base.py 72.4 KB
Newer Older
1
# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
2
3
4
5
#
# See LICENSE for license information.

"""Base modules and utilities for TransformerEngine PyTorch API"""
6
import io
7
import math
8
9
10
import os
import pickle
import warnings
11
from enum import Enum
12
from abc import ABC, abstractmethod
13
from typing import Any, Dict, Generator, List, Optional, Set, Tuple, Union
14
from contextlib import contextmanager
15
import logging
16
from types import MethodType
17
18
19

import torch
import torch.nn.functional as F
20
from torch.distributed.tensor import DTensor
21

22
import transformer_engine_torch as tex
23

24
from ._common import _ParameterInitMeta, noop_cat
25
from ..quantization import (
26
27
    MXFP8BlockScalingRecipeState,
    DelayedScalingRecipeState,
28
    Float8CurrentScalingRecipeState,
29
    Float8BlockScalingRecipeState,
30
    NVFP4BlockScalingRecipeState,
31
    FP8GlobalStateManager,
32
    RecipeState,
33
34
35
36
37
)
from ..distributed import (
    gather_along_first_dim,
    is_fp8_activation_recompute_enabled,
    in_fp8_activation_recompute_phase,
38
    _fsdp_gather_tensors,
39
40
)
from ..constants import dist_group_type
41
from ..cpp_extensions.gemm import _NUM_MAX_UB_STREAMS
42
from ..quantized_tensor import QuantizedTensor, QuantizedTensorStorage, Quantizer
43
44
from ..tensor.float8_tensor import Float8Quantizer, Float8CurrentScalingQuantizer
from ..tensor.mxfp8_tensor import MXFP8Quantizer
45
from ..tensor.float8_blockwise_tensor import Float8BlockQuantizer
46
47
from ..tensor.storage.float8_tensor_storage import Float8TensorStorage
from ..tensor.storage.mxfp8_tensor_storage import MXFP8TensorStorage
48
from ..tensor.storage.nvfp4_tensor_storage import NVFP4TensorStorage
49
50
51
52
53
from ..utils import (
    is_non_tn_fp8_gemm_supported,
    torch_get_autocast_gpu_dtype,
    get_nvtx_range_context,
)
54
from ..tensor.storage.float8_blockwise_tensor_storage import Float8BlockwiseQTensorStorage
55
from ...common.recipe import DelayedScaling, Recipe
56
57
from ...debug.pytorch.debug_state import TEDebugState
from ...debug.pytorch.debug_quantization import DebugQuantizer, DebugQuantizedTensor
58
from ...debug.pytorch.utils import next_iter_when_debug_should_be_run, any_feature_enabled
yuguo's avatar
yuguo committed
59
from torch.utils.cpp_extension import IS_HIP_EXTENSION
60

61
__all__ = ["initialize_ub", "destroy_ub", "UserBufferQuantizationMode"]
62

63
64
65
_2X_ACC_FPROP = False
_2X_ACC_DGRAD = True
_2X_ACC_WGRAD = True
66
_dummy_wgrads = {}
67
_multi_stream_cublas_batchgemm_workspace = []
68
_ub_communicators = None
69
_MIN_STREAM_PRIORITY, _MAX_STREAM_PRIORITY = None, None
70
layers_atomic_ring_exchange = []
71
72


73
74
75
76
77
78
79
80
class UserBufferQuantizationMode(Enum):
    """
    UserBufferQuantizationMode is an enum that represents the quantization mode of the UserBuffer.
    """

    NONE = "none"
    FP8 = "fp8"

yuguo's avatar
yuguo committed
81
82
83
84
85
86
def get_multi_stream_cublas_batchgemm_workspace() -> List[torch.Tensor]:
    """Returns workspace for multi-stream cublas."""
    global _multi_stream_cublas_batchgemm_workspace
    if not _multi_stream_cublas_batchgemm_workspace:
        for _ in range(tex._num_cublas_batchgemm_streams):
            _multi_stream_cublas_batchgemm_workspace.append(
yuguo's avatar
yuguo committed
87
                torch.empty(128, dtype=torch.uint8, device="cuda")
yuguo's avatar
yuguo committed
88
89
90
            )
    return _multi_stream_cublas_batchgemm_workspace

91

yuguo's avatar
yuguo committed
92
93
94
95
if bool(int(os.getenv("NVTE_DISABLE_FC2_DGRAD_OVERLAP", "0"))):
    remove_ag_gemm_dgrad = ["fc2_dgrad"]
else:
    remove_ag_gemm_dgrad = []
96

97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
def get_dummy_wgrad(shape: list, dtype: torch.dtype, zero=False) -> torch.Tensor:
    """Returns a dummy tensor of given shape."""
    assert len(shape) == 2
    global _dummy_wgrads
    if (shape[0], shape[1], dtype) not in _dummy_wgrads:
        _dummy_wgrads[(shape[0], shape[1], dtype)] = torch.empty(
            shape,
            dtype=dtype,
            device="cuda",
            requires_grad=False,
        )
    if zero:
        _dummy_wgrads[(shape[0], shape[1], dtype)].fill_(0)
    return _dummy_wgrads[(shape[0], shape[1], dtype)].detach()

yuguo's avatar
yuguo committed
112
ub_comm_cu_nums = int(os.getenv("NVTE_UB_COMM_CU_NUMS", "8"))
113
114
def initialize_ub(
    shape: list,
115
    tp_size: int,
116
    use_fp8: bool = False,
117
    quantization_modes: List[UserBufferQuantizationMode] = None,
118
    dtype: torch.dtype = torch.bfloat16,
119
    ub_cfgs: Optional[Union[dict, List[dict]]] = None,
120
    bootstrap_backend: Union[str, torch.distributed.Backend] = None,
121
) -> None:
122
123
    r"""
    Initialize the Userbuffers communicator for overlapping tensor-parallel communications with
Paweł Gadziński's avatar
Paweł Gadziński committed
124
    GEMM compute in ``te.Linear``, ``te.LayerNormLinear`` and ``te.LayerNormMLP`` modules.
125
126
127
128
129

    Parameters
    ----------
    shape : list
            shape of the communication buffer, typically set to be the same as the global shape of
Paweł Gadziński's avatar
Paweł Gadziński committed
130
131
            the input tensor to a ``te.TransformerLayer`` forward pass, with the sequence and batch
            dimensions collapsed together -- i.e.: ``(sequence_length * batch_size, hidden_size)``
132
133
134
    tp_size : int
              number of GPUs in the tensor-parallel process group
    use_fp8 : bool = False
135
              allocate the communication buffer for FP8 GEMM inputs/outputs.
Paweł Gadziński's avatar
Paweł Gadziński committed
136
              DEPRECATED: Please use ``quantization_modes`` instead.
137
138
    quantization_modes : List[UserBufferQuantizationMode] = None
              if a list of UserBufferQuantizationMode is provided, a UB communicator is created for each quantization setting in the list.
Paweł Gadziński's avatar
Paweł Gadziński committed
139
              falls back to the legacy ``use_fp8`` parameter if ``None`` is provided.
140
    dtype : torch.dtype = torch.bfloat16
Paweł Gadziński's avatar
Paweł Gadziński committed
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
            non-FP8 data type of the communication buffer when ``use_fp8 = False``
    ub_cfgs : dict = None
             Configuration dictionary with the structure::

                 {
                    <gemm_name> : {
                        "method": <"ring_exchange" or "pipeline">,
                        "is_reduce_scatter": bool,
                        "num_sm": int,
                        "cga_size": int,
                        "set_sm_margin": bool,
                        "num_splits": int,
                        "aggregate": bool,
                        "atomic_gemm": bool,
                        "use_ce": bool,
                        "fp8_buf": bool,
                    }
                 }

             for ``te.TransformerLayer`` GEMM layers in ``["qkv_fprop", "qkv_dgrad", "qkv_wgrad",
161
             "proj_fprop", "proj_dgrad", "proj_wgrad", "fc1_fprop", "fc1_dgrad", "fc2_dgrad",
Paweł Gadziński's avatar
Paweł Gadziński committed
162
163
             "fc2_fprop", "fc2_wgrad"]``.
             a list may be provided to specify different overlap configurations for different the quantization settings in ``quantization_modes``
164
    bootstrap_backend : str = None
Paweł Gadziński's avatar
Paweł Gadziński committed
165
                        ``torch.distributed`` communication backend for the all-gather, broadcast and
166
167
168
169
                        barrier collectives during Userbuffers initialization. Not all backends are
                        valid for every cluster configuration and distributed launch method even if
                        they are available in PyTorch. When left unset, the initialization prefers
                        to use the MPI backend, falling back first on Gloo and then NCCL if MPI is
Paweł Gadziński's avatar
Paweł Gadziński committed
170
                        not available. Setting ``NVTE_UB_WITH_MPI=1`` when building TE overrides this
171
                        option and always initializes Userbuffers with direct MPI calls in C++,
Paweł Gadziński's avatar
Paweł Gadziński committed
172
                        which also requires ``MPI_HOME=/path/to/mpi/root`` to be set at compile time.
173
    """
174
    if not tex.device_supports_multicast():
yuguo's avatar
yuguo committed
175
        assert bool(int(os.getenv("UB_SKIPMC", "1"))), (
176
177
178
179
            "CUDA device, driver and/or toolkit version does not support comm+GEMM overlap with "
            + "CUDA Multicast. Launch app with UB_SKIPMC=1 to try CUDA IPC instead."
        )

