linear.py 74.1 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, get_module_quantizers
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
    assert_dim_for_all_gather,
39
40
    nvtx_range_pop,
    nvtx_range_push,
41
42
43
44
45
)
from ..distributed import (
    set_tensor_model_parallel_attributes,
    get_distributed_world_size,
    allreduce,
46
    symmetric_all_reduce,
47
48
    reduce_scatter_along_first_dim,
    gather_along_first_dim,
49
    is_fp8_activation_recompute_enabled,
50
    in_fp8_activation_recompute_phase,
51
52
    _fsdp_scatter_tensors,
    _fsdp_gather_tensors,
53
54
)
from ..cpp_extensions import (
55
    general_gemm,
56
)
57
from ..constants import GemmParallelModes, dist_group_type
58
from ..jit import no_torch_dynamo
59
from ..graph import is_graph_capturing
60
61
from ..tensor.quantized_tensor import (
    QuantizedTensor,
62
    QuantizedTensorBase,
63
64
65
66
    Quantizer,
    prepare_for_saving,
    restore_from_saved,
)
67
from ..tensor.float8_tensor import Float8CurrentScalingQuantizer, Float8Quantizer
68
from ..tensor.mxfp8_tensor import MXFP8Quantizer
69
from ..tensor.utils import is_experimental
70
from ..export import is_in_onnx_export_mode, assert_warmed_up
71
from ..cpu_offload import is_cpu_offload_enabled, mark_activation_offload
72
from ...debug.pytorch.debug_state import TEDebugState
73

74
75
76
77
78
79
80
81
82
83
84
__all__ = ["Linear"]


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

    @staticmethod
    def forward(
        ctx,
85
        weight: torch.Tensor,
86
        inp: torch.Tensor,
87
        bias: Optional[torch.Tensor],
88
89
90
        is_first_microbatch: Union[bool, None],
        fp8: bool,
        fp8_calibration: bool,
91
        wgrad_store: WeightGradStore,
92
93
94
95
        input_quantizer: Optional[Quantizer],
        weight_quantizer: Optional[Quantizer],
        output_quantizer: Optional[Quantizer],
        grad_input_quantizer: Optional[Quantizer],
96
97
        grad_weight_quantizer: Optional[Quantizer],
        grad_output_quantizer: Optional[Quantizer],
98
        fuse_wgrad_accumulation: bool,
99
        cpu_offloading: bool,
100
101
102
103
104
105
106
        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,
107
108
109
110
111
112
        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,
113
        ub_name: str,
114
        fp8_output: bool,  # pylint: disable=unused-argument
115
        fsdp_group: Union[dist_group_type, None],
116
117
        module: torch.nn.Module,
        skip_fp8_weight_update: bool,
118
        symmetric_ar_type: str,
119
        save_original_input: bool = False,
120
        debug: Optional[bool] = False,
121
    ) -> torch.Tensor:
122
        # pylint: disable=missing-function-docstring
123

124
125
126
127
128
        # NVTX label for profiling
        nvtx_label = "transformer_engine._Linear.forward"
        if ub_name is not None:
            nvtx_label = f"{nvtx_label}.{ub_name}"

129
        # Make sure input dimensions are compatible
130
        out_features, in_features = weight.shape
131
        assert inp.shape[-1] == in_features, "GEMM not possible"
132

133
        # Configure tensor-parallel communication
134
        tp_world_size = get_distributed_world_size(tp_group)
135
136
137
138
        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
        )
139
140

        # Configure Userbuffers communication (comm+GEMM overlap)
141
142
143
144
145
146
        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
147
148
149
        ub_obj = None
        ub_type = None
        if ub_overlap_rs_fprop:
150
            ub_obj = get_ub(ub_name + "_fprop", fp8)
151
152
            ub_type = tex.CommOverlapType.RS
        elif ub_overlap_ag_fprop:
153
            ub_obj = get_ub(ub_name + "_fprop", fp8)
154
155
            ub_type = tex.CommOverlapType.AG

156
157
158
        # experimental recipe check
        experimental = is_experimental(input_quantizer) or is_experimental(weight_quantizer)

159
160
161
162
163
164
165
166
        # ------------------------------------------------------
        # 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
167
        if fp8:
168
            assert_dim_for_fp8_exec(inputmat, weight)
169
            assert_dim_for_all_gather(inputmat, with_input_all_gather_nccl, input_quantizer)
170
171
172
173
174
            if save_original_input:
                assert not isinstance(
                    input_quantizer, Float8Quantizer
                ), "DelayedScaling recipe is not supported with save_original_input"

