: WeightGradStore,
90
91
92
93
        input_quantizer: Optional[Quantizer],
        weight_quantizer: Optional[Quantizer],
        output_quantizer: Optional[Quantizer],
        grad_input_quantizer: Optional[Quantizer],
94
95
        grad_weight_quantizer: Optional[Quantizer],
        grad_output_quantizer: Optional[Quantizer],
96
        fuse_wgrad_accumulation: bool,
97
        cpu_offloading: bool,
98
99
100
101
102
103
104
        tp_group: Union[dist_group_type, None],
        tp_size: int,
        sequence_parallel: bool,
        tensor_parallel: bool,
        activation_dtype: torch.dtype,
        parallel_mode: Union[str, None],
        is_grad_enabled: bool,
105
106
107
108
109
110
        ub_overlap_rs_fprop: bool,
        ub_overlap_ag_dgrad: bool,
        ub_overlap_ag_fprop: bool,
        ub_overlap_rs_dgrad: bool,
        ub_bulk_dgrad: bool,
        ub_bulk_wgrad: bool,
111
        ub_name: str,
112
        fp8_output: bool,  # pylint: disable=unused-argument
113
        fsdp_group: Union[dist_group_type, None],
114
115
        module: torch.nn.Module,
        skip_fp8_weight_update: bool,
116
        symmetric_ar_type: str,
117
        save_original_input: bool = False,
118
        debug: Optional[bool] = False,
119
    ) -> torch.Tensor:
120
        # pylint: disable=missing-function-docstring
121

122
123
124
125
126
        # NVTX label for profiling
        nvtx_label = "transformer_engine._Linear.forward"
        if ub_name is not None:
            nvtx_label = f"{nvtx_label}.{ub_name}"

127
        # Make sure input dimensions are compatible
128
        out_features, in_features = weight.shape
129
        assert inp.shape[-1] == in_features, "GEMM not possible"
130

131
        # Configure tensor-parallel communication
132
        tp_world_size = get_distributed_world_size(tp_group)
133
134
135
136
        backward_needs_input = is_grad_enabled and weight.requires_grad
        with_input_all_gather_nccl = (
            parallel_mode == "column" and sequence_parallel and not ub_overlap_ag_fprop
        )
137
138

        # Configure Userbuffers communication (comm+GEMM overlap)
139
140
141
142
143
144
        if debug:  # turn off userbuffers in debug mode
            ub_overlap_rs_fprop = False
            ub_overlap_ag_fprop = False
            ub_overlap_rs_dgrad = False
            ub_bulk_wgrad = False
            ub_bulk_dgrad = False
145
146
147
        ub_obj = None
        ub_type = None
        if ub_overlap_rs_fprop:
148
            ub_obj = get_ub(ub_name + "_fprop", fp8)
149
150
            ub_type = tex.CommOverlapType.RS
        elif ub_overlap_ag_fprop:
151
            ub_obj = get_ub(ub_name + "_fprop", fp8)
152
153
154
155
156
157
158
159
160
161
            ub_type = tex.CommOverlapType.AG

        # ------------------------------------------------------
        # Prepare input tensor
        # Note: Cast to expected dtype and perform tensor-parallel communication
        # ------------------------------------------------------
        nvtx_range_push(f"{nvtx_label}.input_cast_comm")
        inputmat = inp  # Input tensor to save for backward (maybe sharded)
        inputmat_total = None  # Input tensor to pass to GEMM (gathered)
        own_quantized_input = False
162
        if fp8:
163
            assert_dim_for_fp8_exec(inputmat, weight)
164
165
166
167
168
            if save_original_input:
                assert not isinstance(
                    input_quantizer, Float8Quantizer
                ), "DelayedScaling recipe is not supported with save_original_input"

169
170
171
172
173
174
        if with_input_all_gather_nccl or ub_overlap_ag_fprop:  # All-gather input tensor

            # Cast local input tensor if needed
            if fp8 or debug:
                if input_quantizer is None:
                    raise ValueError("Missing quantizer for input tensor")
175
                if not isinstance(inputmat, QuantizedTensorBase):
176
177
                    own_quantized_input = True
                    input_quantizer.set_usage(rowwise=True, columnwise=backward_needs_input)
178
179
180
181
182
                    if isinstance(
                        input_quantizer, (Float8Quantizer, Float8CurrentScalingQuantizer)
                    ):
                        # All-gather is not supported with FP8 column-wise data
                        input_quantizer.set_usage(columnwise=False)
183
184
185
186
187
                    if save_original_input:
                        # No need for column-wise data since this
                        # tensor will not be cached for backward pass
                        input_quantizer.set_usage(columnwise=False)
                        own_quantized_input = False
188
                    inputmat = input_quantizer(inputmat)
189
            else:
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
                inputmat = cast_if_needed(inp, activation_dtype)  # Cast for AMP

            # Initialize gathered input tensor
            quantizer = None
            if fp8 or debug:
                quantizer = input_quantizer
                quantizer.set_usage(rowwise=True, columnwise=False)
            if with_input_all_gather_nccl:  # Perform NCCL all-gather
                inputmat_total, _ = gather_along_first_dim(
                    inputmat,
                    tp_group,
                    quantizer=quantizer,
                )
            elif ub_overlap_ag_fprop:  # Initialize Userbuffers all-gather
                inputmat_total, _ = fill_userbuffers_buffer_for_all_gather(
                    ub_obj,
                    inputmat,
                    quantizer,
                    tp_group,
                )

        else:  # Do not all-gather input tensor
            if fp8 or debug:
                if isinstance(inputmat, QuantizedTensorBase):
                    inputmat.update_usage(rowwise_usage=True)
215
                else:
216
217
                    if input_quantizer is None:
                        raise ValueError("Missing quantizer for input tensor")
218
219
220
                    input_quantizer.set_usage(
                        rowwise=True, columnwise=backward_needs_input and not save_original_input
                    )
221
                    inputmat = input_quantizer(inputmat)
222
                    own_quantized_input = True
223
            else:
224
225
                inputmat = cast_if_needed(inp, activation_dtype)  # Cast for AMP
            inputmat_total = inputmat
226
        nvtx_range_pop(f"{nvtx_label}.input_cast_comm")
227
228
229
        # ------------------------------------------------------
        # Input tensor is ready for GEMM...
        # ------------------------------------------------------
230

231
232
233
        # ------------------------------------------------------
        # Prepare weight tensor
        # ------------------------------------------------------
234
235
        weightmat = weight
        if fp8 or debug:
236
237
238
239
240
241
242
243
244
            # Configure quantizer
            if weight_quantizer is not None:
                columnwise_usage = is_grad_enabled and inp.requires_grad
                if not columnwise_usage:
                    columnwise_usage = (
                        is_fp8_activation_recompute_enabled()
                        and not in_fp8_activation_recompute_phase()
                    )
                weight_quantizer.set_usage(rowwise=True, columnwise=columnwise_usage)
245
246

            # Get quantized weight
247
248
249
250
251
252
253
254
            update_workspace = is_first_microbatch is None or is_first_microbatch
            weightmat = module.get_weight_workspace(
                tensor=weight,
                quantizer=weight_quantizer,
                cache_name=(None if is_first_microbatch is None else "weight"),
                update_workspace=update_workspace,
                skip_update_flag=skip_fp8_weight_update,
                fsdp_group=fsdp_group,
255
                workspace_dtype=activation_dtype,
256
            )
257
258
            weightmat.update_usage(rowwise_usage=True)

259
        else:
260
261
262
263
            weightmat = cast_if_needed(weightmat, activation_dtype)  # Cast for AMP
        # ------------------------------------------------------
        # Weight tensor is ready for GEMM...
        # ------------------------------------------------------
264
265
266

        # Cast bias to expected dtype
        bias_dtype = activation_dtype
267
        if needs_quantized_gemm(inputmat_total) and activation_dtype == torch.float32:
268
            # cuBLAS does not support FP8 GEMM with FP32 bias, so we cast to BF16
269
270
271
272
273
274
275
276
277
278
            bias_dtype = torch.bfloat16
        bias = cast_if_needed(bias, bias_dtype) if bias is not None else bias

        # Calibrate quantizers if needed
        if not fp8 and fp8_calibration:
            if input_quantizer is not None:
                input_quantizer.calibrate(inputmat_total)
            if weight_quantizer is not None:
                weight_quantizer.calibrate(weight)

279
280
        # Choose whether to use GEMM kernel with split accumulator
        use_split_accumulator = _2X_ACC_FPROP
281
282
283
        if fp8:
            recipe = FP8GlobalStateManager.get_fp8_recipe()
            if hasattr(recipe, "fp8_gemm_fprop"):
284
285
286
287
288
                use_split_accumulator = recipe.fp8_gemm_fprop.use_split_accumulator