180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
    if not quantization_modes:
        warnings.warn(
            "Initializing Userbuffers with use_fp8 is deprecated. Please use quantization_modes"
            " instead.",
            DeprecationWarning,
        )
        quantization_modes = [
            UserBufferQuantizationMode.FP8 if use_fp8 else UserBufferQuantizationMode.NONE
        ]
    else:
        assert isinstance(quantization_modes, list), "quantization_modes must be a list"
        assert all(
            isinstance(mode, UserBufferQuantizationMode) for mode in quantization_modes
        ), "quantization_modes must be a list of UserBufferQuantizationMode"

    if isinstance(ub_cfgs, dict) or ub_cfgs is None:
        ub_cfgs = [ub_cfgs] * len(quantization_modes)
    else:
        assert len(ub_cfgs) == len(
            quantization_modes
        ), "Number of ub_cfgs settings must match number of quantization configurations"

202
203
204
    global _ub_communicators
    assert _ub_communicators is None, "UB communicators are already initialized."
    _ub_communicators = {}
205
206

    if tex.ubuf_built_with_mpi():
207
208
        # We're bootstrapping with direct calls to MPI in Userbuffers code so we need to force
        # an MPI_Init() here by creating a new MPI process group...
209
        assert torch.distributed.is_mpi_available()
210
211
        _ = torch.distributed.new_group(backend="mpi")
        helper = tex.CommOverlapHelper()
212
    else:
213
214
        # Bootstrapping with torch.distributed API, so check backend and construct
        # intra/inter-node process groups...
215
216
217
218
219
        assert (
            torch.distributed.is_initialized()
        ), "torch.distributed must be initialized before Userbuffers"
        if bootstrap_backend is None:
            bootstrap_backend = "nccl"
220
            if torch.distributed.is_mpi_available():
221
                bootstrap_backend = "mpi"
222
223
            elif torch.distributed.is_gloo_available():
                bootstrap_backend = "gloo"
224
        else:
225
226
227
228
229
230
231
232
233
            assert bootstrap_backend in [
                "gloo",
                "mpi",
                "nccl",
            ], "Invalid torch.distributed backend for bootstrapping Userbuffers!"
            assert torch.distributed.is_backend_available(bootstrap_backend), (
                f"PyTorch must be compiled with '{bootstrap_backend}' support in order to "
                f"bootstrap Userbuffers with '{bootstrap_backend}' collectives."
            )
234
235
236
237
238

        world_group = torch.distributed.new_group(backend=bootstrap_backend)
        world_rank = torch.distributed.get_rank(world_group)
        world_size = torch.distributed.get_world_size(world_group)

239
240
        num_domains = world_size // tp_size
        mydomain_idx = world_rank // tp_size
241
        if num_domains > 1:
242
243
244
245
            ranks_per_domain_list = [
                [i * tp_size + t for t in range(tp_size)] for i in range(num_domains)
            ]
            tp_domain_group, _ = torch.distributed.new_subgroups_by_enumeration(
246
247
                ranks_per_domain_list, backend=bootstrap_backend
            )
248
249
            local_rank = torch.distributed.get_rank(tp_domain_group)
            tp_domain_ranks = torch.distributed.get_process_group_ranks(tp_domain_group)
250

251
            helper = tex.CommOverlapHelper(world_group, tp_domain_group)
252
        else:
253
254
            # TP model on single NVLink domain, no replication, no data-parallelism
            mydomain_idx = 0
255
            local_rank = world_rank
256
            tp_domain_ranks = list(range(world_size))
257
258

            helper = tex.CommOverlapHelper(world_group)
259

260
        if world_rank == 0:
261
            print(f"!!! [UB] Number of TP domains: {num_domains}\n", end="", flush=True)
262
263
        if local_rank == 0:
            print(
264
                f"!!! [UB] Global ranks on TP domain {mydomain_idx}: {tp_domain_ranks}\n",
265
266
267
268
                end="",
                flush=True,
            )

269
    # Default buffer precision: AllGather buffers use fp8 when using fp8 recipe
270
    layers_all_gather_overlap = [
271
272
273
        "qkv_fprop",
        "qkv_dgrad",
        "proj_dgrad",
274
        "proj_wgrad",
275
276
277
        "fc1_fprop",
        "fc1_dgrad",
        "fc2_dgrad",
278
        "fc2_wgrad",
279
    ]
280
    layers_reduce_scatter_overlap = ["proj_fprop", "fc2_fprop", "qkv_wgrad", "fc1_wgrad"]
Jaemin Choi's avatar
Jaemin Choi committed
281
    dgrad_reduce_scatter_overlap = ["qkv_dgrad", "fc1_dgrad"]
282
    # Default overlap methods for layers
yuguo's avatar
yuguo committed
283
    if bool(int(os.getenv("NVTE_PROJ_NO_PIPELINE_OVERLAP", "0"))) and bool(int(os.getenv("NVTE_FC2_NO_PIPELINE_OVERLAP", "0"))):
yuguo's avatar
yuguo committed
284
285
286
287
        methods = {
            "ring_exchange": ["qkv_fprop", "fc1_fprop", "proj_dgrad", "fc2_dgrad", "proj_fprop", "fc2_fprop"],
            "pipeline": [],
            "bulk": ["qkv_dgrad", "qkv_wgrad", "fc1_dgrad", "fc1_wgrad"],
288
            "external": ["proj_wgrad", "fc2_wgrad"],
yuguo's avatar
yuguo committed
289
        }
yuguo's avatar
yuguo committed
290
291
292
293
294
    elif bool(int(os.getenv("NVTE_PROJ_NO_PIPELINE_OVERLAP", "0"))):
        methods = {
            "ring_exchange": ["qkv_fprop", "fc1_fprop", "proj_dgrad", "fc2_dgrad", "proj_fprop"],
            "pipeline": ["fc2_fprop"],
            "bulk": ["qkv_dgrad", "qkv_wgrad", "fc1_dgrad", "fc1_wgrad"],
295
            "external": ["proj_wgrad", "fc2_wgrad"],
yuguo's avatar
yuguo committed
296
297
298
299
300
301
        }
    elif bool(int(os.getenv("NVTE_FC2_NO_PIPELINE_OVERLAP", "0"))):
        methods = {
            "ring_exchange": ["qkv_fprop", "fc1_fprop", "proj_dgrad", "fc2_dgrad", "fc2_fprop"],
            "pipeline": ["proj_fprop"],
            "bulk": ["qkv_dgrad", "qkv_wgrad", "fc1_dgrad", "fc1_wgrad"],
302
            "external": ["proj_wgrad", "fc2_wgrad"],
yuguo's avatar
yuguo committed
303
        }
yuguo's avatar
yuguo committed
304
305
306
307
308
    else:
        methods = {
            "ring_exchange": ["qkv_fprop", "fc1_fprop", "proj_dgrad", "fc2_dgrad"],
            "pipeline": ["proj_fprop", "fc2_fprop"],
            "bulk": ["qkv_dgrad", "qkv_wgrad", "fc1_dgrad", "fc1_wgrad"],
309
            "external": ["proj_wgrad", "fc2_wgrad"],
yuguo's avatar
yuguo committed
310
        }
311

312
    # AG-RS overlap pairs of layers forming a tensor-parallel block
313
314
    ag_rs_pairs = {"qkv_fprop": "proj_fprop", "fc1_fprop": "fc2_fprop"}
    rs_ag_pairs = {v: k for k, v in ag_rs_pairs.items()}
315
    external_gemm_to_overlap = {"proj_wgrad": "proj_dgrad", "fc2_wgrad": "fc2_dgrad"}
316
317
318
    global layers_atomic_ring_exchange
    layers_atomic_ring_exchange = []

319
320
321
322
323
324
    def get_method(name):
        for method, names in methods.items():
            if name in names:
                return method
        raise KeyError(f"Given layer name {name} does not exist.")

325
    def get_default_config(name):
326
        global _MIN_STREAM_PRIORITY, _MAX_STREAM_PRIORITY
327
328
        method = get_method(name)
        is_reduce_scatter = name in layers_reduce_scatter_overlap
329
330
        if _MIN_STREAM_PRIORITY is None or _MAX_STREAM_PRIORITY is None:
            _MIN_STREAM_PRIORITY, _MAX_STREAM_PRIORITY = tex.get_stream_priority_range()
331
332
333
        default_cfg = {
            "method": method,
            "is_reduce_scatter": is_reduce_scatter,
yuguo's avatar
yuguo committed
334
            "num_sm": 1 if method == "ring_exchange" else ub_comm_cu_nums,
335
            "cga_size": 1 if method == "ring_exchange" else 2,
336
337
            "set_sm_margin": not method == "ring_exchange",
            "num_splits": tp_size if method == "ring_exchange" else 4,
yuguo's avatar
yuguo committed
338
            "aggregate": bool(int(os.getenv("NVTE_TP_OVERLAP_AGGREGATE", "0"))),
339
340
341
            "atomic_gemm": False,
            "use_ce": True,
            "fp8_buf": name in layers_all_gather_overlap,
342
343
344
            "comm_priority": _MAX_STREAM_PRIORITY,
            "gemm_priority": _MIN_STREAM_PRIORITY,
            "pipeline_rs_overlap_first_gemm": False,
345
346
347
        }
        return default_cfg