175
176
177
178
179
180
        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")
181
                if not isinstance(inputmat, QuantizedTensorBase) and not experimental:
182
183
                    own_quantized_input = True
                    input_quantizer.set_usage(rowwise=True, columnwise=backward_needs_input)
184
185
186
187
188
                    if isinstance(
                        input_quantizer, (Float8Quantizer, Float8CurrentScalingQuantizer)
                    ):
                        # All-gather is not supported with FP8 column-wise data
                        input_quantizer.set_usage(columnwise=False)
189
190
191
192
193
                    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
194
                    inputmat = input_quantizer(inputmat)
195
            else:
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
                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)
221
                else:
222
223
                    if input_quantizer is None:
                        raise ValueError("Missing quantizer for input tensor")
224
225
226
                    input_quantizer.set_usage(
                        rowwise=True, columnwise=backward_needs_input and not save_original_input
                    )
227
                    inputmat = input_quantizer(inputmat)
228
                    own_quantized_input = True
229
            else:
230
231
                inputmat = cast_if_needed(inp, activation_dtype)  # Cast for AMP
            inputmat_total = inputmat
232
        nvtx_range_pop(f"{nvtx_label}.input_cast_comm")
233
234
235
        # ------------------------------------------------------
        # Input tensor is ready for GEMM...
        # ------------------------------------------------------
236

237
238
239
        # ------------------------------------------------------
        # Prepare weight tensor
        # ------------------------------------------------------
240
241
        weightmat = weight
        if fp8 or debug:
242
243
244
245
246
247
248
249
250
            # 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)
251
252

            # Get quantized weight
253
254
255
256
257
258
259
260
            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,
261
                workspace_dtype=activation_dtype,
262
            )
263
264
            weightmat.update_usage(rowwise_usage=True)

265
        else:
266
267
268
269
            weightmat = cast_if_needed(weightmat, activation_dtype)  # Cast for AMP
        # ------------------------------------------------------
        # Weight tensor is ready for GEMM...
        # ------------------------------------------------------
270
271
272

        # Cast bias to expected dtype
        bias_dtype = activation_dtype
273
        if needs_quantized_gemm(inputmat_total) and activation_dtype == torch.float32:
274
            # cuBLAS does not support FP8 GEMM with FP32 bias, so we cast to BF16
275
276
277
278
279
280
281
282
283
284
            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)

285
286
        # Choose whether to use GEMM kernel with split accumulator
        use_split_accumulator = _2X_ACC_FPROP
287
288
289
        if fp8:
            recipe = FP8GlobalStateManager.get_fp8_recipe()
            if hasattr(recipe, "fp8_gemm_fprop"):
290
291
292
293
294
                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)
295

296
297
298
299
300
301
302
303
304
305
306
307
308
309
        # 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(
310
311
312
313
            weightmat,
            inputmat_total,
            get_workspace(),
            quantization_params=output_quantizer,
314
            out_dtype=activation_dtype,
315
            bias=bias,
316
            use_split_accumulator=use_split_accumulator,
317
318
            ub=ub_obj,
            ub_type=ub_type,
319
            extra_output=reduce_scatter_out,
320
        )
321
        nvtx_range_pop(f"{nvtx_label}.gemm")
322
323
324
325
        # ------------------------------------------------------
        # Finished forward GEMM...
        # ------------------------------------------------------

326
327
328
329
330
331
332
        # Deallocate GEMM input tensor if no longer needed
        # TODO(yuzhongw, tmoon): Figure out why inputmat_total is not automatically
        # deallocated by GC. Manually deallocating is a temporary hack.
        if with_input_all_gather_nccl:
            clear_tensor_data(inputmat_total)
            inputmat_total = None

333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
        # ------------------------------------------------------
        # 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
        # ------------------------------------------------------
360
361

        if is_grad_enabled:
362
363
364
            if save_original_input:
                inputmat = inp

365
            ctx.weight_quantizer = weight_quantizer
366
367
368
369
370

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

371
372
373
374
375
376
            # Discard unneeded data in input tensor
            if (
                backward_needs_input
                and own_quantized_input
                and isinstance(inputmat, QuantizedTensorBase)
            ):
377
378
379
                if (
                    ctx.backward_input_needs_gather
                    and weight_quantizer.supports_only_rowwise_all_gather()
380
381
382
383
384
385
386
387
388
                ):
                    # 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
389
390
            if backward_needs_input:
                saved_inputmat = inputmat
391

392
393
            # Weight with column-wise usage is needed for dgrad GEMM.
            if inp.requires_grad:
394
                if isinstance(weightmat, QuantizedTensorBase):
395
396
                    weightmat.update_usage(columnwise_usage=True)