        # Configure output quantizer
        if output_quantizer is not None:
            output_quantizer.set_usage(rowwise=True, columnwise=False)
289

290
291
292
293
294
295
296
297
298
299
300
301
302
303
        # Output buffer for Userbuffers reduce-scatter
        reduce_scatter_out = None
        if ub_overlap_rs_fprop:
            out_shape = list(inp.shape)
            out_shape[0] //= tp_world_size
            out_shape[-1] = out_features
            reduce_scatter_out = torch.empty(out_shape, dtype=activation_dtype, device=inp.device)

        # ------------------------------------------------------
        # Forward GEMM
        # Note: y = x * w^T
        # ------------------------------------------------------
        nvtx_range_push(f"{nvtx_label}.gemm")
        gemm_out, *_, reduce_scatter_out = general_gemm(
304
305
306
307
            weightmat,
            inputmat_total,
            get_workspace(),
            quantization_params=output_quantizer,
308
            out_dtype=activation_dtype,
309
            bias=bias,
310
            use_split_accumulator=use_split_accumulator,
311
312
            ub=ub_obj,
            ub_type=ub_type,
313
            extra_output=reduce_scatter_out,
314
        )
315
        nvtx_range_pop(f"{nvtx_label}.gemm")
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
        # ------------------------------------------------------
        # Finished forward GEMM...
        # ------------------------------------------------------

        # ------------------------------------------------------
        # Prepare output tensor
        # Note: Perform tensor-parallel communication
        # ------------------------------------------------------
        out = None
        if ub_overlap_rs_fprop:
            out = reduce_scatter_out
        elif parallel_mode == "row" and tp_size > 1:
            nvtx_range_push(f"{nvtx_label}.row_parallel_comm")
            out = gemm_out
            if sequence_parallel:
                out, _ = reduce_scatter_along_first_dim(out, tp_group)
            elif tensor_parallel:
                if symmetric_ar_type is not None:
                    out, _ = symmetric_all_reduce(out, tp_group, all_reduce_type=symmetric_ar_type)
                else:
                    out, _ = allreduce(out, tp_group)
            nvtx_range_pop(f"{nvtx_label}.row_parallel_comm")
        else:
            out = gemm_out
        # ------------------------------------------------------
        # Output tensor is ready to return...
        # ------------------------------------------------------

        # ------------------------------------------------------
        # Cache state for backward pass
        # ------------------------------------------------------
347
348

        if is_grad_enabled:
349
350
351
            if save_original_input:
                inputmat = inp

352
            ctx.weight_quantizer = weight_quantizer
353
354
355
356
357

            ctx.backward_input_needs_gather = (
                weight.requires_grad and parallel_mode == "column" and sequence_parallel
            )

358
359
360
361
362
363
            # Discard unneeded data in input tensor
            if (
                backward_needs_input
                and own_quantized_input
                and isinstance(inputmat, QuantizedTensorBase)
            ):
364
365
366
                if (
                    ctx.backward_input_needs_gather
                    and weight_quantizer.supports_only_rowwise_all_gather()
367
368
369
370
371
372
373
374
375
                ):
                    # All-gather is not supported with FP8 column-wise data
                    inputmat.update_usage(rowwise_usage=True, columnwise_usage=False)
                else:
                    # Discard row-wise data since it is not needed in backward pass
                    inputmat.update_usage(rowwise_usage=False, columnwise_usage=True)

            # Cached input tensor
            saved_inputmat = None
376
377
            if backward_needs_input:
                saved_inputmat = inputmat
378

379
380
            # Weight with column-wise usage is needed for dgrad GEMM.
            if inp.requires_grad:
381
                if isinstance(weightmat, QuantizedTensorBase):
382
383
                    weightmat.update_usage(columnwise_usage=True)

384
385
            if cpu_offloading and saved_inputmat is not None:
                mark_activation_offload(saved_inputmat)
386

387
388
            # Scatter intermediate/activation tensors saved for the backward pass
            # NOTE: FSDP sharding is not valid for models initialized with primary Fp8 weights
389
            nvtx_range_push(f"{nvtx_label}.fsdp_scatter")
390
391
392
            ctx.fsdp_group = fsdp_group
            ctx.fsdp_shapes = _fsdp_scatter_tensors(
                fsdp_group,
393
                saved_inputmat,
394
                weightmat if fp8 and not isinstance(weight, QuantizedTensorBase) else None,
395
            )
396
            nvtx_range_pop(f"{nvtx_label}.fsdp_scatter")
397

398
399
400
401
402
403
404
405
406
407
408
            if cpu_offloading:
                ctx.grad_added_to_main_grad = hasattr(weight, "grad_added_to_main_grad")

                if ctx.grad_added_to_main_grad:
                    # If you are passing torch.nn.Parameter through the Torch hooks, you will
                    # get back torch.Tensor. Torch rips off the Parameter wrapper.
                    # You need to preserve the weight object to have all the attributes user
                    # sets for the weights. Because of this, it is not recommended to offload
                    # weights if weights are externally touched outside this module
                    ctx.weight_object = weight

409
410
            # TODO(ksivamani): Check memory usage
            tensors_to_save, tensor_objects = prepare_for_saving(
411
                saved_inputmat,
412
                weightmat,
413
                weight,
414
                bias,
415
            )
416
417
            ctx.save_for_backward(*tensors_to_save)
            ctx.tensor_objects = tensor_objects
418

419
420
            ctx.activation_dtype = activation_dtype
            ctx.fp8 = fp8
421
            ctx.fp8_recipe = FP8GlobalStateManager.get_fp8_recipe() if fp8 else None
422
423
            ctx.input_quantizer = input_quantizer
            ctx.grad_input_quantizer = grad_input_quantizer
424
425
            ctx.grad_weight_quantizer = grad_weight_quantizer
            ctx.grad_output_quantizer = grad_output_quantizer
426
            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
427
            if fuse_wgrad_accumulation and weight.requires_grad:
428
429
430
431
432
433
434
435
                # This check is needed to ensure that main_grad is not created
                # during the forward pass when using MCore FSDP as it creates
                # the main_grad buffer lazily before backprop
                if hasattr(weight, "__fsdp_param__"):
                    # MCore FSDP creates main_grad lazily before backward
                    ctx.main_grad_func = weight.get_main_grad
                else:
                    ctx.main_grad_func = lambda: weight.main_grad
436

437
            ctx.debug = debug
438
            ctx.cpu_offloading = cpu_offloading
439
            ctx.is_first_microbatch = is_first_microbatch
440
            ctx.use_bias = bias is not None
441
442
            ctx.sequence_parallel = sequence_parallel
            ctx.tensor_parallel = tensor_parallel
443
            ctx.inp_shape = inp.shape
444
445
            ctx.parallel_mode = parallel_mode
            ctx.tp_group = tp_group
446
447
448
449
            ctx.ub_overlap_ag = ub_overlap_ag_dgrad
            ctx.ub_overlap_rs_dgrad = ub_overlap_rs_dgrad
            ctx.ub_bulk_dgrad = ub_bulk_dgrad
            ctx.ub_bulk_wgrad = ub_bulk_wgrad
450
            ctx.ub_name = ub_name
451
452
            ctx.tp_size = tp_size
            ctx.requires_dgrad = inp.requires_grad
453
            ctx.requires_wgrad = weight.requires_grad
454
            ctx.reduce_and_update_bwd_fp8_tensors = False
455

456
            ctx.owns_input = saved_inputmat is not inp
457
            if ctx.fp8 and requires_grad(inp, weight, bias):
458
459
460
461
                _first_fp8_module = FP8GlobalStateManager.IS_FIRST_FP8_MODULE
                ctx.reduce_and_update_bwd_fp8_tensors = FP8GlobalStateManager.is_first_fp8_module()
                if in_fp8_activation_recompute_phase():
                    FP8GlobalStateManager.IS_FIRST_FP8_MODULE = _first_fp8_module
462
            ctx.wgrad_store = wgrad_store
463

464
465
466
        # ------------------------------------------------------
        # Cached state for backward pass is ready...
        # ------------------------------------------------------
467

468
        return out
469
470

    @staticmethod
471
    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
472
        # pylint: disable=missing-function-docstring
473

474
475
476
477
478
        # NVTX label for profiling
        nvtx_label = "transformer_engine._Linear.backward"
        if ctx.ub_name is not None:
            nvtx_label = f"{nvtx_label}.{ctx.ub_name}"