348
349
    def add_ub(
        name: str,
350
        quantization_mode: UserBufferQuantizationMode,
351
        method: str,
352
        is_reduce_scatter: bool,
353
354
        num_sm: int = 16,
        cga_size: int = 2,
355
        set_sm_margin: bool = False,
356
        num_splits: int = 0,
357
358
        aggregate: bool = False,
        atomic_gemm: bool = False,
359
        use_ce: bool = True,
360
        fp8_buf: bool = False,
361
362
363
        comm_priority: int = 0,
        gemm_priority: int = 0,
        pipeline_rs_overlap_first_gemm: bool = False,
364
    ) -> None:
365
366
367
368
        if atomic_gemm:
            warnings.warn(
                "Atomic GEMM uses a beta API from cublas and is not tested for all use cases."
            )
369
370
371
            assert (
                quantization_mode == UserBufferQuantizationMode.FP8
            ), "Atomic GEMM overlap supported only for FP8 GEMM."
372
            if method in ("bulk", "external"):
373
                warnings.warn(
374
                    f"At {name}, atoimic GEMM not is supported for a bulk overlap."
375
376
377
                    "Defaulting to `atomic_gemm=False`."
                )
                atomic_gemm = 0
378
        if not is_reduce_scatter and method == "pipeline":
379
            raise ValueError(
380
                f"At {name}, `pipeline` overlap method is not supported for AllGather."
381
            )
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
        # Check if both AG and RS overlaps use `atomic GEMM`` + `p2p ring-exchange`.
        # Using atomic GEMM + p2p ring-exchange in only one of the pair breaks functionality.
        global layers_atomic_ring_exchange
        if atomic_gemm and method == "ring_exchange" and name in ag_rs_pairs:
            layers_atomic_ring_exchange += [name, ag_rs_pairs[name]]
        if name in rs_ag_pairs:
            assert_message = (
                f"At {name}, atomic AG-GEMM overlap with `ring_exchange` shuffles GEMM chunk "
                "outputs, and  RS-GEMM overlap un-suffle them. When one of the GEMM-AG and "
                "GEMM-RS overlaps forming a TP block (e.g., qkv_fprop and proj_fprop) uses "
                "`atomic gemm` and `ring_exhcnage`, its pair must use the same overlap config "
                "for functionality."
            )
            if name in layers_atomic_ring_exchange:
                assert atomic_gemm and method == "ring_exchange", assert_message
            else:
                if atomic_gemm and method == "ring_exchange":
                    assert rs_ag_pairs[name] in layers_atomic_ring_exchange, assert_message

401
402
403
404
405
406
407
408
409
410
        if name in external_gemm_to_overlap:
            assert method == "external", (
                f"At {name}, `external` overlap method is specified, but the selected method is"
                f" {method}"
            )
            assert external_gemm_to_overlap[name] in methods["ring_exchange"], (
                f"At {name}, `external` overlap method is specified, but the external gemm"
                f" {external_gemm_to_overlap[name]} is not using `ring_exchange` overlap method"
            )

411
412
413
414
415
        buffer_dtype = (
            torch.uint8
            if (quantization_mode == UserBufferQuantizationMode.FP8 and fp8_buf)
            else dtype
        )
416
        if method == "ring_exchange":
417
418
419
420
            ub_obj = tex.CommOverlapP2P(
                shape,  # Communication buffer shape
                buffer_dtype,  # Communication buffer data type
                helper,  # Helper for torch.distributed callbacks during bootstrapping
421
                tp_size,  # Tensor-parallel group size (may be different than local_size)
422
423
424
425
426
427
428
429
                tex.CommOverlapType.RS if is_reduce_scatter else tex.CommOverlapType.AG,
                num_max_streams=_NUM_MAX_UB_STREAMS,
                comm_cga_size=cga_size,
                num_comm_sm=num_sm,
                set_sm_margin=set_sm_margin,
                atomic_gemm=atomic_gemm,
                use_ce=use_ce,
                aggregate=aggregate,
430
431
                gemm_priority=gemm_priority,
                comm_priority=comm_priority,
432
            )
433
        else:
434
435
436
437
            ub_obj = tex.CommOverlap(
                shape,  # Communication buffer shape
                buffer_dtype,  # Communication buffer data type
                helper,  # Helper for torch.distributed callbacks during bootstrapping
438
                tp_size,  # Tensor-parallel group size (may be different than local_size)
439
                num_splits=num_splits,
yuguo's avatar
yuguo committed
440
                num_max_streams=_NUM_MAX_UB_STREAMS,
441
442
443
444
                comm_cga_size=cga_size,
                num_comm_sm=num_sm,
                set_sm_margin=set_sm_margin,
                atomic_gemm=atomic_gemm,
445
446
447
                gemm_priority=gemm_priority,
                comm_priority=comm_priority,
                rs_overlap_first_gemm=pipeline_rs_overlap_first_gemm,
448
            )
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
        _ub_communicators[(name, quantization_mode)] = ub_obj

    for quantization_mode, user_ub_cfg in zip(quantization_modes, ub_cfgs):
        if user_ub_cfg is not None:
            for name in dgrad_reduce_scatter_overlap:
                if (
                    name in user_ub_cfg
                    and "method" in user_ub_cfg[name]
                    and user_ub_cfg[name]["method"] != "bulk"
                ):
                    wgrad_name = name.replace("dgrad", "wgrad")
                    assert wgrad_name not in user_ub_cfg
                    layers_reduce_scatter_overlap.remove(wgrad_name)
                    layers_all_gather_overlap.remove(name)
                    layers_reduce_scatter_overlap.append(name)
                    methods["bulk"].remove(name)
                    new_method = user_ub_cfg[name]["method"]
                    methods[new_method].append(name)

        for name in (
            methods["ring_exchange"] + methods["pipeline"] + methods["bulk"] + methods["external"]
        ):
wenjh's avatar
wenjh committed
471
472
            if name in remove_ag_gemm_dgrad:
                continue
473
474
475
476
477
            ub_cfg = get_default_config(name)
            if user_ub_cfg is not None and name in user_ub_cfg:
                fp8_buf = (name in layers_all_gather_overlap) or (
                    user_ub_cfg[name].get("fp8_buf", False) and name in methods["pipeline"]
                )
478
                ub_cfg.update(user_ub_cfg[name])
479
480
                ub_cfg["fp8_buf"] = fp8_buf
            add_ub(name, quantization_mode, **ub_cfg)
481
482


483
def get_ub(name: str, use_fp8: bool):
484
    """Get userbuffer communicator corresponding to give key."""
485
486
487
488
    # For now use `use_fp8` boolean input as it matches the current design in the modules
    # So favour simplicity until the correct design becomes clear.
    # This is mainly an internal API so we don't need to worry about future changes
    key = (name, UserBufferQuantizationMode.FP8 if use_fp8 else UserBufferQuantizationMode.NONE)
489
    assert _ub_communicators is not None, "UB manager is not initialized."
490
    assert key in _ub_communicators, f"UB for {name} with use_fp8={use_fp8} is not registered."
yuguo's avatar
yuguo committed
491
492
493
    # assert name in _ub_communicators, f"UB for {name} is not registered."
    if name in remove_ag_gemm_dgrad:
        return None
494
    return _ub_communicators[key]
495

496

497
498
499
500
501
502
503
def destroy_ub():
    """Destroy all allocated userbuffer communicators."""
    global _ub_communicators
    _ub_communicators = None
    global layers_atomic_ring_exchange
    layers_atomic_ring_exchange = []

504

505
506
507
508
509
def fill_userbuffers_buffer_for_all_gather(
    comm,
    local_tensor: torch.Tensor,
    quantizer: Optional[Quantizer],
    process_group,
510
) -> tuple[torch.Tensor | QuantizedTensorStorage, torch.Tensor | QuantizedTensorStorage]:
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
    """Fill local shard of Userbuffers buffer with data for all-gather

    Returns the full tensor and the local shard, both using the
    Userbuffers buffer as their underlying data. These tensors should
    be used carefully (e.g. only immediately before and after a
    Userbuffers operation) since the underlying data may be
    overwritten by other Userbuffers operations.

    May perform blocking communication if needed for the gathered
    tensor's metadata, e.g. scaling factors.

    """

    # Tensor dimensions
    local_shape = local_tensor.size()
    if not local_shape:
        raise ValueError(f"Invalid local tensor (shape={tuple(local_shape)})")
    process_group_size = torch.distributed.get_world_size(process_group)
    global_shape = list(local_shape)
    global_shape[0] *= process_group_size

    # Unquantized data
    if quantizer is None:
534
        if isinstance(local_tensor, QuantizedTensorStorage):
535
536
537
538
539
540
541
542
543
544
545
546
            local_tensor = local_tensor.dequantize()
        if comm.is_fp8_ubuf():
            raise RuntimeError(
                "Attempting to all-gather unquantized tensor, "
                "but Userbuffers is initialized with FP8 buffers"
            )
        comm.copy_into_buffer(local_tensor, local_chunk=True)
        global_tensor = comm.get_buffer(shape=global_shape)
        return global_tensor, local_tensor

    # FP8 data
    if isinstance(quantizer, (Float8Quantizer, Float8CurrentScalingQuantizer)):
547
548
        if not isinstance(local_tensor, Float8TensorStorage):
            if isinstance(local_tensor, QuantizedTensorStorage):
549
550
551
552
553
554
555
556
557
558
                local_tensor.dequantize()
            quantizer.set_usage(rowwise=True, columnwise=False)
            local_tensor = quantizer(local_tensor)
        if not comm.is_fp8_ubuf():
            raise RuntimeError(
                "Attempting to all-gather FP8 tensor, "
                "but Userbuffers is not initialized with FP8 buffers"
            )
        comm.copy_into_buffer(local_tensor._data, local_chunk=True)
        global_tensor_data = comm.get_buffer(shape=global_shape)
559
        global_tensor = Float8TensorStorage(
560
561
562
563
564
565
566
567
568
569
570
            data=global_tensor_data,
            fp8_scale_inv=local_tensor._scale_inv,
            fp8_dtype=local_tensor._fp8_dtype,
            quantizer=quantizer,
        )
        return global_tensor, local_tensor

    # MXFP8 data
    if isinstance(quantizer, MXFP8Quantizer):

        # Cast to MXFP8 if needed
571
572
        if not isinstance(local_tensor, MXFP8TensorStorage):
            if isinstance(local_tensor, QuantizedTensorStorage):
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
                local_tensor.dequantize()
            local_tensor = quantizer(local_tensor)
        if not comm.is_fp8_ubuf():
            raise RuntimeError(
                "Attempting to all-gather MXFP8 tensor, "
                "but Userbuffers is not initialized with FP8 buffers"
            )