397
398
            if cpu_offloading and saved_inputmat is not None:
                mark_activation_offload(saved_inputmat)
399

400
401
            # Scatter intermediate/activation tensors saved for the backward pass
            # NOTE: FSDP sharding is not valid for models initialized with primary Fp8 weights
402
            nvtx_range_push(f"{nvtx_label}.fsdp_scatter")
403
404
405
            ctx.fsdp_group = fsdp_group
            ctx.fsdp_shapes = _fsdp_scatter_tensors(
                fsdp_group,
406
                saved_inputmat,
407
                weightmat if fp8 and not isinstance(weight, QuantizedTensorBase) else None,
408
            )
409
            nvtx_range_pop(f"{nvtx_label}.fsdp_scatter")
410

411
412
413
414
415
416
417
418
419
420
421
            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

422
423
            # TODO(ksivamani): Check memory usage
            tensors_to_save, tensor_objects = prepare_for_saving(
424
                saved_inputmat,
425
                weightmat,
426
                weight,
427
                bias,
428
            )
429
430
            ctx.save_for_backward(*tensors_to_save)
            ctx.tensor_objects = tensor_objects
431

432
433
            ctx.activation_dtype = activation_dtype
            ctx.fp8 = fp8
434
            ctx.fp8_recipe = FP8GlobalStateManager.get_fp8_recipe() if fp8 else None
435
436
            ctx.input_quantizer = input_quantizer
            ctx.grad_input_quantizer = grad_input_quantizer
437
438
            ctx.grad_weight_quantizer = grad_weight_quantizer
            ctx.grad_output_quantizer = grad_output_quantizer
439
            ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
440
            if fuse_wgrad_accumulation and weight.requires_grad:
441
442
443
444
445
446
447
448
                # 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
449

450
            ctx.debug = debug
451
            ctx.experimental = experimental
452
            ctx.cpu_offloading = cpu_offloading
453
            ctx.is_first_microbatch = is_first_microbatch
454
            ctx.use_bias = bias is not None
455
456
            ctx.sequence_parallel = sequence_parallel
            ctx.tensor_parallel = tensor_parallel
457
            ctx.inp_shape = inp.shape
458
459
            ctx.parallel_mode = parallel_mode
            ctx.tp_group = tp_group
460
461
462
463
            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
464
            ctx.ub_name = ub_name
465
466
            ctx.tp_size = tp_size
            ctx.requires_dgrad = inp.requires_grad
467
            ctx.requires_wgrad = weight.requires_grad
468
            ctx.reduce_and_update_bwd_fp8_tensors = False
469

470
            ctx.owns_input = saved_inputmat is not inp
471
            if ctx.fp8 and requires_grad(inp, weight, bias):
472
473
474
475
                _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
476
            ctx.wgrad_store = wgrad_store
477

478
479
480
        # ------------------------------------------------------
        # Cached state for backward pass is ready...
        # ------------------------------------------------------
481

482
        return out
483
484

    @staticmethod
485
    def backward(ctx, grad_output: torch.Tensor) -> Tuple[Union[torch.Tensor, None], ...]:
486
        # pylint: disable=missing-function-docstring
487

488
489
490
491
492
        # 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}"

493
        with torch.cuda.nvtx.range("_Linear_backward"):
494
495
496
497
            saved_tensors = ctx.saved_tensors
            inputmat, weight_fp8, weight, bias = (  # pylint: disable=unbalanced-tuple-unpacking
                restore_from_saved(ctx.tensor_objects, saved_tensors)
            )
498

499
500
501
            # 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
502
503
504

            # Since main_grad can be modified inplace, it should not be a part of saved_tensors
            main_grad = (
505
                ctx.main_grad_func()
506
507
508
509
                if weight is not None and ctx.fuse_wgrad_accumulation and ctx.requires_wgrad
                else None
            )

510
511
512
513
514
            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
515

516
517
518
            # 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
519
            nvtx_range_push(f"{nvtx_label}.fsdp_gather")
520
521
522
523
            _fsdp_gather_tensors(
                ctx.fsdp_group,
                ctx.fsdp_shapes,
                inputmat,
524
                weight_fp8,
525
            )
526
            nvtx_range_pop(f"{nvtx_label}.fsdp_gather")
527