479
        with torch.cuda.nvtx.range("_Linear_backward"):
480
481
482
483
            saved_tensors = ctx.saved_tensors
            inputmat, weight_fp8, weight, bias = (  # pylint: disable=unbalanced-tuple-unpacking
                restore_from_saved(ctx.tensor_objects, saved_tensors)
            )
484

485
486
487
            # Delete the references to tensor objects once they've been consumed
            # by the `restore_from_saved` method to construct back the actual tensors.
            ctx.tensor_objects = None
488
489
490

            # Since main_grad can be modified inplace, it should not be a part of saved_tensors
            main_grad = (
491
                ctx.main_grad_func()
492
493
494
495
                if weight is not None and ctx.fuse_wgrad_accumulation and ctx.requires_wgrad
                else None
            )

496
497
498
499
500
            if ctx.cpu_offloading:
                if ctx.grad_added_to_main_grad:
                    weight = ctx.weight_object
                if ctx.requires_wgrad and ctx.fuse_wgrad_accumulation:
                    weight.main_grad = main_grad
501

502
503
504
            # Gather intermediate/activation tensors if needed
            # NOTE: weight_fp8 = weight when ctx.fp8 == False and torch.disttributed.FSDP already
            #       shards/unshards the base weights so we don't do it ourselves
505
            nvtx_range_push(f"{nvtx_label}.fsdp_gather")
506
507
508
509
            _fsdp_gather_tensors(
                ctx.fsdp_group,
                ctx.fsdp_shapes,
                inputmat,
510
                weight_fp8,
511
            )
512
            nvtx_range_pop(f"{nvtx_label}.fsdp_gather")
513

514
            # Configure Userbuffers communication (comm+GEMM overlap)
515
            ctx.ub_obj_gradout = None
516
            ub_obj_dgrad = None
517
            ub_obj_wgrad = None
518
519
            ub_type_dgrad = None
            ub_type_wgrad = None
520
            dgrad_shape = [reduce(multiply_op, ctx.inp_shape[:-1]), ctx.inp_shape[-1]]
521
            if ctx.ub_overlap_ag:
522
                # Overlap grad_output all-gather with dgrad compute
523
                ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad", ctx.fp8)
524
525
                ub_obj_dgrad = ctx.ub_obj_gradout
                ub_type_dgrad = tex.CommOverlapType.AG
526
527
            elif ctx.ub_overlap_rs_dgrad:
                # Overlap dgrad reduce-scatter with dgrad compute
528
                ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad", ctx.fp8)
529
530
                ub_obj_dgrad = ctx.ub_obj_gradout
                ub_type_dgrad = tex.CommOverlapType.RS
531
532
533
            else:
                if ctx.ub_bulk_dgrad:
                    # Overlap inputmat all-gather with dgrad compute
534
                    ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad", ctx.fp8)
535
536
                    ub_obj_dgrad = ctx.ub_obj_gradout
                    ub_type_dgrad = tex.CommOverlapType.AG
537
538
                if ctx.ub_bulk_wgrad:
                    # Overlap dgrad reduce-scatter with wgrad compute
539
                    ub_obj_wgrad = get_ub(ctx.ub_name + "_wgrad", ctx.fp8)
540
                    ub_type_wgrad = tex.CommOverlapType.RS
541
542
543
544
545
546
547
548

            # --------------------------------------------------
            # Prepare grad output tensor
            # Note: Cast to expected dtype and perform tensor-parallel communication
            # --------------------------------------------------

            # Unmodified grad output tensor
            grad_output_arg = grad_output
549

550
551
552
553
            # Configure quantizer for grad output tensor
            # Note: dgrad GEMM requires row-wise usage, wgrad GEMM
            # requires column-wise usage
            if ctx.grad_output_quantizer is not None:
554
555
556
557
558
559
560
                quantizer = ctx.grad_output_quantizer
                quantizer.set_usage(rowwise=True, columnwise=True)
                if ctx.ub_overlap_ag:
                    # Userbuffers only supports communication for one
                    # tensor usage at a time. Configure quantizer with
                    # usage for only dgrad GEMM.
                    quantizer.set_usage(columnwise=False)
561

562
563
564
565
566
567
568
569
570
571
572
573
574
            # Adjust the quantization direction approach depending
            # on whether wgrad calculations will be performed.
            # NOTE: If requires_dgrad is False, disabling `rowwise` quantization and keeping `columnwise` quantization
            #       results in `Assertion failed: output_tensor->has_data(). Quantizing in only the columnwise direction not supported yet!`
            # NOTE: For `ctx.bias is True`, selected quantize kernel errors with
            #       `cast_kernels.cuh:1322 in function fp8_quantize_arch_l_100: Not implemented scaling mode or fusion: NVTE_DELAYED_TENSOR_SCALING or IS_DBIAS=true on GPU with compute capability < 10.0.`
            if (
                not ctx.use_bias
                and not ctx.requires_wgrad
                and ctx.grad_output_quantizer is not None
            ):
                ctx.grad_output_quantizer.set_usage(columnwise=False)

575
            # Prepare grad output tensor
576
            nvtx_range_push(f"{nvtx_label}.grad_output_preprocess")
577
578
579
580
            (
                grad_output,
                grad_bias,
            ) = TransformerEngineBaseModule.grad_output_preprocess(
581
582
583
584
                ctx,
                grad_output,
                ctx.parallel_mode == "row",
                ctx.grad_output_quantizer,
585
            )
586
            nvtx_range_pop(f"{nvtx_label}.grad_output_preprocess")
587

588
589
590
591
592
593
594
595
596
597
            # --------------------------------------------------
            # Grad output tensor is ready for computing grad input...
            # --------------------------------------------------

            # --------------------------------------------------
            # Prepare input tensor
            # Note: Input tensor is needed for wgrad GEMM.
            # Tensor-parallel communication is overlapped with dgrad
            # GEMM.
            # --------------------------------------------------
598
            inputmat_total = None
599
            inputmat_total_work = None
600
601
            if ctx.requires_wgrad:
                if ctx.fp8 or ctx.debug:
602
603
604
605
606
607
608
609
                    if isinstance(inputmat, QuantizedTensorBase):
                        # Input tensor is already quantized
                        pass
                    elif ctx.debug:
                        # Debug quantizer will be applied immediately before wgrad GEMM
                        pass
                    else:
                        # Quantize input tensor
610
                        quantizer = ctx.input_quantizer
611
                        if quantizer.supports_only_rowwise_all_gather():
612
                            # All-gather is not supported with FP8 column-wise data
613
614
615
616
                            quantizer.set_usage(
                                rowwise=True,
                                columnwise=not ctx.backward_input_needs_gather,
                            )
617
                        else:
618
                            quantizer.set_usage(rowwise=False, columnwise=True)
619
620
                        inputmat = quantizer(inputmat)
                else:
621
                    if isinstance(inputmat, QuantizedTensorBase):
622
623
624
                        inputmat = inputmat.dequantize(dtype=ctx.activation_dtype)
                    else:
                        inputmat = cast_if_needed(inputmat, ctx.activation_dtype)
625
            if ctx.backward_input_needs_gather:
626
                quantizer = None
627
                if ctx.fp8 or ctx.debug:
628
                    quantizer = ctx.input_quantizer
629
                    if quantizer.supports_only_rowwise_all_gather():
630
631
632
633
634
                        # If data is in FP8, we compute FP8 transposes manually
                        quantizer.set_usage(rowwise=True, columnwise=False)
                    else:
                        # wgrad GEMM requires input with column-wise usage
                        quantizer.set_usage(rowwise=False, columnwise=True)
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
                if ctx.ub_bulk_dgrad:
                    inputmat_total, _ = fill_userbuffers_buffer_for_all_gather(
                        ub_obj_dgrad,
                        inputmat,
                        quantizer,
                        ctx.tp_group,
                    )
                else:
                    nvtx_range_push(f"{nvtx_label}.column_parallel_comm_input")
                    inputmat_total, inputmat_total_work = gather_along_first_dim(
                        inputmat,
                        ctx.tp_group,
                        async_op=True,
                        quantizer=quantizer,
                    )
                    nvtx_range_pop(f"{nvtx_label}.column_parallel_comm_input")
651
652
            else:
                inputmat_total = inputmat
653
654
655
            # --------------------------------------------------
            # Input tensor is ready for computing grad weight...
            # --------------------------------------------------
656

657
            # --------------------------------------------------
658
            # Compute grad input tensor
659
660
            # --------------------------------------------------

661
662
            dgrad = None
            dgrad_work = None
663
            if ctx.requires_dgrad:
664

