test_sanity.py 40.3 KB
Newer Older
1
# Copyright (c) 2022-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
Przemek Tredak's avatar
Przemek Tredak committed
2
3
4
#
# See LICENSE for license information.

5
6
from typing import Optional

Przemek Tredak's avatar
Przemek Tredak committed
7
8
import torch
import pytest
9
import os
yuguo's avatar
yuguo committed
10
from torch.utils.cpp_extension import IS_HIP_EXTENSION
Przemek Tredak's avatar
Przemek Tredak committed
11

12
13
14
import transformer_engine
import transformer_engine.pytorch as te
from transformer_engine.pytorch.quantization import FP8GlobalStateManager
Przemek Tredak's avatar
Przemek Tredak committed
15
16
17
18
19
from transformer_engine.pytorch.utils import (
    init_method_normal,
    scaled_init_method_normal,
)
from transformer_engine.pytorch import (
20
21
    autocast,
    quantized_model_init,
Przemek Tredak's avatar
Przemek Tredak committed
22
23
    LayerNormLinear,
    Linear,
24
    GroupedLinear,
Przemek Tredak's avatar
Przemek Tredak committed
25
26
    LayerNormMLP,
    TransformerLayer,
27
28
    RMSNorm,
    LayerNorm,
29
30
31
32
33
34
35
    Float8CurrentScalingQuantizer,
    Float8Quantizer,
    Float8Tensor,
    MXFP8Tensor,
    checkpoint,
    QuantizedTensor,
    is_bf16_available,
Przemek Tredak's avatar
Przemek Tredak committed
36
37
)
from transformer_engine.common import recipe
38
import transformer_engine_torch as tex
39
from transformer_engine.pytorch.cpp_extensions import general_gemm
40
from transformer_engine.pytorch.module.base import get_workspace
41
from transformer_engine.pytorch.tensor.utils import replace_raw_data
42
from utils import ModelConfig
Przemek Tredak's avatar
Przemek Tredak committed
43

44
# Only run FP8 tests on supported devices.
45
46
47
fp8_available, reason_for_no_fp8 = te.is_fp8_available(return_reason=True)
fp8_block_scaling_available, _ = te.is_fp8_block_scaling_available(return_reason=True)
mxfp8_available, reason_for_no_mxfp8 = te.is_mxfp8_available(return_reason=True)
48

49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
# Record initial RNG state from script run.
seed = 1234
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)

NVTE_TEST_NVINSPECT_ENABLED = int(os.environ.get("NVTE_TEST_NVINSPECT_ENABLED", "0"))


if NVTE_TEST_NVINSPECT_ENABLED:
    # The sanity tests should work the same,
    # when debug=True. I fed them with dummy feature
    # to prevent switching off debug, which can happen if
    # no feature is active.
    import nvdlfw_inspect.api as debug_api

    debug_api.initialize(
        os.environ["NVTE_TEST_NVINSPECT_CONFIG_FILE"],
        feature_dirs=os.environ["NVTE_TEST_NVINSPECT_FEATURE_DIRS"],
    )

69

70
71
72
73
74
75
76
77
78
def is_fp8_supported(config: ModelConfig):
    if (
        config.max_seqlen_q * config.batch_size % 16
        or config.max_seqlen_kv * config.batch_size % 16
    ):
        return False
    if config.hidden_size % 16 or config.hidden_size_kv % 16:
        return False
    return True
Przemek Tredak's avatar
Przemek Tredak committed
79

80

Przemek Tredak's avatar
Przemek Tredak committed
81
model_configs = {
82
83
84
85
    "126m": ModelConfig(2, 2048, 12, 64, num_layers=12),
    "small": ModelConfig(2, 32, 2, 32, num_layers=2),
    "weird": ModelConfig(3, 37, 3, 23, num_layers=2),
    "large": ModelConfig(2, 128, 4, 128, num_layers=1),
Przemek Tredak's avatar
Przemek Tredak committed
86
87
}

88
89
90
91
92
93
94
95
96

def nvfp4_vanilla():
    nvfp4_recipe = recipe.NVFP4BlockScaling()
    nvfp4_recipe.fp4_quant_fwd_inp = recipe.QParams()
    nvfp4_recipe.fp4_quant_fwd_weight = recipe.QParams()
    nvfp4_recipe.fp4_quant_bwd_grad = recipe.QParams()
    return nvfp4_recipe


97
98
99
fp8_recipes = []
if mxfp8_available:
    fp8_recipes.append(recipe.MXFP8BlockScaling())
100
    fp8_recipes.append(nvfp4_vanilla())  # TODO: fix check for this
101
102
103
104
105
106
if fp8_block_scaling_available:
    fp8_recipes.append(recipe.Float8BlockScaling())
if fp8_available:
    fp8_recipes.append(recipe.Float8CurrentScaling())
    fp8_recipes.append(recipe.DelayedScaling())
fp8_recipes.append(None)
Przemek Tredak's avatar
Przemek Tredak committed
107