528
            # Configure Userbuffers communication (comm+GEMM overlap)
529
            ctx.ub_obj_gradout = None
530
            ub_obj_dgrad = None
531
            ub_obj_wgrad = None
532
533
            ub_type_dgrad = None
            ub_type_wgrad = None
534
            dgrad_shape = [reduce(multiply_op, ctx.inp_shape[:-1]), ctx.inp_shape[-1]]
535
            if ctx.ub_overlap_ag:
536
                # Overlap grad_output all-gather with dgrad compute
537
                ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad", ctx.fp8)
538
539
                ub_obj_dgrad = ctx.ub_obj_gradout
                ub_type_dgrad = tex.CommOverlapType.AG
540
541
            elif ctx.ub_overlap_rs_dgrad:
                # Overlap dgrad reduce-scatter with dgrad compute
542
                ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad", ctx.fp8)
543
544
                ub_obj_dgrad = ctx.ub_obj_gradout
                ub_type_dgrad = tex.CommOverlapType.RS
545
546
547
            else:
                if ctx.ub_bulk_dgrad:
                    # Overlap inputmat all-gather with dgrad compute
548
                    ctx.ub_obj_gradout = get_ub(ctx.ub_name + "_dgrad", ctx.fp8)
549
550
                    ub_obj_dgrad = ctx.ub_obj_gradout
                    ub_type_dgrad = tex.CommOverlapType.AG
551
552
                if ctx.ub_bulk_wgrad:
                    # Overlap dgrad reduce-scatter with wgrad compute
553
                    ub_obj_wgrad = get_ub(ctx.ub_name + "_wgrad", ctx.fp8)
554
                    ub_type_wgrad = tex.CommOverlapType.RS
555
556
557
558
559
560
561
562

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

            # Unmodified grad output tensor
            grad_output_arg = grad_output
563

564
565
566
567
            # 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:
568
569
570
571
572
573
574
                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)
575

576
577
578
579
580
581
582
583
584
585
586
587
588
            # 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)

589
            # Prepare grad output tensor
590
            nvtx_range_push(f"{nvtx_label}.grad_output_preprocess")
591
592
593
594
            (
                grad_output,
                grad_bias,
            ) = TransformerEngineBaseModule.grad_output_preprocess(
595
596
597
598
                ctx,
                grad_output,
                ctx.parallel_mode == "row",
                ctx.grad_output_quantizer,
599
            )
600
            nvtx_range_pop(f"{nvtx_label}.grad_output_preprocess")
601

602
603
604
605
606
607
608
609
610
611
            # --------------------------------------------------
            # 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.
            # --------------------------------------------------
612
            inputmat_total = None
613
            inputmat_total_work = None
614
615
            if ctx.requires_wgrad:
                if ctx.fp8 or ctx.debug:
616
617
618
                    if isinstance(inputmat, QuantizedTensorBase):
                        # Input tensor is already quantized
                        pass
619
                    elif ctx.debug or ctx.experimental:
620
621
622
623
                        # Debug quantizer will be applied immediately before wgrad GEMM
                        pass
                    else:
                        # Quantize input tensor
624
                        quantizer = ctx.input_quantizer
625
                        if quantizer.supports_only_rowwise_all_gather():
626
                            # All-gather is not supported with FP8 column-wise data
627
628
629
630
                            quantizer.set_usage(
                                rowwise=True,
                                columnwise=not ctx.backward_input_needs_gather,
                            )
631
                        else:
632
                            quantizer.set_usage(rowwise=False, columnwise=True)
633
634
                        inputmat = quantizer(inputmat)
                else:
635
                    if isinstance(inputmat, QuantizedTensorBase):
636
637
638
                        inputmat = inputmat.dequantize(dtype=ctx.activation_dtype)
                    else:
                        inputmat = cast_if_needed(inputmat, ctx.activation_dtype)
639
            if ctx.backward_input_needs_gather:
640
                quantizer = None
641
                if ctx.fp8 or ctx.debug:
642
                    quantizer = ctx.input_quantizer
643
                    if quantizer.supports_only_rowwise_all_gather():
644
645
646
647
648
                        # 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)
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
                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")
665
666
            else:
                inputmat_total = inputmat
667
668
669
            # --------------------------------------------------
            # Input tensor is ready for computing grad weight...
            # --------------------------------------------------
670

671
            # --------------------------------------------------
672
            # Compute grad input tensor
673
674
            # --------------------------------------------------

675
676
            dgrad = None
            dgrad_work = None
677
            if ctx.requires_dgrad:
678

679
680
681
682
683
684
685
686
                # 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
687
688
689
                if ctx.fp8:
                    recipe = ctx.fp8_recipe
                    if hasattr(recipe, "fp8_gemm_dgrad"):
690
                        use_split_accumulator = recipe.fp8_gemm_dgrad.use_split_accumulator
691