665
666
667
668
669
670
671
672
                # Make sure required data is available
                if isinstance(grad_output, QuantizedTensorBase):
                    grad_output.update_usage(rowwise_usage=True)
                if ctx.weight_quantizer is not None and isinstance(weight_fp8, QuantizedTensorBase):
                    weight_fp8.update_usage(columnwise_usage=True)

                # Choose whether to use GEMM kernel with split accumulator
                use_split_accumulator = _2X_ACC_DGRAD
673
674
675
                if ctx.fp8:
                    recipe = ctx.fp8_recipe
                    if hasattr(recipe, "fp8_gemm_dgrad"):
676
                        use_split_accumulator = recipe.fp8_gemm_dgrad.use_split_accumulator
677

678
679
680
681
682
683
684
685
686
687
                # Update grad input quantizer
                if ctx.grad_input_quantizer is not None:
                    ctx.grad_input_quantizer.set_usage(rowwise=True, columnwise=False)

                # Output buffers for Userbuffers reduce-scatter
                gemm_out = None
                reduce_scatter_out = None
                if ctx.ub_overlap_rs_dgrad:
                    reduce_scatter_out = torch.empty(
                        dgrad_shape, dtype=ctx.activation_dtype, device=grad_output_arg.device
688
                    )
689
690
                elif ctx.ub_bulk_wgrad:
                    gemm_out = ub_obj_wgrad.get_buffer(local_chunk=False)
691

692
693
694
695
                # dgrad GEMM
                # Note: dx = dy * w
                nvtx_range_push(f"{nvtx_label}.dgrad_gemm")
                gemm_out, *_, reduce_scatter_out = general_gemm(
696
697
698
699
700
701
                    weight_fp8,
                    grad_output,
                    get_workspace(),
                    layout="NN",
                    grad=True,
                    quantization_params=ctx.grad_input_quantizer,
702
                    out=gemm_out,
703
                    out_dtype=ctx.activation_dtype,
704
                    use_split_accumulator=use_split_accumulator,
705
706
                    ub=ub_obj_dgrad,
                    ub_type=ub_type_dgrad,
707
                    extra_output=reduce_scatter_out,
708
709
                    bulk_overlap=ctx.ub_bulk_dgrad,
                )
710
                nvtx_range_pop(f"{nvtx_label}.dgrad_gemm")
711

712
713
                # Prepare grad input tensor
                # Note: Perform tensor-parallel communication
714
                if ctx.ub_overlap_rs_dgrad:
715
716
717
718
                    dgrad = reduce_scatter_out
                elif ctx.ub_bulk_wgrad:
                    dgrad = ub_obj_wgrad.get_buffer(local_chunk=True)
                elif ctx.parallel_mode == "column" and ctx.tp_size > 1:
719
                    nvtx_range_push(f"{nvtx_label}.column_parallel_comm_dgrad")
720
                    dgrad = gemm_out
721
722
723
724
725
                    if ctx.sequence_parallel:
                        dgrad, dgrad_work = reduce_scatter_along_first_dim(
                            dgrad,
                            ctx.tp_group,
                            async_op=True,
726
                        )
727
                    else:
728
                        dgrad, dgrad_work = allreduce(dgrad, ctx.tp_group, async_op=True)
729
                    nvtx_range_pop(f"{nvtx_label}.column_parallel_comm_dgrad")
730
731
732
733
734
735
736
737
738
739
                else:
                    dgrad = gemm_out

            # --------------------------------------------------
            # Grad input tensor has been computed...
            # --------------------------------------------------

            # --------------------------------------------------
            # Compute grad weight
            # --------------------------------------------------
740

741
742
            wgrad = None
            if ctx.requires_wgrad:
743

744
745
746
                # Prepare input tensor
                # Note: Synchronize tensor-parallel communication and
                # make sure required data is available
747
748
749
                if inputmat_total_work is not None:
                    inputmat_total_work.wait()
                    inputmat_total_work = None
750
751
752
753
754
755
756
757
758
759
760
                if ctx.fp8 or ctx.debug:
                    if isinstance(inputmat_total, QuantizedTensorBase):
                        inputmat_total.update_usage(columnwise_usage=True)
                    else:
                        ctx.input_quantizer.set_usage(rowwise=False, columnwise=True)
                        inputmat_total = ctx.input_quantizer(inputmat_total)

                # Prepare grad output tensor
                # Note: Synchronize tensor-parallel communication and
                # make sure required data is available
                if ctx.ub_overlap_ag and isinstance(ctx.grad_output_quantizer, MXFP8Quantizer):
761
                    # UB does not support pipelined overlapping grad output
762
763
764
                    # all-gather with wgrad GEMM. Also, we can't
                    # convert row-scaled MXFP8 to column-scaled, so we
                    # can't reuse the grad output that was gathered
765
                    # for the dgrad GEMM. We work around by explicitly
766
767
768
769
770
771
                    # overlapping the AG operation with the dgrad GEMM.

                    # Get the communication stream from the dgrad GEMM to use for the AG
                    dgrad_send_stream, dgrad_recv_stream = ub_obj_dgrad.get_communication_stream()

                    # This object is separate from the ub_obj_wgrad object which is passed to the GEMM
772
                    ub_obj_overlap_wgrad = get_ub(ctx.ub_name + "_wgrad", ctx.fp8)
773

774
                    ctx.grad_output_quantizer.set_usage(rowwise=False, columnwise=True)
775
776
777
778
779
780
781

                    # We use the send stream to copy into the userbuffers.
                    # This is the same stream that we will use to access the data in the AG,
                    # so we dont need to add any syncs yet.
                    with torch.cuda.stream(dgrad_send_stream):
                        grad_output, _ = fill_userbuffers_buffer_for_all_gather(
                            ub_obj_overlap_wgrad,
782
                            grad_output_arg,
783
                            ctx.grad_output_quantizer,
784
785
                            ctx.tp_group,
                        )
786
787
788
789
790

                    # Allgather grad_outputs[0] using the dgrad streams so we can overlap with the fc2_dgrad gemm
                    tex.bulk_overlap_ag_with_external_gemm(
                        ub_obj_overlap_wgrad, dgrad_send_stream, dgrad_recv_stream
                    )
791

792
793
794
795
796
797
                if ctx.fp8 or ctx.debug:
                    if isinstance(grad_output, QuantizedTensorBase):
                        grad_output.update_usage(columnwise_usage=True)
                    else:
                        ctx.grad_output_quantizer.set_usage(rowwise=False, columnwise=True)
                        grad_output = ctx.grad_output_quantizer(grad_output)
798

799
800
801
802
803
804
805
                # Figure out whether to use split accumulator
                use_split_accumulator = _2X_ACC_WGRAD
                if ctx.fp8:
                    recipe = ctx.fp8_recipe
                    if hasattr(recipe, "fp8_gemm_wgrad"):
                        use_split_accumulator = recipe.fp8_gemm_wgrad.use_split_accumulator

806
807
808
809
810
811
812
813
814
                # Figure out whether to output wgrad GEMM directly into main grad
                if ctx.is_first_microbatch is not None:
                    accumulate_wgrad_into_param_main_grad = (
                        ctx.fuse_wgrad_accumulation and not ctx.is_first_microbatch
                    )
                else:
                    accumulate_wgrad_into_param_main_grad = ctx.fuse_wgrad_accumulation

                # Output buffer for overlapping FP8 grad input
815
                # reduce-scatter with wgrad GEMM
816
                reduce_scatter_out = None
817
                if ctx.ub_bulk_wgrad and ub_obj_wgrad.is_fp8_ubuf():
818
819
                    reduce_scatter_out = torch.empty(
                        dgrad_shape, dtype=ctx.activation_dtype, device=grad_output_arg.device
820
821
                    )

822
823
824
825
                # Arguments to include in wgrad GEMM closure
                wgrad_gemm_kwargs = {
                    "workspace": get_workspace(),
                    "out_dtype": (
826
827
                        main_grad.dtype if ctx.fuse_wgrad_accumulation else ctx.activation_dtype
                    ),
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
                    "quantization_params": ctx.grad_weight_quantizer,
                    "accumulate": accumulate_wgrad_into_param_main_grad,
                    "layout": "NT",
                    "out": main_grad if ctx.fuse_wgrad_accumulation else None,
                    "bias": (bias if (grad_bias is None and not ctx.fp8) else None),
                    "use_split_accumulator": use_split_accumulator,
                    "grad": True,
                    "ub": ub_obj_wgrad,
                    "ub_type": ub_type_wgrad,
                    "extra_output": reduce_scatter_out,
                    "bulk_overlap": ctx.ub_bulk_wgrad,
                }

                def wgrad_gemm(
                    x: torch.Tensor,
                    dy: torch.Tensor,
                ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
                    """Perform wgrad GEMM: dw = dy^T * x

                    May be fused with bgrad computation.