        # Check which MXFP8 buffer to communicate
        if quantizer.rowwise_usage == quantizer.columnwise_usage:
            raise ValueError(
                "Userbuffers can only communicate one MXFP8 buffer at a time, "
                f"but quantizer has rowwise_usage={quantizer.rowwise_usage}, "
                f"columnwise_usage={quantizer.columnwise_usage}"
            )
        with_rowwise_data = quantizer.rowwise_usage

        # Copy MXFP8 data to local chunk of Userbuffers buffer
        local_data = (
            local_tensor._rowwise_data if with_rowwise_data else local_tensor._columnwise_data
        )
        comm.copy_into_buffer(local_data, local_chunk=True)

        # Gather scaling-inverses
        if math.prod(local_shape[:-1]) % 128 != 0:
            raise ValueError(
                "Userbuffers requires MXFP8 tensor dims that are divisible by 128, "
                f"but got MXFP8 tensor with shape={tuple(local_shape)}"
            )
602
603
        if local_tensor._with_gemm_swizzled_scales:
            raise ValueError("Userbuffers assumes MXFP8 tensors have unswizzled scales")
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
        local_scale_inv = (
            local_tensor._rowwise_scale_inv
            if with_rowwise_data
            else local_tensor._columnwise_scale_inv
        )
        local_scale_inv_size = list(local_scale_inv.size())
        global_scale_inv = torch.empty(
            [process_group_size * local_scale_inv_size[0]] + local_scale_inv_size[1:],
            dtype=local_scale_inv.dtype,
            device=local_scale_inv.device,
        )
        torch.distributed.all_gather_into_tensor(
            global_scale_inv,
            local_scale_inv,
            group=process_group,
        )

        # Construct MXFP8 tensor with Userbuffers buffer
        rowwise_data, rowwise_scale_inv = None, None
        columnwise_data, columnwise_scale_inv = None, None
        global_data = comm.get_buffer(shape=global_shape)
        if with_rowwise_data:
            rowwise_data, rowwise_scale_inv = global_data, global_scale_inv
        else:
            columnwise_data, columnwise_scale_inv = global_data, global_scale_inv
629
        global_tensor = MXFP8TensorStorage(
630
631
632
633
634
635
            rowwise_data=rowwise_data,
            rowwise_scale_inv=rowwise_scale_inv,
            columnwise_data=columnwise_data,
            columnwise_scale_inv=columnwise_scale_inv,
            fp8_dtype=local_tensor._fp8_dtype,
            quantizer=quantizer,
636
            with_gemm_swizzled_scales=False,
637
638
639
640
641
642
643
        )
        return global_tensor, local_tensor

    # Unsupported data format
    raise ValueError(f"Unsupported quantizer for Userbuffers ({quantizer})")


644
645
646
647
648
649
class TransformerEngineBaseModule(torch.nn.Module, ABC):
    """Base TE module."""

    def __init__(self) -> None:
        super().__init__()
        assert torch.cuda.is_available(), "TransformerEngine needs CUDA."
650
        self.name = None
651
        self.next_iter_when_debug_should_be_run = 0
652
653
654
655
        self.fp8_initialized = False
        self.fp8 = False
        self.fp8_calibration = False
        self.fp8_meta = {}
656
        self.fp8_meta["fp8_checkpoint"] = False
657
658
        self.fp8_meta["fp8_group"] = None
        self.fp8_meta_tensors_initialized = False
659
        self.quantizers = {"scaling_fwd": {}, "scaling_bwd": {}}
660
661
662
        self.tp_group = None
        self.tp_size = 1
        self.sequence_parallel = False
663
664
        self.param_init_meta = {}
        self.primary_weights_in_fp8 = FP8GlobalStateManager.with_fp8_parameters()
665
        self.preserve_high_precision_init_val = FP8GlobalStateManager.with_high_precision_init_val()
666
667
        self.fsdp_wrapped = False
        self.fsdp_group = None
668
        self._fp8_workspaces: Dict[str, QuantizedTensor] = {}
669
        self.activation_dtype: Optional[torch.dtype] = None
670
        self.wgrad_accumulation_and_reduce_hooks = []
671
        self.wgrad_store = None
672

673
674
675
        if not TEDebugState.debug_enabled:
            TEDebugState.initialize()

676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
    # Names of attributes that can be set quickly (see __setattr__
    # method)
    _fast_setattr_names: Set[str] = {
        "activation_dtype",
        "fp8",
        "fp8_initialized",
        "fp8_calibration",
        "fp8_parameters",
    }

    def __setattr__(self, name: str, value: Any) -> None:
        if name in TransformerEngineBaseModule._fast_setattr_names:
            # torch.nn.Module has a custom __setattr__ that handles
            # modules, parameters, and buffers. This is unnecessary
            # overhead when setting plain attrs.
            self.__dict__[name] = value
        else:
            # Default case
            super().__setattr__(name, value)
695

696
    def adjust_amax_history_length(self, length: int, fwd: Optional[bool] = None) -> None:
697
698
699
700
        """
        Delayed scaling only.

        Increase or decrease size of amax history based on given `length`.
701
702
703
704
705
706
707
708
709
710

        .. warning::
            This changes the underlying amax memory location.
        """
        if fwd is None:
            fp8_meta_tensor_keys = ("scaling_fwd", "scaling_bwd")
        else:
            fp8_meta_tensor_keys = ("scaling_fwd" if fwd else "scaling_bwd",)

        for meta_key in fp8_meta_tensor_keys:
711
712
713
            if meta_key not in self.fp8_meta:
                # Handles non-parameter FP8 modules, e.g. DPA.
                continue
714
715
716
717
718
            curr_len = self.fp8_meta[meta_key].amax_history.shape[0]
            if length == curr_len:
                continue
            if length < curr_len:
                self.fp8_meta[meta_key].amax_history = (
719
720
                    self.fp8_meta[meta_key].amax_history[:length].clone()
                )
721
722
723
724
725
726
            elif length > curr_len:
                extra_rows = length - curr_len
                self.fp8_meta[meta_key].amax_history = F.pad(
                    self.fp8_meta[meta_key].amax_history, pad=(0, 0, 0, extra_rows)
                )

727
728
            # Update quantizers with new amax pointers.
            self.quantizers[meta_key] = self.fp8_meta[meta_key].make_quantizers()
729
730
            # Make sure weight tensors has correct quantizers
            self._update_weight_quantizers()
731

732
733
            # Update the global buffers with new amax and history pointers.
            if FP8GlobalStateManager.get_buffer_info() in self.fp8_meta:
734
735
736
                fwd_pos, fwd_key, bwd_pos, bwd_key = self.fp8_meta[
                    FP8GlobalStateManager.get_buffer_info()
                ]
737
738
739
740
741
                for pos, buffer_key in zip((fwd_pos, bwd_pos), (fwd_key, bwd_key)):
                    if buffer_key in FP8GlobalStateManager.global_amax_buffer:
                        assert (
                            buffer_key in FP8GlobalStateManager.global_amax_history_buffer
                        ), "TE internal error during amax history change."
742
743
744
                        FP8GlobalStateManager.global_amax_buffer[buffer_key][pos] = self.fp8_meta[
                            meta_key
                        ].amax_history[0]
745
                        FP8GlobalStateManager.global_amax_history_buffer[buffer_key][pos] = (
746
747
                            self.fp8_meta[meta_key].amax_history
                        )
748

749
    def set_meta_tensor(self, fwd: bool, recipe: Recipe) -> None:
750
751
752
        """Init scales and amaxes for fwd | bwd."""
        fp8_meta_tensor_key = "scaling_fwd" if fwd else "scaling_bwd"

753
        # Return early if recipe state matches recipe
754
        if self.fp8_meta_tensors_initialized:
755
756
757
758
759
760
            recipe_state = self.fp8_meta[fp8_meta_tensor_key]
            if recipe.delayed() and isinstance(recipe_state, DelayedScalingRecipeState):
                self.adjust_amax_history_length(recipe.amax_history_len, fwd=fwd)
                return
            if recipe.mxfp8() and isinstance(recipe_state, MXFP8BlockScalingRecipeState):
                return
761
762
763
764
            if recipe.float8_current_scaling() and isinstance(
                recipe_state, Float8CurrentScalingRecipeState
            ):
                return
765
766
767
768
            if recipe.float8_block_scaling() and isinstance(
                recipe_state, Float8BlockScalingRecipeState
            ):
                return
769
770
            if recipe.nvfp4() and isinstance(recipe_state, NVFP4BlockScalingRecipeState):
                return
771
772
773

        # Max. number of fp8 tensors per GEMM = 3 (input, weight, output) for fwd and
        # 2 (grad_output and grad_input) for bwd
774
        num_fp8_tensors = self.fp8_meta["num_gemms"] * 3 if fwd else self.fp8_meta["num_gemms"] * 2
775

776
777
778
779
780
        # Initialize recipe state and quantizers
        recipe_state = RecipeState.create(
            recipe,
            mode=("forward" if fwd else "backward"),
            num_quantizers=num_fp8_tensors,
781
782
        )

783
784
785
        self.fp8_meta[fp8_meta_tensor_key] = recipe_state
        self.quantizers[fp8_meta_tensor_key] = recipe_state.make_quantizers()

786
787
788
789
790
791
792
793
794
    def _update_weight_quantizers(self) -> None:
        """Update the quantizers for the weight tensors."""
        weight_tensors = self._get_weight_tensors()
        weight_quantizers = self._get_weight_quantizers()
        assert len(weight_tensors) == len(weight_quantizers), (
            f"Number of weight tensors ({len(weight_tensors)}) and quantizers "
            f"({len(weight_quantizers)}) must match"
        )
        for weight, quantizer in zip(weight_tensors, weight_quantizers):
795
            if quantizer is not None and isinstance(weight, QuantizedTensorStorage):
796
797
                weight.update_quantizer(quantizer)