692
693
694
695
696
697
698
699
700
701
                # 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
702
                    )
703
704
                elif ctx.ub_bulk_wgrad:
                    gemm_out = ub_obj_wgrad.get_buffer(local_chunk=False)
705

706
707
                # dgrad GEMM
                # Note: dx = dy * w
708

709
710
                nvtx_range_push(f"{nvtx_label}.dgrad_gemm")
                gemm_out, *_, reduce_scatter_out = general_gemm(
711
712
713
714
715
716
                    weight_fp8,
                    grad_output,
                    get_workspace(),
                    layout="NN",
                    grad=True,
                    quantization_params=ctx.grad_input_quantizer,
717
                    out=gemm_out,
718
                    out_dtype=ctx.activation_dtype,
719
                    use_split_accumulator=use_split_accumulator,
720
721
                    ub=ub_obj_dgrad,
                    ub_type=ub_type_dgrad,
722
                    extra_output=reduce_scatter_out,
723
724
                    bulk_overlap=ctx.ub_bulk_dgrad,
                )
725
                nvtx_range_pop(f"{nvtx_label}.dgrad_gemm")
726

727
728
                # Prepare grad input tensor
                # Note: Perform tensor-parallel communication
729
                if ctx.ub_overlap_rs_dgrad:
730
731
732
733
                    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:
734
                    nvtx_range_push(f"{nvtx_label}.column_parallel_comm_dgrad")
735
                    dgrad = gemm_out
736
737
738
739
740
                    if ctx.sequence_parallel:
                        dgrad, dgrad_work = reduce_scatter_along_first_dim(
                            dgrad,
                            ctx.tp_group,
                            async_op=True,
741
                        )
742
                    else:
743
                        dgrad, dgrad_work = allreduce(dgrad, ctx.tp_group, async_op=True)
744
                    nvtx_range_pop(f"{nvtx_label}.column_parallel_comm_dgrad")
745
746
747
748
749
750
751
752
753
754
                else:
                    dgrad = gemm_out

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

            # --------------------------------------------------
            # Compute grad weight
            # --------------------------------------------------
755

756
757
            wgrad = None
            if ctx.requires_wgrad:
758

759
760
761
                # Prepare input tensor
                # Note: Synchronize tensor-parallel communication and
                # make sure required data is available
762
763
764
                if inputmat_total_work is not None:
                    inputmat_total_work.wait()
                    inputmat_total_work = None
765
766
767
768
769
770
771
772
773
774
775
                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):
776
                    # UB does not support pipelined overlapping grad output
777
778
779
                    # 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
780
                    # for the dgrad GEMM. We work around by explicitly
781
782
783
784
785
786
                    # 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
787
                    ub_obj_overlap_wgrad = get_ub(ctx.ub_name + "_wgrad", ctx.fp8)
788

789
                    ctx.grad_output_quantizer.set_usage(rowwise=False, columnwise=True)
790
791
792
793
794
795
796

                    # 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,
797
                            grad_output_arg,
798
                            ctx.grad_output_quantizer,
799
800
                            ctx.tp_group,
                        )
801
802
803
804
805

                    # 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
                    )
806

807
808
809
810
811
812
                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)
813

814
815
816
817
818
819
820
                # 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

821
822
823
824
825
826
827
828
829
                # 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
830
                # reduce-scatter with wgrad GEMM
831
                reduce_scatter_out = None
832
                if ctx.ub_bulk_wgrad and ub_obj_wgrad.is_fp8_ubuf():
833
834
                    reduce_scatter_out = torch.empty(
                        dgrad_shape, dtype=ctx.activation_dtype, device=grad_output_arg.device
835
836
                    )

837
838
839
840
                # Arguments to include in wgrad GEMM closure
                wgrad_gemm_kwargs = {
                    "workspace": get_workspace(),
                    "out_dtype": (
841
842
                        main_grad.dtype if ctx.fuse_wgrad_accumulation else ctx.activation_dtype
                    ),
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
                    "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
874
                if ctx.wgrad_store is not None and ctx.wgrad_store.delay_wgrad_compute():
875
876
877
878
879
880
881
882
883
884
885
                    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)
886
887
                else:

888
889
890
891
                    # Call wgrad GEMM now
                    wgrad, grad_bias_ = wgrad_gemm(inputmat_total, grad_output)

                    # Update grad bias if needed
892
893
894
895
                    if grad_bias is None:
                        grad_bias = grad_bias_
                    del grad_bias_