                    May be called outside of this function to enable
                    some advanced communication/compute overlapping.

                    """
                    nvtx_range_push(f"{nvtx_label}.wgrad_gemm")
                    dw, db, *_ = general_gemm(x, dy, **wgrad_gemm_kwargs)
                    nvtx_range_pop(f"{nvtx_label}.wgrad_gemm")
                    return dw, db

                # Choose whether to call wgrad GEMM now or delay
859
                if ctx.wgrad_store is not None and ctx.wgrad_store.delay_wgrad_compute():
860
861
862
863
864
865
866
867
868
869
870
                    if (
                        wgrad_gemm_kwargs["ub"] is not None
                        or wgrad_gemm_kwargs["ub_type"] is not None
                        or wgrad_gemm_kwargs["extra_output"] is not None
                        or wgrad_gemm_kwargs["bulk_overlap"]
                    ):
                        raise NotImplementedError(
                            "Delayed weight grad computation is not supported "
                            "with Userbuffers (tensor-parallel communication overlapping)"
                        )
                    ctx.wgrad_store.put([inputmat_total, grad_output], wgrad_gemm)
871
872
                else:

873
874
875
876
                    # Call wgrad GEMM now
                    wgrad, grad_bias_ = wgrad_gemm(inputmat_total, grad_output)

                    # Update grad bias if needed
877
878
879
880
                    if grad_bias is None:
                        grad_bias = grad_bias_
                    del grad_bias_

881
                    # Deallocate input tensor if permitted
882
883
                    if ctx.owns_input:
                        clear_tensor_data(inputmat_total)
884

885
                # Update grad input if overlapping reduce-scatter with wgrad GEMM
886
887
                if ctx.ub_bulk_wgrad:
                    if ub_obj_wgrad.is_fp8_ubuf():
888
                        dgrad = reduce_scatter_out
889
                    else:
890
891
892
893
894
                        dgrad = ub_obj_wgrad.get_buffer(local_chunk=True).clone()

            # --------------------------------------------------
            # Grad weight has been computed...
            # --------------------------------------------------
895

896
            # Don't return grad bias if not needed
897
898
899
            if not ctx.use_bias:
                grad_bias = None

900
            # Make sure all tensor-parallel communication is finished
901
902
903
904
905
906
907
908
            if inputmat_total_work is not None:
                inputmat_total_work.wait()
                inputmat_total_work = None
            if dgrad_work is not None:
                dgrad_work.wait()
                dgrad_work = None

        if ctx.requires_wgrad:
909
            # Handle custom DDP from mcore.
910
911
912
913
914
            if (
                ctx.fuse_wgrad_accumulation
                and weight is not None
                and hasattr(weight, "grad_added_to_main_grad")
            ):
915
                weight.grad_added_to_main_grad = True
916
                if getattr(weight, "zero_out_wgrad", False):
917
918
919
920
                    wgrad = get_dummy_wgrad(
                        list(weight.main_grad.shape),
                        weight.dtype,
                        zero=True,
921
                    )
922
                else:
923
924
925
                    wgrad = get_dummy_wgrad(
                        list(weight.main_grad.shape),
                        weight.dtype,
926
                    )
927
928
929
930
            elif ctx.fuse_wgrad_accumulation:
                wgrad = None
        else:
            wgrad = None
931

932
        # Update FP8 scaling factors if needed
933
        if ctx.reduce_and_update_bwd_fp8_tensors and not is_graph_capturing():
934
            nvtx_range_push(f"{nvtx_label}.reduce_and_update_fp8_tensors")
935
            FP8GlobalStateManager.reduce_and_update_fp8_tensors(forward=False)
936
            nvtx_range_pop(f"{nvtx_label}.reduce_and_update_fp8_tensors")
937

938
        # Scatter fp8 weight buffers
939
        if ctx.fp8 and not isinstance(weight, QuantizedTensorBase):
940
            _fsdp_scatter_tensors(ctx.fsdp_group, weight_fp8)
941
        return (
942
            wgrad,
943
944
            dgrad.view(ctx.inp_shape) if ctx.requires_dgrad else None,
            grad_bias,
945
946
947
            None,  # is_first_microbatch
            None,  # fp8
            None,  # fp8_calibration
948
            None,  # wgrad_store
949
950
951
952
            None,  # input_quantizer
            None,  # weight_quantizer
            None,  # output_quantizer
            None,  # grad_input_quantizer
953
954
            None,  # grad_weight_quantizer
            None,  # grad_output_quantizer
955
956
957
958
959
960
961
962
963
            None,  # fuse_wgrad_accumulation
            None,  # cpu_offloading
            None,  # tp_group
            None,  # tp_size
            None,  # sequence_parallel
            None,  # tensor_parallel
            None,  # activation_dtype
            None,  # parallel_mode
            None,  # is_grad_enabled
964
965
966
967
968
969
            None,  # ub_overlap_rs_fprop
            None,  # ub_overlap_ag_dgrad
            None,  # ub_overlap_ag_fprop
            None,  # ub_overlap_rs_dgrad
            None,  # ub_bulk_dgrad
            None,  # ub_bulk_wgrad
970
            None,  # ub_name
971
            None,  # fp8_output
972
            None,  # fsdp_group
973
974
            None,  # module
            None,  # skip_fp8_weight_update
975
            None,  # symmetric_ar_type
976
            None,  # save_original_input
977
            None,  # debug
978
979
980
981
        )


class Linear(TransformerEngineBaseModule):
982
    """Applies a linear transformation to the incoming data :math:`y = xA^T + b`
983
984
985
986
987
988
989
990
991
992
993
994
995
996

    On NVIDIA GPUs it is a drop-in replacement for `torch.nn.Linear`.

    Parameters
    ----------
    in_features : int
                 size of each input sample.
    out_features : int
                  size of each output sample.
    bias : bool, default = `True`
          if set to `False`, the layer will not learn an additive bias.
    init_method : Callable, default = `None`
                 used for initializing weights in the following way: `init_method(weight)`.
                 When set to `None`, defaults to `torch.nn.init.normal_(mean=0.0, std=0.023)`.
997
    get_rng_state_tracker : Callable, default = `None`
998
                 used to get the random number generator state tracker for initializing weights.
999
1000
    rng_tracker_name : str, default = `None`
                 the param passed to get_rng_state_tracker to get the specific rng tracker.
cyanguwa's avatar
cyanguwa committed
1001
    parameters_split : Optional[Union[Tuple[str, ...], Dict[str, int]]], default = None
1002
1003
1004
1005
1006
1007
1008
                      Configuration for splitting the weight and bias tensors along dim 0 into
                      multiple PyTorch parameters. If a list or tuple of strings is provided,
                      they are used to make the names of equally-sized parameters. If a dict
                      (preferably an OrderedDict) is provided, the keys are used as names and
                      values as split sizes along dim 0. The resulting parameters will have
                      names that end in `_weight` or `_bias`, so trailing underscores are
                      stripped from any provided names.
1009
    device : Union[torch.device, str], default = "cuda"
1010
          The device on which the parameters of the model will be allocated. It is the user's
1011
1012
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
1013
1014
    name: str, default = `None`
        name of the module, currently used for debugging purposes.
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027

    Parallelism parameters
    ----------------------
    sequence_parallel : bool, default = `False`
                       if set to `True`, uses sequence parallelism.
    tp_group : ProcessGroup, default = `None`
              tensor parallel process group.
    tp_size : int, default = 1
             used as TP (tensor parallel) world size when TP groups are not formed during
             initialization. In this case, users must call the
             `set_tensor_parallel_group(tp_group)` method on the initialized module before the
             forward pass to supply the tensor parallel group needed for tensor and sequence
             parallel collectives.
1028
    parallel_mode : {None, 'column', 'row'}, default = `None`
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
                   used to decide whether this Linear layer is Column Parallel Linear or Row
                   Parallel Linear as described `here <https://arxiv.org/pdf/1909.08053.pdf>`_.
                   When set to `None`, no communication is performed.