798
    def _get_weight_tensors(self) -> List[Union[torch.Tensor, QuantizedTensorStorage]]:
799
800
801
802
803
804
805
806
807
808
809
        """Get the weight tensors of the module."""
        raise NotImplementedError(
            f"{self.__class__.__name__} class does not implement _get_weight_tensors function"
        )

    def _get_weight_quantizers(self) -> List[Quantizer]:
        """Get the weight quantizers of the module."""
        raise NotImplementedError(
            f"{self.__class__.__name__} class does not implement _get_weight_quantizers function"
        )

810
    def init_fp8_meta_tensors(self, recipe: Recipe) -> None:
811
        """Init scales and amaxes."""
812
813
814
        self.set_meta_tensor(True, recipe)
        self.set_meta_tensor(False, recipe)

815
816
        self.fp8_meta_tensors_initialized = True

817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
    def get_fp8_meta_tensors(self) -> None:
        """Get scales and amaxes."""
        fwd_key, bwd_key = "scaling_fwd", "scaling_bwd"
        if fwd_key not in self.fp8_meta or bwd_key not in self.fp8_meta:
            return None

        fp8_meta_tensors = {fwd_key: [], bwd_key: []}
        with torch.no_grad():
            for key in (fwd_key, bwd_key):
                fp8_meta_tensors[key].append(self.fp8_meta[key].scale.clone())
                fp8_meta_tensors[key].append(self.fp8_meta[key].amax_history.clone())
        return fp8_meta_tensors

    def reset_fp8_meta_tensors(self, fp8_meta_tensors=None) -> None:
        """Reset scales and amaxes."""
832

833
834
835
836
837
        def reset(key):
            if key in self.fp8_meta:
                if fp8_meta_tensors is None:
                    self.fp8_meta[key].scale.copy_(torch.ones_like(self.fp8_meta[key].scale))
                    self.fp8_meta[key].amax_history.copy_(
838
839
                        torch.zeros_like(self.fp8_meta[key].amax_history)
                    )
840
841
842
                else:
                    assert key in fp8_meta_tensors, "Cannot reset fp8 tensors."
                    self.fp8_meta[key].scale.copy_(fp8_meta_tensors[key][0])
843
                    self.fp8_meta[key].amax_history.copy_(fp8_meta_tensors[key][1])
844

845
846
847
848
        with torch.no_grad():
            reset("scaling_fwd")
            reset("scaling_bwd")

849
    def get_extra_state(self) -> torch.Tensor:
850
        """Save before checkpointing."""
851

852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
        # This implementation is working around a few issues:
        #
        # (1) PyTorch's "extra state" infrastructure might be able to
        #     support any picklable type, but they make no guarantees.
        #     We have experienced problems (e.g. in ONNX export) with
        #     non-tensor extra state.
        # (2) PyTorch's checkpointing infrastructure does not remap
        #     devices for "extra state" like it does for "state dict".
        #     Thus, we want to avoid putting extra state on the GPU
        #     since it may be loaded on the wrong device.
        # (3) The extra state consists of many small tensors. If we
        #     want to copy them all to CPU, then we need to avoid the
        #     overhead of many GPU-CPU memory transfers.
        #
        # See: https://github.com/NVIDIA/TransformerEngine/pull/351
        # See: https://github.com/NVIDIA/TransformerEngine/pull/363

        def to_cpu(src: torch.Tensor) -> torch.Tensor:
            """Helper function to make CPU copy of tensor

            Memory transfer is asynchronous w.r.t. host, so GPU should
            be synchronized before using result.

            """
            dst = torch.empty_like(src, device="cpu")
            dst.copy_(src, non_blocking=True)
            return dst

        # Store FP8 state if needed
        state = None
882
        fp8_checkpoint = self.fp8_meta["fp8_checkpoint"] or self.fp8 or self.fp8_calibration
883
        if not fp8_checkpoint:
884
            return torch.empty(0, dtype=torch.uint8)
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902

        # Copy tensors to CPU and store
        state = {}
        state["recipe"] = self.fp8_meta["recipe"]
        if state["recipe"].delayed():
            state["scale_fwd"] = to_cpu(self.fp8_meta["scaling_fwd"].scale)
            state["amax_history_fwd"] = to_cpu(self.fp8_meta["scaling_fwd"].amax_history)
            state["scale_bwd"] = to_cpu(self.fp8_meta["scaling_bwd"].scale)
            state["amax_history_bwd"] = to_cpu(self.fp8_meta["scaling_bwd"].amax_history)

        # Store other pickelable values
        extra = {}
        for k, v in self.fp8_meta.items():
            if k != "buffer_index_and_autocast_key" and isinstance(
                v, (bool, int, float, str, tuple, list)
            ):
                extra[k] = v
        state["extra_fp8_variables"] = extra
903

904
905
906
907
        # Serialize state into byte tensor
        torch.cuda.synchronize()
        state_serialized = bytearray(pickle.dumps(state))
        state_serialized = torch.frombuffer(state_serialized, dtype=torch.uint8)
908
        return state_serialized
909

910
    def set_extra_state(self, state: torch.Tensor) -> None:
911
        """Load previous state."""
912
913

        # Maintain backwards compatibility with older checkpoints.
914
915
916
        if state is None:
            return

917
        # Load state
918
        if isinstance(state, torch.Tensor):
919
920
921
            # No FP8 is indicated by an empty tensor we don't need to unpickle.
            if state.numel() == 0:
                return
922
            # Default format: byte tensor with pickled data
923
            state = pickle.loads(state.detach().cpu().numpy().tobytes())
924
        elif isinstance(state, io.BytesIO):
925
            # Deprecated format with io.BytesIO
926
            state.seek(0)
927
            state = torch.load(state, map_location="cuda")
928
929
        else:
            raise RuntimeError("Unsupported checkpoint format.")
930
931
932

        if state is None:
            return
933

934
935
936
937
938
939
940
941
        # TE 1.x checkpoint compatibility: add DelayedScaling recipe if missing
        if "recipe" not in state:
            # TE 1.x only supported delayed scaling, which was the default recipe
            state["recipe"] = DelayedScaling()
            # TE 1.x also saved scale_inv, which is not needed with Recipe object
            state.pop("scale_inv_fwd", None)
            state.pop("scale_inv_bwd", None)

942
        # Load extra items
943
        self.fp8_meta.update(state["extra_fp8_variables"])
944
        self.fp8_meta["recipe"] = state["recipe"]
945
946
947
        if "global_fp8_buffer_pos_fwd_recompute" in self.fp8_meta:
            del self.fp8_meta["global_fp8_buffer_pos_fwd_recompute"]

948
        # Initialize before loading
949
        self.init_fp8_meta_tensors(self.fp8_meta["recipe"])
950
951
952
953
954
955
956
957
958
959
960

        def copy_tensor(src: torch.Tensor, dst: torch.Tensor) -> None:
            """Helper function to copy tensor from CPU

            Memory transfer is asynchronous w.r.t. host, so GPU should
            be synchronized before using result.

            """
            dst.copy_(src, non_blocking=True)

        # Load tensors
961
962
963
964
965
        if self.fp8_meta["recipe"].delayed():
            copy_tensor(state["scale_fwd"], self.fp8_meta["scaling_fwd"].scale)
            copy_tensor(state["amax_history_fwd"], self.fp8_meta["scaling_fwd"].amax_history)
            copy_tensor(state["scale_bwd"], self.fp8_meta["scaling_bwd"].scale)
            copy_tensor(state["amax_history_bwd"], self.fp8_meta["scaling_bwd"].amax_history)
966
        torch.cuda.synchronize()
967
968
969
970
971

    def set_activation_dtype(self, inp: torch.Tensor) -> None:
        """Get activation data type for AMP."""
        # Native AMP (`torch.autocast`) gets highest priority
        if torch.is_autocast_enabled():
972
            self.activation_dtype = torch_get_autocast_gpu_dtype()
973
974
975
            return

        # All checks after this have already been performed once, thus skip
976
        if self.activation_dtype == inp.dtype:
977
978
            return

979
        dtype = inp.dtype
980
981
982
983
984
985
986
        if not self.allow_different_data_and_param_types:
            for name, param in self.named_parameters():
                if param is not None:
                    assert dtype == param.dtype, (
                        "Data types for parameters must match when outside of autocasted region. "
                        f" Found input dtype: {dtype} and {name!r} dtype: {param.dtype}"
                    )
987
        self.activation_dtype = dtype
988
989

    def set_tensor_parallel_group(self, tp_group: Union[dist_group_type, None]) -> None:
990
991
992
993
994
995
        """
        Set the tensor parallel group for the given
        module before executing the forward pass.