896
                    # Deallocate tensors if permitted
897
                    if ctx.owns_input:
898
899
900
901
                        # Input tensor is internal
                        clear_tensor_data(inputmat_total)
                    elif ctx.backward_input_needs_gather:
                        # Gathered input tensor is internal
902
                        clear_tensor_data(inputmat_total)
903
904
905
                    if ctx.parallel_mode == "row" and ctx.sequence_parallel:
                        # Gathered grad output tensor is internal
                        clear_tensor_data(grad_output)
906

907
                # Update grad input if overlapping reduce-scatter with wgrad GEMM
908
909
                if ctx.ub_bulk_wgrad:
                    if ub_obj_wgrad.is_fp8_ubuf():
910
                        dgrad = reduce_scatter_out
911
                    else:
912
913
914
915
916
                        dgrad = ub_obj_wgrad.get_buffer(local_chunk=True).clone()

            # --------------------------------------------------
            # Grad weight has been computed...
            # --------------------------------------------------
917

918
            # Don't return grad bias if not needed
919
920
921
            if not ctx.use_bias:
                grad_bias = None

922
            # Make sure all tensor-parallel communication is finished
923
924
925
926
927
928
929
930
            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:
931
            # Handle custom DDP from mcore.
932
933
934
935
936
            if (
                ctx.fuse_wgrad_accumulation
                and weight is not None
                and hasattr(weight, "grad_added_to_main_grad")
            ):
937
                weight.grad_added_to_main_grad = True
938
                if getattr(weight, "zero_out_wgrad", False):
939
940
941
942
                    wgrad = get_dummy_wgrad(
                        list(weight.main_grad.shape),
                        weight.dtype,
                        zero=True,
943
                    )
944
                else:
945
946
947
                    wgrad = get_dummy_wgrad(
                        list(weight.main_grad.shape),
                        weight.dtype,
948
                    )
949
950
951
952
            elif ctx.fuse_wgrad_accumulation:
                wgrad = None
        else:
            wgrad = None
953

954
        # Update FP8 scaling factors if needed
955
        if ctx.reduce_and_update_bwd_fp8_tensors and not is_graph_capturing():
956
            nvtx_range_push(f"{nvtx_label}.reduce_and_update_fp8_tensors")
957
            FP8GlobalStateManager.reduce_and_update_fp8_tensors(forward=False)
958
            nvtx_range_pop(f"{nvtx_label}.reduce_and_update_fp8_tensors")
959

960
        # Scatter fp8 weight buffers
961
        if ctx.fp8 and not isinstance(weight, QuantizedTensorBase):
962
            _fsdp_scatter_tensors(ctx.fsdp_group, weight_fp8)
963
        return (
964
            wgrad,
965
966
            dgrad.view(ctx.inp_shape) if ctx.requires_dgrad else None,
            grad_bias,
967
968
969
            None,  # is_first_microbatch
            None,  # fp8
            None,  # fp8_calibration
970
            None,  # wgrad_store
971
972
973
974
            None,  # input_quantizer
            None,  # weight_quantizer
            None,  # output_quantizer
            None,  # grad_input_quantizer
975
976
            None,  # grad_weight_quantizer
            None,  # grad_output_quantizer
977
978
979
980
981
982
983
984
985
            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
986
987
988
989
990
991
            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
992
            None,  # ub_name
993
            None,  # fp8_output
994
            None,  # fsdp_group
995
996
            None,  # module
            None,  # skip_fp8_weight_update
997
            None,  # symmetric_ar_type
998
            None,  # save_original_input
999
            None,  # debug
1000
1001
1002
1003
        )


class Linear(TransformerEngineBaseModule):
1004
    """Applies a linear transformation to the incoming data :math:`y = xA^T + b`
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018

    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)`.
1019
    get_rng_state_tracker : Callable, default = `None`
1020
                 used to get the random number generator state tracker for initializing weights.
1021
1022
    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
1023
    parameters_split : Optional[Union[Tuple[str, ...], Dict[str, int]]], default = None
1024
1025
1026
1027
1028
1029
1030
                      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.
1031
    device : Union[torch.device, str], default = "cuda"
1032
          The device on which the parameters of the model will be allocated. It is the user's
1033
1034
          responsibility to ensure all parameters are moved to the GPU before running the
          forward pass.
1035
1036
    name: str, default = `None`
        name of the module, currently used for debugging purposes.
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049

    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.
1050
    parallel_mode : {None, 'column', 'row'}, default = `None`
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
                   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.
1068
    params_dtype : torch.dtype, default = `torch.get_default_dtype()`
1069
1070
1071
                  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.
1072
1073
1074
1075
    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.
1076
1077
1078
1079
1080
    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.
1081
1082
1083
1084
1085
    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.
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
    """