    Optimization parameters
    -----------------------
    fuse_wgrad_accumulation : bool, default = 'False'
                             if set to `True`, enables fusing of creation and accumulation of
                             the weight gradient. When enabled, it is assumed that the weights
                             have an additional `main_grad` attribute (used instead of the
                             regular `grad`) which is a pre-allocated buffer of the correct
                             size to accumulate gradients in.
    return_bias : bool, default = `False`
                 when set to `True`, this module will not apply the additive bias itself, but
                 instead return the bias value during the forward pass together with the
                 output of the linear transformation :math:`y = xA^T`. This is useful when
                 the bias addition can be fused to subsequent operations.
1046
    params_dtype : torch.dtype, default = `torch.get_default_dtype()`
1047
1048
1049
                  it controls the type used to allocate the initial parameters. Useful when
                  the model is trained with lower precision and the original FP32 parameters
                  would not fit in GPU memory.
1050
1051
1052
1053
    delay_wgrad_compute : bool, default = `False`
                         Whether or not to delay weight gradient computation. If set to `True`,
                         it's the user's responsibility to call `module.backward_dw` to compute
                         weight gradients.
1054
1055
1056
1057
1058
    symmetric_ar_type : {None, 'multimem_all_reduce', 'two_shot', 'one_shot'}, default = None
                   Type of symmetric memory all-reduce to use during the forward pass.
                   This can help in latency bound communication situations.
                   Requires PyTorch version 2.7.0 or higher. When set to None, standard all-reduce
                   is used.
1059
1060
1061
1062
1063
    save_original_input : bool, default = `False`
                       If set to `True`, always saves the original input tensor rather than the
                       cast tensor. In some scenarios, the input tensor is used by multiple modules,
                       and saving the original input tensor may reduce the memory usage.
                       Cannot work with FP8 DelayedScaling recipe.
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
    """

    def __init__(
        self,
        in_features: int,
        out_features: int,
        sequence_parallel: bool = False,
        fuse_wgrad_accumulation: bool = False,
        tp_group: Optional[dist_group_type] = None,
        tp_size: int = 1,
        get_rng_state_tracker: Optional[Callable] = None,
1075
        rng_tracker_name: Optional[str] = None,
1076
1077
1078
        init_method: Optional[Callable] = None,
        bias: bool = True,
        return_bias: bool = False,
1079
        params_dtype: Optional[torch.dtype] = None,
1080
        parallel_mode: Optional[str] = None,
cyanguwa's avatar
cyanguwa committed
1081
        parameters_split: Optional[Union[Tuple[str, ...], Dict[str, int]]] = None,
1082
        device: Union[torch.device, str] = "cuda",
1083
        ub_overlap_ag: bool = False,
1084
        ub_overlap_rs: bool = False,
1085
        ub_overlap_rs_dgrad: bool = False,
1086
1087
        ub_bulk_dgrad: bool = False,
        ub_bulk_wgrad: bool = False,
1088
        ub_name: Optional[str] = None,
1089
        delay_wgrad_compute: bool = False,
1090
        symmetric_ar_type: Optional[str] = None,
1091
        save_original_input: bool = False,
1092
        name: Optional[str] = None,
1093
1094
    ) -> None:
        super().__init__()
1095
1096

        params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
1097
1098
1099
1100
1101
1102
        self.in_features = in_features
        self.out_features = out_features
        self.fuse_wgrad_accumulation = fuse_wgrad_accumulation
        self.use_bias = bias
        self.return_bias = return_bias
        self.apply_bias = bias and not return_bias
1103
1104
        self.get_rng_state_tracker = get_rng_state_tracker
        self.rng_tracker_name = rng_tracker_name
1105
        self.symmetric_ar_type = symmetric_ar_type
1106
        self.save_original_input = save_original_input
1107
1108
        self.name = name

1109
1110
        self.wgrad_store = WeightGradStore(delay_wgrad_compute, ub_bulk_wgrad)

1111
1112
        if device == "meta":
            assert parameters_split is None, "Cannot split module parameters on 'meta' device."
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
        if tp_group is None:
            self.tp_size = tp_size
            if tp_size == 1:
                self.set_tensor_parallel_group(tp_group)
        else:
            self.tp_size = get_distributed_world_size(tp_group)
            self.set_tensor_parallel_group(tp_group)
        self.set_nccl_overlap_warning_if_tp()

        self.parallel_mode = parallel_mode
        assert (
            self.parallel_mode in GemmParallelModes
        ), f"parallel_mode {parallel_mode} not supported"

        if self.parallel_mode == "column":
            self.out_features = divide(self.out_features, self.tp_size)
        elif self.parallel_mode == "row":
            self.in_features = divide(self.in_features, self.tp_size)

        self.sequence_parallel = (self.tp_size > 1) and sequence_parallel

1134
        # Column parallel TP overlap options
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
        self.ub_overlap_ag_fprop = (
            self.parallel_mode == "column" and self.sequence_parallel and ub_overlap_ag
        )
        self.ub_overlap_rs_dgrad = (
            self.parallel_mode == "column" and self.sequence_parallel and ub_overlap_rs_dgrad
        )
        self.ub_bulk_dgrad = (
            self.parallel_mode == "column"
            and self.sequence_parallel
            and ub_bulk_dgrad
            and not self.ub_overlap_rs_dgrad
        )
        self.ub_bulk_wgrad = (
            self.parallel_mode == "column"
            and self.sequence_parallel
            and ub_bulk_wgrad
            and not self.ub_overlap_rs_dgrad
        )
1153
1154

        # Row parallel TP overlap options
1155
1156
1157
1158
1159
1160
        self.ub_overlap_rs_fprop = (
            self.parallel_mode == "row" and self.sequence_parallel and ub_overlap_rs
        )
        self.ub_overlap_ag_dgrad = (
            self.parallel_mode == "row" and self.sequence_parallel and ub_overlap_ag
        )
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174

        if any(
            [
                self.ub_overlap_rs_fprop,
                self.ub_overlap_ag_dgrad,
                self.ub_overlap_ag_fprop,
                self.ub_overlap_rs_dgrad,
                self.ub_bulk_dgrad,
                self.ub_bulk_wgrad,
            ]
        ):
            assert ub_name is not None, f"Comm+GEMM overlap layer '{ub_name}' is not initialized."
        self.ub_name = ub_name

1175
1176
1177
1178
1179
1180
1181
        if self.symmetric_ar_type is not None:
            assert torch_version() >= (
                2,
                7,
                0,
            ), "Torch version must be at least 2.7 to use symmetric memory"

1182
1183
1184
        # Initialize params in FP8
        with_fp8_params = FP8GlobalStateManager.with_fp8_parameters()

1185
1186
1187
1188
1189
1190
1191
1192
        # Contiguous buffers for params
        weight_tensor = torch.empty(
            self.out_features,
            self.in_features,
            device=device,
            dtype=params_dtype,
        )
        bias_tensor = None
1193
        if self.use_bias:
1194
1195
1196
1197
1198
            bias_tensor = torch.empty(
                self.out_features,
                device=device,
                dtype=params_dtype,
            )
1199

1200
1201
1202
1203
        # Configure parameter splits
        self.weight_names = []
        self.bias_names = []
        self.parameter_split_sizes = []
1204
        if parameters_split is None:
1205
1206
1207
1208
1209
1210
            # Split into a single parameter by default
            self.weight_names = ["weight"]
            self.bias_names = ["bias"]
            self.parameter_split_sizes = [out_features]
        elif not parameters_split:
            raise ValueError("Cannot split weight buffer into 0 parameters")
cyanguwa's avatar
cyanguwa committed
1211
        elif isinstance(parameters_split, dict):
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
            # Split parameters with provided sizes
            for name, split_size in parameters_split.items():
                self.weight_names.append(f"{name.rstrip('_')}_weight")
                self.bias_names.append(f"{name.rstrip('_')}_bias")
                self.parameter_split_sizes.append(split_size)
        elif all(isinstance(name, str) for name in parameters_split):
            # Split parameters evenly
            split_size = out_features // len(parameters_split)
            for name in parameters_split:
                self.weight_names.append(f"{name.rstrip('_')}_weight")
                self.bias_names.append(f"{name.rstrip('_')}_bias")
                self.parameter_split_sizes.append(split_size)
cyanguwa's avatar
cyanguwa committed
1224
        else:
1225
            raise TypeError("Invalid configuration for parameters split")
1226

1227
1228
1229
1230
1231
1232
        # Make sure parameter splits are valid
        if sum(self.parameter_split_sizes) != out_features:
            raise ValueError(
                f"Trying to split weight buffer ({out_features=}) "
                f"with split sizes {self.parameter_split_sizes}"
            )
1233

1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
        # Adjust parameter splits for tensor-parallel distribution
        if self.parallel_mode == "column":
            for i, size in enumerate(self.parameter_split_sizes):
                if size % self.tp_size != 0:
                    raise RuntimeError(
                        f"Attempting to distribute a parameter with out_features={size} "
                        f"between {self.tp_size} tensor-parallel processes"
                    )
                self.parameter_split_sizes[i] = size // self.tp_size