        Parameters
        ----------
Paweł Gadziński's avatar
Paweł Gadziński committed
996
        tp_group : ProcessGroup, default = None
997
998
                  tensor parallel process group.
        """
999
1000
1001
        self.tp_group = tp_group
        self.tp_group_initialized = True

1002
1003
1004
    def _get_fp8_params(self) -> Union[List[torch.Tensor], None]:
        """returns the FP8 weights."""
        fp8_params = []
1005
        for param in self.parameters(recurse=False):
1006
            if isinstance(param, QuantizedTensor) and param.requires_grad:
1007
1008
1009
1010
1011
                fp8_params.append(param)
        if len(fp8_params) == 0:
            return None
        return fp8_params

1012
1013
    # This routine is shared across FP8 and FP8_calibration paths so should not actually
    # assume FP8 execution.
1014
    def init_fp8_metadata(self, num_gemms: int = 1) -> None:
1015
        """Initialize fp8 related metadata and tensors during fprop."""
1016
1017
        _original_recipe = self.fp8_meta.get("recipe", None)

1018
        self.fp8_parameters = FP8GlobalStateManager.with_fp8_parameters()
1019
1020
        self.fp8 = FP8GlobalStateManager.is_fp8_enabled()
        self.fp8_calibration = FP8GlobalStateManager.is_fp8_calibration()
1021
        fp8_enabled = self.fp8 or self.fp8_calibration
1022
        self.fp8_meta["fp8_checkpoint"] = self.fp8 or self.fp8_calibration
1023

1024
        if self.fp8_parameters or fp8_enabled:
1025
1026
1027
1028
            if (
                self.fp8_initialized
                and FP8GlobalStateManager.get_fp8_recipe() == self.fp8_meta["recipe"]
            ):
1029
                # FP8 init has already been run and recipe is the same, don't do anything.
1030
                return
1031
            self.fp8_meta["recipe"] = FP8GlobalStateManager.get_fp8_recipe()
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
        else:
            # If fp8 isn't enabled, turn off and return.
            self.fp8_initialized = False
            return

        if self.fp8_parameters and not self.fp8_initialized:
            self.fp8_meta["num_gemms"] = num_gemms
            self.init_fp8_meta_tensors(self.fp8_meta["recipe"])

        if fp8_enabled:
            # Set FP8 and other FP8 metadata
1043
            self.fp8_meta["num_gemms"] = num_gemms
1044
            self.fp8_meta["fp8_group"] = FP8GlobalStateManager.get_fp8_group()
1045
1046

            # Set FP8_MAX per tensor according to recipe
1047
1048
1049
            if hasattr(self.fp8_meta["recipe"], "fp8_format"):
                self.fp8_meta["fp8_max_fwd"] = self.fp8_meta["recipe"].fp8_format.value.max_fwd
                self.fp8_meta["fp8_max_bwd"] = self.fp8_meta["recipe"].fp8_format.value.max_bwd
1050
1051

            # Allocate scales and amaxes
1052
            self.init_fp8_meta_tensors(self.fp8_meta["recipe"])
1053
            self.fp8_initialized = True
1054
1055

            self.fp8_meta["recipe"] = FP8GlobalStateManager.get_fp8_recipe()
1056

1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
        _current_recipe = self.fp8_meta["recipe"]
        if _original_recipe is not None and not (
            issubclass(_current_recipe.__class__, _original_recipe.__class__)
            or issubclass(_original_recipe.__class__, _current_recipe.__class__)
        ):
            warnings.warn(
                f"Recipe type changed from {_original_recipe.__class__.__name__} "
                f"to {_current_recipe.__class__.__name__}. "
                "This may affect model behavior."
            )
            # Clear cached workspaces as they were created with the old recipe/quantizer type
            self._fp8_workspaces.clear()

1070
1071
1072
1073
1074
    @contextmanager
    def prepare_forward(
        self,
        inp: torch.Tensor,
        num_gemms: int = 1,
1075
        allow_non_contiguous: bool = False,
1076
        allow_different_data_and_param_types: bool = False,
Jan Bielak's avatar
Jan Bielak committed
1077
    ) -> Generator[torch.Tensor, None, None]:
1078
1079
1080
1081
1082
1083
        """Checks and prep for FWD.
        The context manager is needed because there isn't a way for a module to know
        if it's the last FP8 module in the forward autocast. It is useful
        to setup the forward aggregated amax reduction for every module
        just in case. The autocast exit will pick up the most recent one.
        """
1084
        self.allow_different_data_and_param_types = allow_different_data_and_param_types
1085
        self.forwarded_at_least_once = True
1086

1087
1088
        # Activation recomputation is used and this is the second forward phase.
        if self.fp8 and in_fp8_activation_recompute_phase():
1089
            delayed_scaling_recipe = self.fp8_meta["recipe"].delayed()
1090
            FP8GlobalStateManager.get_old_fp8_meta_tensors_for_recompute(self.fp8_meta)
1091
1092
1093
1094
1095
1096
1097
        else:
            assert inp.is_cuda, "TransformerEngine needs CUDA."

            if self.tp_size > 1:
                assert self.tp_group_initialized, "TP group not initialized."

            self.set_activation_dtype(inp)
1098
            self.init_fp8_metadata(num_gemms=num_gemms)
1099
            self._check_weight_tensor_recipe_correspondence()
1100

1101
1102
1103
1104
1105
1106
1107
            delayed_scaling_recipe = self.fp8 and self.fp8_meta["recipe"].delayed()
            if delayed_scaling_recipe:
                if self.sequence_parallel:
                    assert self.fp8_meta["recipe"].reduce_amax, (
                        "Amax reduction across tensor parallel group is "
                        "necessary when using sequence parallelism with FP8."
                    )
1108

1109
1110
                if not FP8GlobalStateManager.fp8_graph_capturing():
                    FP8GlobalStateManager.add_fp8_tensors_to_global_buffer(self.fp8_meta)
1111

1112
1113
1114
                # Activation recomputation is used and this is the first forward phase.
                if self.training and is_fp8_activation_recompute_enabled():
                    FP8GlobalStateManager.copy_forward_fp8_meta_tensors_for_recompute(self.fp8_meta)
1115

1116
        with get_nvtx_range_context(self.__class__.__name__ + " forward"):
1117
1118
1119
            if not allow_non_contiguous and not inp.is_contiguous():
                inp = inp.contiguous()
            yield inp
1120

1121
        if delayed_scaling_recipe and self.fp8 and in_fp8_activation_recompute_phase():
1122
            FP8GlobalStateManager.restore_fp8_meta_tensors(self.fp8_meta)
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142

    def set_nccl_overlap_warning_if_tp(self) -> None:
        """When using TP, the NCCL communication needs to be scheduled
        before the GEMM for there to be a guaranteed overlap. From the
        host side in TE, the comm calls are always launched first, but
        to ensure that the GEMM isn't scheduled first, the environment
        variable `CUDA_DEVICE_MAX_CONNECTIONS` needs to be set to 1 to
        force a single channel.
        """
        if self.tp_size == 1:
            return
        num_cuda_work_queues = int(os.getenv("CUDA_DEVICE_MAX_CONNECTIONS", "0"))
        if num_cuda_work_queues != 1:
            warnings.warn(
                "To guarantee overlapping TP and SP collectives with the backward"
                "GEMMs, set environment variable CUDA_DEVICE_MAX_CONNECTIONS = 1"
            )

    @staticmethod
    def grad_output_preprocess(
1143
1144
1145
1146
        ctx,
        grad_output: torch.Tensor,
        row_parallel_mode: bool,
        quantizer: Optional[Quantizer],
1147
1148
1149
    ) -> Tuple[Union[torch.Tensor, None], ...]:
        """Utility function for backward.
        Returns tuple in order (all optional/None based on training precion/recipe):
1150
1151
            R1: gathered `grad_output`.
            R2: bias gradient on R1.
1152
1153

        """
1154
1155
        grad_output = grad_output.reshape((-1, grad_output.shape[-1]))
        grad_output = grad_output.contiguous()
1156
1157
        gather_grad_output = row_parallel_mode and ctx.sequence_parallel

1158
        # Non-FP8 case: bgrad is fused with wgrad for this case.
1159
        if not ctx.fp8 and not ctx.debug:
1160
            if gather_grad_output:
1161
                if not ctx.ub_overlap_ag or ctx.ub_obj_gradout is None:  # Perform NCCL all-gather
1162
                    grad_output, _ = gather_along_first_dim(grad_output, ctx.tp_group)
1163
1164
1165
1166
1167
1168
1169
                else:  # Initialize Userbuffers all-gather
                    grad_output, _ = fill_userbuffers_buffer_for_all_gather(
                        ctx.ub_obj_gradout,
                        grad_output,
                        None,
                        ctx.tp_group,
                    )
1170
1171
1172
            return grad_output, None

        # FP8 with all-gather: unfused bgrad, fused cast + transpose
1173
        # Also supports debug quantization, which is handled inside gather_along_first_dim.
1174
1175
        if gather_grad_output:
            grad_bias = None
1176
            if ctx.use_bias:
1177
                grad_bias = grad_output.view(-1, grad_output.shape[-1]).sum(dim=0)
yuguo's avatar
yuguo committed
1178
            if ctx.ub_overlap_ag and ctx.ub_obj_gradout is not None:
1179
1180
                # Quantize the gradient if needed
                if not isinstance(
1181
1182
1183
                    grad_output,
                    (
                        QuantizedTensor,
1184
1185
1186
                        Float8TensorStorage,
                        MXFP8TensorStorage,
                        Float8BlockwiseQTensorStorage,
1187
                    ),
1188
1189
1190
1191
                ):
                    grad_output = quantizer(grad_output)

                # Copy into communication buffer, and replace original gradient with it
1192
1193
1194
1195
1196
1197
                grad_output, _ = fill_userbuffers_buffer_for_all_gather(
                    ctx.ub_obj_gradout,
                    grad_output,
                    quantizer,
                    ctx.tp_group,
                )
1198
            else:
1199
1200
1201
1202
                grad_output, _ = gather_along_first_dim(
                    grad_output,
                    ctx.tp_group,
                    quantizer=quantizer,
1203
                )
1204
            return grad_output, grad_bias
1205

1206
1207
1208
1209
        # Debug without all-gather: unfused cast and bgrad
        # bgrad only if wgrad is in FP8, otherwise it is fused with wgrad and we return None
        if ctx.debug:
            grad_output_ = quantizer(grad_output)
1210
            if ctx.use_bias:
1211
1212
1213
1214
1215
1216
                grad_bias = grad_output.view(-1, grad_output.shape[-1]).sum(dim=0)
            else:
                grad_bias = None
            grad_output = grad_output_
            return grad_output, grad_bias