    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,
1097
        rng_tracker_name: Optional[str] = None,
1098
1099
1100
        init_method: Optional[Callable] = None,
        bias: bool = True,
        return_bias: bool = False,
1101
        params_dtype: Optional[torch.dtype] = None,
1102
        parallel_mode: Optional[str] = None,
cyanguwa's avatar
cyanguwa committed
1103
        parameters_split: Optional[Union[Tuple[str, ...], Dict[str, int]]] = None,
1104
        device: Union[torch.device, str] = "cuda",
1105
        ub_overlap_ag: bool = False,
1106
        ub_overlap_rs: bool = False,
1107
        ub_overlap_rs_dgrad: bool = False,
1108
1109
        ub_bulk_dgrad: bool = False,
        ub_bulk_wgrad: bool = False,
1110
        ub_name: Optional[str] = None,
1111
        delay_wgrad_compute: bool = False,
1112
        symmetric_ar_type: Optional[str] = None,
1113
        save_original_input: bool = False,
1114
        name: Optional[str] = None,
1115
1116
    ) -> None:
        super().__init__()
1117
1118

        params_dtype = torch.get_default_dtype() if params_dtype is None else params_dtype
1119
1120
1121
1122
1123
1124
        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
1125
1126
        self.get_rng_state_tracker = get_rng_state_tracker
        self.rng_tracker_name = rng_tracker_name
1127
        self.symmetric_ar_type = symmetric_ar_type
1128
        self.save_original_input = save_original_input
1129
1130
        self.name = name

1131
1132
        self.wgrad_store = WeightGradStore(delay_wgrad_compute, ub_bulk_wgrad)

1133
1134
        if device == "meta":
            assert parameters_split is None, "Cannot split module parameters on 'meta' device."
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
        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

1156
        # Column parallel TP overlap options
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
        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
        )
1175
1176

        # Row parallel TP overlap options
1177
1178
1179
1180
1181
1182
        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
        )
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196

        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

1197
1198
1199
1200
1201
1202
1203
        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"

1204
1205
1206
        # Initialize params in FP8
        with_fp8_params = FP8GlobalStateManager.with_fp8_parameters()

1207
1208
1209
1210
1211
1212
1213
1214
        # Contiguous buffers for params
        weight_tensor = torch.empty(
            self.out_features,
            self.in_features,
            device=device,
            dtype=params_dtype,
        )
        bias_tensor = None
1215
        if self.use_bias:
1216
1217
1218
1219
1220
            bias_tensor = torch.empty(
                self.out_features,
                device=device,
                dtype=params_dtype,
            )
1221

1222
1223
1224
1225
        # Configure parameter splits
        self.weight_names = []
        self.bias_names = []
        self.parameter_split_sizes = []
1226
        if parameters_split is None:
1227
1228
1229
1230
1231
1232
            # 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
1233
        elif isinstance(parameters_split, dict):
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
            # 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
1246
        else:
1247
            raise TypeError("Invalid configuration for parameters split")
1248

1249
1250
1251
1252
1253
1254
        # 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}"
            )
1255

1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
        # 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

1266
1267
1268
1269
1270
        # 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.
1271
1272
1273
1274
1275
1276
1277
1278
        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)
1279
            if is_subview and with_fp8_params:
1280
1281
1282
                raise RuntimeError(
                    "Splitting QuantizedTensor into multiple params is not supported"
                )
1283

1284
            # Construct weight parameter
1285
1286
1287
1288
1289
1290
1291
            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,
            )
1292

1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
        # 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
1309

1310
        if with_fp8_params:
1311
1312
            self.init_fp8_metadata()

1313
        self.reset_parameters(defer_init=device == "meta")
1314

1315
1316
1317
1318
1319
1320
1321
        # 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

1322
1323
1324
1325
1326
        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

1327
1328
1329
1330
1331
1332
1333
1334
    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)
1335
1336
        elif recipe.float8_block_scaling():
            self._customize_quantizers_float8_blockwise_scaling(fwd, recipe)
1337
1338
        elif recipe.nvfp4():
            self._customize_quantizers_nvfp4(fwd, recipe)
1339
1340
        # elif for other recipes (mxfp8, etc.)