1244
1245
1246
1247
1248
        # Construct weight parameters
        # Note: Register weights together so that they are adjacent to
        # each other in Linear.parameters(). This makes it more likely
        # that they will stay contiguous if the weights are
        # manipulated externally, e.g. by FSDP.
1249
1250
1251
1252
1253
1254
1255
1256
        offset = 0
        for i, split_size in enumerate(self.parameter_split_sizes):
            split_start = offset
            offset += split_size
            split_end = offset

            # Check if parameters are subviews of buffers
            is_subview = (split_start, split_end) != (0, self.out_features)
1257
            if is_subview and with_fp8_params:
1258
1259
1260
                raise RuntimeError(
                    "Splitting QuantizedTensor into multiple params is not supported"
                )
1261

1262
            # Construct weight parameter
1263
1264
1265
1266
1267
1268
1269
            self.register_parameter(
                self.weight_names[i],
                torch.nn.Parameter(weight_tensor[split_start:split_end]),
                init_fn=init_method,
                get_rng_state_tracker=get_rng_state_tracker,
                fp8_meta_index=tex.FP8FwdTensors.GEMM1_WEIGHT,
            )
1270

1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
        # Construct bias parameters if needed
        if self.use_bias:
            offset = 0
            for i, split_size in enumerate(self.parameter_split_sizes):
                split_start = offset
                offset += split_size
                split_end = offset
                self.register_parameter(
                    self.bias_names[i],
                    torch.nn.Parameter(bias_tensor[split_start:split_end]),
                    init_fn=init_method_constant(0.0),
                )
        else:
            for name in self.bias_names:
                bias = torch.Tensor().to(dtype=params_dtype, device=device)
                setattr(self, name, bias)
cyanguwa's avatar
cyanguwa committed
1287

1288
        if with_fp8_params:
1289
1290
            self.init_fp8_metadata()

1291
        self.reset_parameters(defer_init=device == "meta")
1292

1293
1294
1295
1296
1297
1298
1299
        # For RPL, bias has to be added after TP collectives
        # So it cannot be fused with the GEMM
        if self.parallel_mode == "row" and self.apply_bias:
            self.gemm_bias_unfused_add = True
        else:
            self.gemm_bias_unfused_add = False

1300
1301
1302
1303
1304
        if self.wgrad_store.delay_wgrad_compute():
            for name, param in self.named_parameters():
                if name in self.weight_names or name in self.bias_names:
                    param.skip_backward_post_hook = True

1305
1306
1307
1308
1309
1310
1311
1312
    def set_meta_tensor(self, fwd: bool, recipe: Recipe) -> None:
        """Init scales and amaxes for fwd | bwd."""
        super().set_meta_tensor(fwd, recipe)

        # customize quantizers based on each recipe & layer configs
        recipe = FP8GlobalStateManager.get_fp8_recipe()
        if recipe.float8_current_scaling():
            self._customize_quantizers_float8_current_scaling(fwd, recipe)
1313
1314
        elif recipe.float8_block_scaling():
            self._customize_quantizers_float8_blockwise_scaling(fwd, recipe)
1315
1316
        # elif for other recipes (mxfp8, etc.)

1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
    def reset_parameters(self, defer_init=False):
        super().reset_parameters(defer_init=defer_init)

        if not defer_init:
            # Set parallelism attributes for linear weights
            for weight in self.weight_names:
                set_tensor_model_parallel_attributes(
                    tensor=getattr(self, weight),
                    is_parallel=True,
                    dim=1 if self.parallel_mode == "row" else 0,
                    stride=1,
                )

            # Set parallelism attributes for linear biases
            if self.use_bias:
                for bias in self.bias_names:
                    if self.parallel_mode == "row":
                        setattr(getattr(self, bias), "sequence_parallel", self.sequence_parallel)
                    elif self.parallel_mode == "column":
                        set_tensor_model_parallel_attributes(getattr(self, bias), True, 0, 1)

1338
    @no_torch_dynamo()
1339
1340
1341
1342
    def forward(
        self,
        inp: torch.Tensor,
        is_first_microbatch: Optional[bool] = None,
1343
        fp8_output: Optional[bool] = False,
1344
        fp8_grad: Optional[bool] = False,
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
    ) -> Union[torch.Tensor, Tuple[torch.Tensor, ...]]:
        """
        Apply the linear transformation to the input.

        Parameters
        ----------
        inp : torch.Tensor
             Input tensor.
        is_first_microbatch : {True, False, None}, default = None
                             During training using either gradient accumulation or
                             pipeline parallelism a minibatch of data is further split
                             into microbatches. Between the microbatches of the same minibatch
                             the model weights are not updated. Setting this parameter indicates
                             whether the current microbatch is the first in a minibatch or not.
                             When set, this parameter enables additional optimizations:

                             * during FP8 training, it allows caching of the FP8 versions of
                               the weights
                             * it also allows skipping gradient accumulation during the
                               first microbatch (since it is the first gradient being
                               produced)
        """
1367
1368
1369
        if is_in_onnx_export_mode():
            return self.onnx_forward(inp, fp8_output)

1370
        debug = self.is_debug_iter()
1371

1372
1373
1374
1375
        if FP8GlobalStateManager.fp8_graph_capturing():
            skip_fp8_weight_update = FP8GlobalStateManager.get_skip_fp8_weight_update_tensor()
        else:
            skip_fp8_weight_update = None
1376
1377
1378
        if skip_fp8_weight_update is not None:
            is_first_microbatch = False

1379
        if self.ub_overlap_rs_fprop:
1380
1381
1382
            if get_ub(
                self.ub_name + "_fprop", FP8GlobalStateManager.is_fp8_enabled()
            ).is_fp8_ubuf():
1383
1384
                fp8_output = True
        if self.ub_overlap_rs_dgrad:
1385
1386
1387
            if get_ub(
                self.ub_name + "_dgrad", FP8GlobalStateManager.is_fp8_enabled()
            ).is_fp8_ubuf():
1388
1389
                fp8_grad = True

1390
1391
1392
        with torch.cuda.device(
            getattr(self, list(self.named_parameters())[0][0]).device
        ), self.prepare_forward(
1393
            inp,
1394
            allow_non_contiguous=isinstance(inp, QuantizedTensor),
1395
        ) as inp:
1396

1397
            weight_tensor, bias_tensor = self._get_weight_and_bias_tensors()
1398

1399
1400
1401
1402
1403
            quantizers = (
                self._get_quantizers(fp8_output, fp8_grad)
                if not debug
                else self._get_debug_quantizers(fp8_output, fp8_grad)
            )
1404

1405
            if debug:
1406
                if self.no_debug_features_active(quantizers):
1407
                    debug = False
1408
                    quantizers = self._get_quantizers(fp8_output, fp8_grad)
1409

1410
1411
1412
1413
1414
            (
                input_quantizer,
                weight_quantizer,
                output_quantizer,
                grad_input_quantizer,
1415
1416
1417
                grad_weight_quantizer,
                grad_output_quantizer,
            ) = quantizers
1418

1419
1420
1421
1422
1423
1424
1425
1426
1427
            if torch.is_grad_enabled():
                linear_fn = _Linear.apply
                args = []
            else:
                linear_fn = _Linear.forward
                args = [None]
            args += (
                weight_tensor,
                inp,
1428
                bias_tensor if (self.apply_bias and not self.gemm_bias_unfused_add) else None,
1429
1430
1431
                is_first_microbatch,
                self.fp8,
                self.fp8_calibration,
1432
                self.wgrad_store,
1433
1434
1435
1436
                input_quantizer,
                weight_quantizer,
                output_quantizer,
                grad_input_quantizer,
1437
1438
                grad_weight_quantizer,
                grad_output_quantizer,
1439
                self.fuse_wgrad_accumulation,
1440
                is_cpu_offload_enabled(),
1441
1442
1443
1444
1445
1446
1447
                self.tp_group,
                self.tp_size,
                self.sequence_parallel,
                self.tp_size > 1,
                self.activation_dtype,
                self.parallel_mode,
                torch.is_grad_enabled(),
1448
1449
1450
1451
1452
1453
                self.ub_overlap_rs_fprop,
                self.ub_overlap_ag_dgrad,
                self.ub_overlap_ag_fprop,
                self.ub_overlap_rs_dgrad,
                self.ub_bulk_dgrad,
                self.ub_bulk_wgrad,
1454
                self.ub_name,
1455
                fp8_output,
1456
                self.fsdp_group,
1457
1458
                self,
                skip_fp8_weight_update,
1459
                self.symmetric_ar_type,
1460
                self.save_original_input,
1461
                debug,
1462
1463
1464
1465
1466
1467
1468
1469
            )
            out = linear_fn(*args)
        if self.gemm_bias_unfused_add:
            out = out + cast_if_needed(bias_tensor, self.activation_dtype)