1217
1218
        # FP8 without all-gather: fused bgrad + cast + transpose
        grad_bias = None
1219
        if ctx.use_bias:
1220
1221
            if isinstance(
                grad_output,
1222
1223
1224
1225
1226
1227
                (
                    QuantizedTensor,
                    Float8TensorStorage,
                    MXFP8TensorStorage,
                    Float8BlockwiseQTensorStorage,
                ),
1228
            ):
1229
                grad_bias = grad_output.dequantize().view(-1, grad_output.shape[-1]).sum(dim=0)
1230
            else:
yuguo's avatar
yuguo committed
1231
                if isinstance(quantizer, Float8BlockQuantizer) or (isinstance(quantizer, Float8CurrentScalingQuantizer) and IS_HIP_EXTENSION):
1232
1233
1234
1235
                    # unfuse bgrad for now until cast_transpose + dgrad calculation is ready for Float8BlockQuantizer.
                    grad_bias = grad_output.view(-1, grad_output.shape[-1]).sum(dim=0)
                else:
                    grad_bias, grad_output = tex.bgrad_quantize(grad_output, quantizer)
1236
        if not isinstance(grad_output, QuantizedTensorStorage):
1237
1238
            grad_output = quantizer(grad_output)
        return grad_output, grad_bias
1239

1240
1241
1242
1243
1244
1245
    def register_parameter(self, name, param, **kwargs):
        """
        Thin wrapper around PyTorch parameter registration to stash additional parameter
        metedata used in deferred initialization.
        """
        super().register_parameter(name, param)
1246
1247
1248
1249
1250
1251
        # Initialize param_init_meta exactly once during the init. FSDP2 can call
        # register parameter again to change parameters to DTensors. And it calls
        # it without custom fp8 specific kwargs that we need. And so we dont want
        # to reset/loose our fp8 init attributes.
        if hasattr(self, "param_init_meta") and name not in self.param_init_meta:
            self.param_init_meta[name] = _ParameterInitMeta(**kwargs)
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262

    def reset_parameters(self, defer_init: Optional[bool] = False) -> None:
        """
        Reset all module parameters to initial values. Unless deferred initialization
        is specified, all parameters on a 'meta' device are also materialized on a real cuda
        device before the values are reset to initial.
        """
        if defer_init:
            return

        for name, param in self.named_parameters(recurse=False):
1263
1264
1265
1266
1267
            # Check if parameter is a DTensor (FSDP2) or regular tensor
            is_dtensor = isinstance(param, DTensor)
            dtensor_param = param if is_dtensor else None
            # Need to update/quantize local tensor in case of DTensor
            param = param._local_tensor if is_dtensor else param
1268
            # Ensure parameter is on a real device
1269
1270
            if param.device == torch.device("meta"):
                param = torch.empty_like(param, device="cuda")
1271
1272
1273
1274
1275
1276
            # Initialize the parameter values on device
            init_fn = self.param_init_meta[name].init_fn
            get_rng_state_tracker = self.param_init_meta[name].get_rng_state_tracker
            if get_rng_state_tracker is None:
                init_fn(param)
            else:
1277
1278
1279
1280
1281
1282
                if hasattr(self, "rng_tracker_name") and self.rng_tracker_name:
                    with get_rng_state_tracker().fork(self.rng_tracker_name):
                        init_fn(param)
                else:
                    with get_rng_state_tracker().fork():
                        init_fn(param)
1283

1284
            # Wrap parameters in QuantizedTensor if needed
1285
            fp8_meta_index = self.param_init_meta[name].fp8_meta_index
1286
            high_precision_init_val = None
1287
            if self.primary_weights_in_fp8 and fp8_meta_index is not None:
1288
1289

                # Keep high-precision values on CPU if needed
1290
1291
1292
                if self.preserve_high_precision_init_val:
                    high_precision_init_val = param.detach().cpu()

1293
                # Configure quantizer
1294
                quantizer = self.quantizers["scaling_fwd"][fp8_meta_index]
1295
1296
1297
                if quantizer is None:
                    raise RuntimeError("Weight quantizer has not been initialized")
                quantizer.set_usage(rowwise=True, columnwise=torch.is_grad_enabled())
1298
                quantizer.internal = False
1299
1300
1301
1302
1303
1304
1305
1306
1307
                if is_dtensor and isinstance(quantizer, Float8CurrentScalingQuantizer):
                    device_mesh = dtensor_param.device_mesh
                    amax_reduction_group = (
                        device_mesh.get_group(mesh_dim="shard")
                        if device_mesh.ndim > 1
                        else device_mesh.get_group()
                    )
                    quantizer.amax_reduction_group = amax_reduction_group
                    quantizer.with_amax_reduction = True
1308
                # Quantize parameter
1309
                param = quantizer(param)
1310
1311
1312
1313
1314

            # Redo parameter wrap in case we broke it above
            # NOTE: Currently this can only be broken when primary weights are in Fp8 but
            #       re-applying the nn.Parameter() wrap is a no-op when the input is already
            #       a parameter so we always re-apply it just for extra safety.
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
            if is_dtensor:
                # recreate the DTensor from the parameter.
                dtensor_param = DTensor.from_local(
                    param,
                    device_mesh=dtensor_param.device_mesh,
                    placements=dtensor_param.placements,
                    shape=dtensor_param.size(),
                    stride=dtensor_param.stride(),
                )
                dtensor_param = torch.nn.Parameter(dtensor_param)
            else:
                param = torch.nn.Parameter(param)
1327
1328

            # Keep high-precision values on CPU if needed
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
            if high_precision_init_val is not None:

                # - Master weights are initialized from model weights, if we use fp8 primary
                #   weights to initialize master weights, the numerical values of master weights
                #   are not consistent with the numerical values when we initialize them from
                #   bf16/fp16 weights.
                # - So we add a `_high_precision_init_val` attribute to each model weight to store
                #   the original bf16/fp16 weight on cpu before casting it to fp8. And users can
                #   use `get_high_precision_init_val` to get this cpu tensor.
                # - This cpu tensor is not needed once the master weight is initialized, so users
                #   should call `clear_high_precision_init_val` to remove it after master weight
                #   is initialized.

                def get(self):
                    if hasattr(self, "_high_precision_init_val"):
                        return self._high_precision_init_val
                    return None

                def clear(self):
                    if hasattr(self, "_high_precision_init_val"):
                        del self._high_precision_init_val

                param._high_precision_init_val = high_precision_init_val
                param.get_high_precision_init_val = MethodType(get, param)
                param.clear_high_precision_init_val = MethodType(clear, param)
1354
                # Update the parameter based on its type
1355

1356
1357
1358
1359
            if not is_dtensor:
                setattr(self, name, param)
            else:
                setattr(self, name, dtensor_param)
1360

1361
1362
1363
    @abstractmethod
    def forward(self):
        """Needs override."""
1364

1365
    def get_weight_workspace(
1366
        self,
1367
1368
        *,
        tensor: Optional[torch.Tensor] = None,
1369
        quantizer: Optional[Quantizer] = None,
1370
1371
1372
        cache_name: Optional[str] = None,
        update_workspace: bool = True,
        skip_update_flag: Optional[torch.Tensor] = None,
1373
        fsdp_group: Optional[dist_group_type] = None,
1374
        workspace_dtype: Optional[torch.dtype] = None,
1375
    ) -> QuantizedTensor:
1376
        """Get workspace buffer for weights and maybe update its values
1377
1378
1379
1380
1381
1382
1383
1384

        The workspace buffer may be cached for future function calls.

        Parameters
        ----------
        tensor : torch.Tensor, optional
            Values to copy into workspace. Required if the workspace
            is being constructed or updated.
1385
1386
1387
        quantizer: Quantizer, optional
            Quantizer used to cast the weights. Required if the
            workspace is being constructed or updated.
1388
1389
        cache_name: str, optional
            Key for caching.
Paweł Gadziński's avatar
Paweł Gadziński committed
1390
        update_workspace: bool, default = True
1391
1392
1393
1394
            Update workspace with values from `tensor`.
        skip_update_flag: torch.Tensor, optional
            GPU flag to skip updating the workspace. Take precedence
            over `update_workspace` if provided.
1395
1396
        fsdp_group: bool, default = None
            FSDP process group that the weights are distributed over.
1397
1398
1399
        workspace_dtype: torch.dtype, default = None
            If weight workspace contains high-precision tensor - for example
            for debug quantization, this is dtype of the tensor.
1400
1401
        """

1402
1403
1404
        # Handle case where weights are already quantized
        # Note: Make sure weights have required usages, but do not
        # destroy unnecessary usages since they may be used later.
1405
        if isinstance(tensor, QuantizedTensor):
1406
1407
1408
1409
1410
1411
            update_rowwise_usage = True if quantizer.rowwise_usage else None
            update_columnwise_usage = True if quantizer.columnwise_usage else None
            tensor.update_usage(
                rowwise_usage=update_rowwise_usage,
                columnwise_usage=update_columnwise_usage,
            )
1412
1413
            return tensor

1414
        # Try getting workspace from cache
1415
1416
1417
        out = None
        if cache_name is not None:
            out = self._fp8_workspaces.get(cache_name, None)
1418
1419
1420
1421