1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
    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)

1362
    @no_torch_dynamo()
1363
1364
1365
1366
    def forward(
        self,
        inp: torch.Tensor,
        is_first_microbatch: Optional[bool] = None,
1367
        fp8_output: Optional[bool] = False,
1368
        fp8_grad: Optional[bool] = False,
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
    ) -> 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)
        """
1391
1392
1393
        if is_in_onnx_export_mode():
            return self.onnx_forward(inp, fp8_output)

1394
        debug = self.is_debug_iter()
1395

1396
1397
1398
1399
        if FP8GlobalStateManager.fp8_graph_capturing():
            skip_fp8_weight_update = FP8GlobalStateManager.get_skip_fp8_weight_update_tensor()
        else:
            skip_fp8_weight_update = None
1400
1401
1402
        if skip_fp8_weight_update is not None:
            is_first_microbatch = False

1403
        if self.ub_overlap_rs_fprop:
1404
1405
1406
            if get_ub(
                self.ub_name + "_fprop", FP8GlobalStateManager.is_fp8_enabled()
            ).is_fp8_ubuf():
1407
1408
                fp8_output = True
        if self.ub_overlap_rs_dgrad:
1409
1410
1411
            if get_ub(
                self.ub_name + "_dgrad", FP8GlobalStateManager.is_fp8_enabled()
            ).is_fp8_ubuf():
1412
1413
                fp8_grad = True

1414
1415
1416
        with torch.cuda.device(
            getattr(self, list(self.named_parameters())[0][0]).device
        ), self.prepare_forward(
1417
            inp,
1418
            allow_non_contiguous=isinstance(inp, QuantizedTensor),
1419
        ) as inp:
1420

1421
            weight_tensor, bias_tensor = self._get_weight_and_bias_tensors()
1422

1423
            quantizers = get_module_quantizers(self, fp8_output, fp8_grad, debug)
1424
            if debug:
1425
                if self.no_debug_features_active(quantizers):
1426
                    debug = False
1427
                    quantizers = self._get_quantizers(fp8_output, fp8_grad)
1428

1429
1430
1431
1432
1433
            (
                input_quantizer,
                weight_quantizer,
                output_quantizer,
                grad_input_quantizer,
1434
1435
1436
                grad_weight_quantizer,
                grad_output_quantizer,
            ) = quantizers
1437

1438
1439
1440
1441
1442
1443
1444
1445
1446
            if torch.is_grad_enabled():
                linear_fn = _Linear.apply
                args = []
            else:
                linear_fn = _Linear.forward
                args = [None]
            args += (
                weight_tensor,
                inp,
1447
                bias_tensor if (self.apply_bias and not self.gemm_bias_unfused_add) else None,
1448
1449
1450
                is_first_microbatch,
                self.fp8,
                self.fp8_calibration,
1451
                self.wgrad_store,
1452
1453
1454
1455
                input_quantizer,
                weight_quantizer,
                output_quantizer,
                grad_input_quantizer,
1456
1457
                grad_weight_quantizer,
                grad_output_quantizer,
1458
                self.fuse_wgrad_accumulation,
1459
                is_cpu_offload_enabled(),
1460
1461
1462
1463
1464
1465
1466
                self.tp_group,
                self.tp_size,
                self.sequence_parallel,
                self.tp_size > 1,
                self.activation_dtype,
                self.parallel_mode,
                torch.is_grad_enabled(),
1467
1468
1469
1470
1471
1472
                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,
1473
                self.ub_name,
1474
                fp8_output,
1475
                self.fsdp_group,
1476
1477
                self,
                skip_fp8_weight_update,
1478
                self.symmetric_ar_type,
1479
                self.save_original_input,
1480
                debug,
1481
1482
1483
1484
1485
1486
1487
1488
            )
            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
1489
1490
1491

    def _get_quantizers(self, fp8_output, fp8_grad):
        if not self.fp8:
1492
            return [None] * 6
1493
        grad_input_quantizer = None
1494
        grad_weight_quantizer = None
1495
1496
1497
        grad_output_quantizer = None
        output_quantizer = None
        input_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_INPUT]
1498
        input_quantizer.internal = True
1499
        (weight_quantizer,) = self._get_weight_quantizers()
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
        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,
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
            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)
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
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
    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

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
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
    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
1662

1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
    def _customize_quantizers_nvfp4(self, fwd: bool, recipe: Recipe) -> None:
        """Customize quantizers based on current scaling recipe + linear."""
        assert recipe.nvfp4(), "Incorrect recipe."
        if fwd:
            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:
            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

1685
1686
    def _get_weight_quantizers(self) -> List[Quantizer]:
        """Get the weight quantizers of the module."""
1687
        if not self.fp8 and not self.fp8_calibration:
1688
1689
1690
1691
            return [None]
        weight_quantizer = self.quantizers["scaling_fwd"][tex.FP8FwdTensors.GEMM1_WEIGHT]
        weight_quantizer.internal = True
        return [weight_quantizer]
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710

    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