        if self.return_bias:
            return out, cast_if_needed(bias_tensor, self.activation_dtype)
        return out
1470
1471
1472

    def _get_quantizers(self, fp8_output, fp8_grad):
        if not self.fp8:
1473
            return [None] * 6
1474
        grad_input_quantizer = None
1475
        grad_weight_quantizer = None
1476
1477
1478
        grad_output_quantizer = None
        output_quantizer = None
        input_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_INPUT]
1479
        input_quantizer.internal = True
1480
        (weight_quantizer,) = self._get_weight_quantizers()
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
        if fp8_output:
            output_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_OUTPUT]
        if torch.is_grad_enabled():
            grad_output_quantizer = self.quantizers["scaling_bwd"][tex.FP8BwdTensors.GRAD_OUTPUT1]
            grad_output_quantizer.internal = True
            if fp8_grad:
                grad_input_quantizer = self.quantizers["scaling_bwd"][tex.FP8BwdTensors.GRAD_INPUT1]
        return (
            input_quantizer,
            weight_quantizer,
            output_quantizer,
            grad_input_quantizer,
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
            grad_weight_quantizer,
            grad_output_quantizer,
        )

    def _get_debug_quantizers(self, fp8_output, fp8_grad):
        original_quantizers = self._get_quantizers(fp8_output, fp8_grad)
        assert TEDebugState.debug_enabled
        from ...debug.pytorch.debug_quantization import DebugQuantizer

        names = ["activation", "weight", "output", "dgrad", "wgrad", "gradient"]
        return tuple(
            DebugQuantizer(self.name, name, q, self.tp_group)
            for name, q in zip(names, original_quantizers)
1506
        )
1507

1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
    def _get_weight_tensors(self) -> List[Union[torch.Tensor, QuantizedTensorBase]]:
        """Get the weight tensors of the module."""
        unfused_weights = [getattr(self, name) for name in self.weight_names]
        if any(isinstance(w, QuantizedTensor) for w in unfused_weights):
            if self.fp8:
                if len(unfused_weights) != 1:
                    raise RuntimeError(
                        "Splitting QuantizedTensor into multiple params is not supported"
                    )
            else:
                warnings.warn(
                    "You are using quantized weights without quantized compute. "
                    "Please make sure this is intentional."
                )
                unfused_weights = [w.dequantize() for w in unfused_weights]
        return unfused_weights

    def _get_weight_and_bias_tensors(self) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
        # Get concatenated weight and bias tensors
        unfused_weights = self._get_weight_tensors()
        if any(isinstance(w, QuantizedTensor) for w in unfused_weights):
            if self.fp8:
                if len(unfused_weights) != 1:
                    raise RuntimeError(
                        "Splitting QuantizedTensor into multiple params is not supported"
                    )
            else:
                warnings.warn(
                    "You are using quantized weights without quantized compute. "
                    "Please make sure this is intentional."
                )
                unfused_weights = [w.dequantize() for w in unfused_weights]

        weight_tensor = noop_cat(unfused_weights)
        if self.use_bias:
            bias_tensor = noop_cat([getattr(self, name) for name in self.bias_names])
        else:
            bias_tensor = None

        return weight_tensor, bias_tensor

    def onnx_forward(
        self,
        inp: torch.Tensor,
        fp8_output: bool,
    ) -> torch.Tensor:
        """
        ONNX-compatible version of the forward function that provides numerical equivalence
        while only using operations that have defined ONNX symbolic translations.
        This simplified implementation is designed specifically for inference scenarios.
        """
        from ..export import onnx_gemm

        assert_warmed_up(self)
        assert not TEDebugState.debug_enabled, "Debug mode is not supported in ONNX export."
        weight_tensor, bias_tensor = self._get_weight_and_bias_tensors()
        (
            input_quantizer,
            weight_quantizer,
            output_quantizer,
            *_,
        ) = self._get_quantizers(fp8_output, False)
        inp_dtype = inp.dtype

        if input_quantizer is not None:
            inp_q = input_quantizer.onnx_quantize(inp)
            inp = input_quantizer.onnx_dequantize(inp_q)
            inp = inp.to(inp_dtype)

        if weight_quantizer is not None:
            weight_q = weight_quantizer.onnx_quantize(weight_tensor)
            weight_tensor = weight_quantizer.onnx_dequantize(weight_q)
        if bias_tensor is not None:
            bias_tensor = bias_tensor.to(inp_dtype)
        weight_tensor = weight_tensor.to(inp_dtype)

        if self.apply_bias:
            output = onnx_gemm(weight_tensor, inp, bias_tensor)
        else:
            output = onnx_gemm(weight_tensor, inp, None)

        if output_quantizer is not None:
            raise NotImplementedError("ONNX export of quantized output is not supported")

        if self.return_bias:
            return output, bias_tensor

        return output

1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
    def _customize_quantizers_float8_current_scaling(self, fwd: bool, recipe: Recipe) -> None:
        """Customize quantizers based on current scaling recipe + linear."""
        assert (
            recipe.float8_current_scaling()
        ), "current scaling recipe quantizer customization here"
        if fwd:
            # set configs about amax epsilon and power_2_scale
            self.quantizers["scaling_fwd"][
                tex.FP8FwdTensors.GEMM1_INPUT
            ].force_pow_2_scales = recipe.fp8_quant_fwd_inp.power_2_scale
            self.quantizers["scaling_fwd"][
                tex.FP8FwdTensors.GEMM1_INPUT
            ].amax_epsilon = recipe.fp8_quant_fwd_inp.amax_epsilon
            # also set weight quantizer with same amax_epsilon & power_2_scale
            self.quantizers["scaling_fwd"][
                tex.FP8FwdTensors.GEMM1_WEIGHT
            ].force_pow_2_scales = recipe.fp8_quant_fwd_weight.power_2_scale
            self.quantizers["scaling_fwd"][
                tex.FP8FwdTensors.GEMM1_WEIGHT
            ].amax_epsilon = recipe.fp8_quant_fwd_weight.amax_epsilon
            # paralle related
            if self.sequence_parallel and self.parallel_mode == "column":
                # customize input_quantizer with amax reduction TP group
                self.quantizers["scaling_fwd"][
                    tex.FP8FwdTensors.GEMM1_INPUT
                ].with_amax_reduction = True
                self.quantizers["scaling_fwd"][
                    tex.FP8FwdTensors.GEMM1_INPUT
                ].amax_reduction_group = self.tp_group
        else:
            # set grad_output_quantizer with amax epsilon and power_2_scale
            self.quantizers["scaling_bwd"][
                tex.FP8BwdTensors.GRAD_OUTPUT1
            ].force_pow_2_scales = recipe.fp8_quant_bwd_grad.power_2_scale
            self.quantizers["scaling_bwd"][
                tex.FP8BwdTensors.GRAD_OUTPUT1
            ].amax_epsilon = recipe.fp8_quant_bwd_grad.amax_epsilon
            # parallel related
            if self.sequence_parallel and self.parallel_mode == "row":
                # customize grad_output_quantizer with amax reduction TP group
                self.quantizers["scaling_bwd"][
                    tex.FP8BwdTensors.GRAD_OUTPUT1
                ].with_amax_reduction = True
                self.quantizers["scaling_bwd"][
                    tex.FP8BwdTensors.GRAD_OUTPUT1
                ].amax_reduction_group = self.tp_group
1643
1644
1645

    def _get_weight_quantizers(self) -> List[Quantizer]:
        """Get the weight quantizers of the module."""
1646
        if not self.fp8 and not self.fp8_calibration:
1647
1648
1649
1650
            return [None]
        weight_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_WEIGHT]
        weight_quantizer.internal = True
        return [weight_quantizer]
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669

    def _customize_quantizers_float8_blockwise_scaling(self, fwd: bool, recipe: Recipe) -> None:
        """Customize quantizers based on blockwise scaling recipe + linear."""
        assert (
            recipe.float8_block_scaling()
        ), "blockwise scaling recipe quantizer customization here"

        if fwd:
            if self.sequence_parallel and self.parallel_mode == "column":
                # set compact for inp tensor X
                self.quantizers["scaling_fwd"][
                    tex.FP8FwdTensors.GEMM1_INPUT
                ].all_gather_usage = True
        else:
            if self.sequence_parallel and self.parallel_mode == "row":
                # set compact for grad_output tensor dY
                self.quantizers["scaling_bwd"][
                    tex.FP8BwdTensors.GRAD_OUTPUT1
                ].all_gather_usage = True