        # Reset cache if workspace is invalid
        if out is not None and quantizer is not None:
            reset_cache = False
1422
            if isinstance(out, Float8TensorStorage):
1423
1424
1425
1426
1427
1428
                if (
                    not is_non_tn_fp8_gemm_supported()
                    and quantizer.columnwise_usage
                    and out._transpose is None
                ):
                    reset_cache = True
1429
            elif isinstance(out, MXFP8TensorStorage):
1430
                if quantizer.rowwise_usage and out._rowwise_data is None:
1431
                    reset_cache = True
1432
                elif quantizer.columnwise_usage and out._columnwise_data is None:
1433
                    reset_cache = True
1434
1435
1436
1437
1438
            elif isinstance(out, NVFP4TensorStorage):
                if quantizer.rowwise_usage and out._rowwise_data is None:
                    reset_cache = True
                elif quantizer.columnwise_usage and out._columnwise_data is None:
                    reset_cache = True
1439
1440
1441
            if isinstance(out, DebugQuantizedTensor) != isinstance(quantizer, DebugQuantizer):
                reset_cache = True
            if reset_cache:
1442
                out = None
1443
                del self._fp8_workspaces[cache_name]
1444

1445
1446
1447
1448
1449
        # Gather cached Fp8 workspace if it's distributed
        # NOTE: FSDP sharding is supported only for Fp8 buffers and will not work
        #       for models initialized with Fp8 primary weights.
        if (
            out is not None
1450
            and tensor is not None
1451
            and fsdp_group is not None
1452
            and out.data.shape != tensor.data.shape
1453
1454
1455
1456
        ):
            _fsdp_gather_tensors(fsdp_group, [tensor.data.shape], out)

        # Construct workspace if needed
1457
        if out is None:
1458
            if tensor is None or quantizer is None:
1459
                raise ValueError(
1460
                    "tensor and quantizer kwargs must be provided to construct FP8 workspace"
1461
                )
1462
1463
1464
1465
1466
1467
1468

            if cache_name is not None:
                # Ensure the tensor in the cache is an instance of torch.Tensor,
                # as it persists beyond a single forward pass.
                # Setting internal=True would cause the data to be removed in prepare_for_saving(...).
                quantizer_internal = quantizer.internal
                quantizer.internal = False
1469
            out = quantizer.quantize(tensor, dtype=workspace_dtype)
1470
1471
            if cache_name is not None:
                quantizer.internal = quantizer_internal
1472
1473

            # Update cache
1474
1475
            if cache_name is not None:
                self._fp8_workspaces[cache_name] = out
1476
            return out
1477
1478
1479
1480
1481
1482

        # Update workspace if needed
        if skip_update_flag is not None:
            update_workspace = True
        if update_workspace:
            if tensor is None:
1483
                raise ValueError("tensor kwarg must be provided to update FP8 workspace")
1484
            if hasattr(out, "quantize_"):
1485
                out.quantize_(tensor, noop_flag=skip_update_flag)
1486
1487
            else:
                tex.quantize(tensor, quantizer, out, skip_update_flag)
1488
        return out
1489

1490
1491
1492
    def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
        """
        This function loads tensors and extra state including fp8 metadata.
        This metadata is essential for copying fp8 tensors, as the copy_ function
        uses the scale_inv parameter from fp8_meta to set the correct scaling factor
        for the new tensor.
        Hence, this extra state must be loaded before the tensor copying process,
        not after, as is typically done in _load_from_state_dict.
        Tensors are copied into fp8 tensors only when self.primary_weights_in_fp8=True,
        otherwise, this behavior is not required.
        """
        if self.primary_weights_in_fp8:
            extra_state_key = prefix + torch.nn.modules.module._EXTRA_STATE_KEY_SUFFIX
            if extra_state_key in state_dict:
                self.set_extra_state(state_dict[extra_state_key])
1507
1508
1509
        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )
1510

1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
    def register_wgrad_accumulation_and_reduce_hooks(self, wgrad_accumulation_and_reduce_hook):
        """
        This method is used to manually control the weight gradient accumulation and reduce.
        This method should be called before the backward() method.
        Set the skip_wgrad_accumulation_and_reduce to True to skip the weight gradient accumulation
        and reduce in backward();
        And register the wgrad_accumulation_and_reduce_func to be called in backward_dw() method.
        """
        self.wgrad_accumulation_and_reduce_hooks.append(wgrad_accumulation_and_reduce_hook)

1521
1522
1523
1524
1525
1526
1527
1528
1529
    def need_backward_dw(self):
        """
        Check if this module needs to execute the delayed weight gradient computation.
        This method should be used at the beginning of self.backward_dw() to determine if it
        should actually be executed or just return without doing anything.
        User can also manually call this method to check that before calling into backward_dw().
        """
        return self.wgrad_store is not None and self.wgrad_store.delay_wgrad_compute()

1530
1531
1532
1533
1534
    def backward_dw(self):
        """
        Execute the delayed weight gradient computation.
        This method is called after the main backward pass to compute weight gradients.
        """
1535
        if not self.need_backward_dw():
1536
            return
1537
        with get_nvtx_range_context(f"_{self.__class__.__name__}_wgrad"):
1538
            (wgrad, bgrad), _ = self.wgrad_store.pop()
1539
            if not self.fuse_wgrad_accumulation:
1540
                weight_tensor = noop_cat(self._get_weight_tensors())
1541
                weight_tensor.grad = wgrad.to(weight_tensor.dtype)
1542
1543
1544
            if self.use_bias:
                bias_tensor = noop_cat([getattr(self, name) for name in self.bias_names])
                if bias_tensor.grad is None:
1545
                    bias_tensor.grad = bgrad.to(bias_tensor.dtype)
1546
1547
1548
1549
            del wgrad
            del bgrad
            for wgrad_accumulation_and_reduce_hook in self.wgrad_accumulation_and_reduce_hooks:
                wgrad_accumulation_and_reduce_hook()
1550

1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
    def is_debug_iter(self) -> bool:
        """
        This function checks if the debug should be enabled for this layer.
        """
        debug = TEDebugState.debug_enabled
        if not debug:
            return False
        self._validate_name()

        # If layer is run first time in new iteration,
        # we need to check if the debug should be enabled for this layer -
        # maybe in previous iterations debug features returned information
        # that no feature will be active for this layer for multiple next iterations.
        started_new_iteration = TEDebugState.get_iteration() != getattr(
            self, "debug_last_iteration", None
        )
        if started_new_iteration:
            if self.next_iter_when_debug_should_be_run is None:
                debug = False
            else:
                debug = TEDebugState.get_iteration() >= self.next_iter_when_debug_should_be_run
1572
1573
1574
1575
1576
1577
1578
            self.debug_last_iteration = TEDebugState.get_iteration()
            self.debug_enabled_in_this_iteration = debug
        else:
            # If this is the same iteration as previous invocation of the module,
            # we use the debug value from the first invocation in the iteration.
            debug = self.debug_enabled_in_this_iteration

1579
1580
1581
1582
1583
1584
        self.debug_last_iteration = TEDebugState.get_iteration()

        if self.wgrad_store is not None:
            if debug and self.wgrad_store.delay_wgrad_compute():
                raise RuntimeError("Delayed wgrad compute is not supported in debug mode.")

1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
        return debug

    def no_debug_features_active(self, quantizers):
        """
        Checks if any debug feature is active for this layer.
        """
        run_current = any_feature_enabled(quantizers)

        # Sometimes features inform that they will not be enabled for particular layer
        # for multiple next iterations.
        self.next_iter_when_debug_should_be_run = next_iter_when_debug_should_be_run(quantizers)

        if not run_current:
            return True

        if self.primary_weights_in_fp8:
            raise RuntimeError("FP8 weights are not supported in debug mode.")
        return False
1603

1604
1605
1606
1607
1608
1609
    def _validate_name(self):
        """
        Validate name passed to the module.
        This is invoked in the forward() method as module names are assigned after Model is initialized in Megatron-LM.
        If no name is assigned, it creates a default name with layer count as the variable.
        """
1610
1611
        if self.name is not None:
            return
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
        assert TEDebugState.debug_enabled
        import nvdlfw_inspect.api as debug_api

        if self.name is None:
            debug_api.log_message(
                "Names are not provided to debug modules. ",
                "Creating and using generic names. Pass names to debug modules for better"
                " insight. ",
                level=logging.WARNING,
            )
            self.name = f"Layer_{TEDebugState.get_layer_count()}"

1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
    def _check_weight_tensor_recipe_correspondence(self) -> None:
        """
        Verify that the weight tensor types match their corresponding recipe type.
        This is invoked in the forward().

        This establishes a 1:1 correspondence between recipe types and tensor types:
        - DelayedScaling → Float8Tensor
        - Float8CurrentScaling → Float8Tensor
        - MXFP8BlockScaling → MXFP8Tensor
        - Float8BlockScaling → Float8BlockTensor

1635
1636
        Example case to check: recipe is DelayedScaling (DelayedScaling is set in autocast()),
        but the weight tensor is MXFP8Tensor (MXFP8BlockScaling is set in quantized_model_init()).
1637
1638
1639
        """
        if not self.fp8 and not self.fp8_calibration:
            return
1640
1641
        if not self.primary_weights_in_fp8:
            return
1642
1643
1644
1645
1646
1647
        if not hasattr(self, "weight_names") or not self.weight_names:
            return

        recipe = self.fp8_meta["recipe"]
        weight_tensors = [getattr(self, name) for name in self.weight_names]
        for i, tensor in enumerate(weight_tensors):
1648
            if isinstance(tensor, QuantizedTensorStorage):
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
                quantizer = tensor._get_quantizer()
                if quantizer is None:
                    continue
                compatible_recipe_class = quantizer._get_compatible_recipe()
                if compatible_recipe_class is None:
                    continue
                if not isinstance(recipe, compatible_recipe_class):
                    raise RuntimeError(
                        f"Recipe mismatch for '{self.weight_names[i]}': tensor supports recipe"
                        f" {compatible_recipe_class.__name__}, but got {recipe.__class__.__name__}."
1659
1660
                        " Please check the recipes assigned during quantized_model_init() and"
                        " autocast() calls."
1661
                    )