108
param_types = [torch.float32, torch.float16]
109
if is_bf16_available():  # bf16 requires sm_80 or higher
110
    param_types.append(torch.bfloat16)
Przemek Tredak's avatar
Przemek Tredak committed
111

112
all_boolean = [True, False]
113
batch_sizes_with_zero = [0, 1, 2]
Przemek Tredak's avatar
Przemek Tredak committed
114

115
116
117
118
119
120
121
122
123
124
125
126
all_activations = [
    "gelu",
    "geglu",
    "qgelu",
    "qgeglu",
    "relu",
    "reglu",
    "srelu",
    "sreglu",
    "silu",
    "swiglu",
]
127
all_normalizations = ["LayerNorm", "RMSNorm"]
schetlur-nv's avatar
schetlur-nv committed
128

129

schetlur-nv's avatar
schetlur-nv committed
130
131
132
133
134
def _disable_wgrads(block):
    for p in block.parameters():
        p.requires_grad = False


135
136
137
138
139
140
@pytest.fixture(autouse=True)
def reset_global_fp8_state():
    yield
    FP8GlobalStateManager.reset()


141
def _test_sanity_e2e_amp(block, dtype, config, fp8_recipe, skip_wgrad):
Przemek Tredak's avatar
Przemek Tredak committed
142
    te_inp_hidden_states = torch.randn(
143
        (config.max_seqlen_q, config.batch_size, config.hidden_size),
144
145
146
147
        dtype=torch.float32,
        device="cuda",
        requires_grad=True,
    )
148
    te_inp_hidden_states.retain_grad()
149
150
    te_inp_attn_mask = torch.randint(
        2,
151
        (1, 1, config.max_seqlen_q, config.max_seqlen_kv),
152
153
154
        dtype=torch.bool,
        device="cuda",
    )
schetlur-nv's avatar
schetlur-nv committed
155
156
157
158

    if skip_wgrad:
        _disable_wgrads(block)

159
160
    use_fp8 = fp8_recipe is not None
    with torch.autocast(device_type="cuda", enabled=True, dtype=dtype):
161
        with autocast(enabled=use_fp8, recipe=fp8_recipe):
162
            te_out = block(te_inp_hidden_states, attention_mask=te_inp_attn_mask)
Przemek Tredak's avatar
Przemek Tredak committed
163
164
165
166
167
        loss = te_out.sum()

    loss.backward()
    torch.cuda.synchronize()

168
    assert te_out.dtype == dtype, "AMP wrong output type."
169
    assert te_inp_hidden_states.grad is not None, "Gradient should not be empty"
170
171
172
173
174
175
    assert te_inp_hidden_states.grad.dtype == torch.float32, "AMP wrong dgrad type."
    for name, p in block.named_parameters():
        if p.requires_grad:
            assert p.grad.dtype == torch.float32, f"AMP wrong wgrad type for {name}."


176
def _test_sanity_e2e_gradient_accumulation_fusion(block, dtype, config, fp8_recipe, skip_wgrad):
177
    te_inp_hidden_states = torch.randn(
178
        (config.max_seqlen_q, config.batch_size, config.hidden_size),
179
180
181
182
183
184
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    te_inp_attn_mask = torch.randint(
        2,
185
        (1, 1, config.max_seqlen_q, config.max_seqlen_kv),
186
187
188
        dtype=torch.bool,
        device="cuda",
    )
189
190
191
192
193
194
195
196
197
198
199

    if skip_wgrad:
        _disable_wgrads(block)

    for name, p in block.named_parameters():
        if "layer_norm_weight" in name:
            continue
        elif "weight" in name and p.requires_grad:
            p.main_grad = torch.zeros_like(p)

    use_fp8 = fp8_recipe is not None
200
    with autocast(enabled=use_fp8, recipe=fp8_recipe):
201
        te_out = block(te_inp_hidden_states, attention_mask=te_inp_attn_mask)
202
203
204
205
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()

206
    failed_grads = []
207
208
209
210
    for name, p in block.named_parameters():
        if "layer_norm_weight" in name:
            continue
        elif "weight" in name and p.requires_grad:
211
212
213
            if not torch.count_nonzero(p.main_grad) > 0:
                failed_grads.append(name)
    assert len(failed_grads) == 0, f"Gradient not accumulated for {failed_grads}."
214

Przemek Tredak's avatar
Przemek Tredak committed
215

216
def _test_sanity_e2e(block, dtype, config, fp8_recipe, skip_wgrad):
Przemek Tredak's avatar
Przemek Tredak committed
217
    te_inp_hidden_states = torch.randn(
218
        (config.max_seqlen_q, config.batch_size, config.hidden_size),
219
220
221
222
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
223
224
225
226
227

    if skip_wgrad:
        _disable_wgrads(block)

    use_fp8 = fp8_recipe is not None
228
    with autocast(enabled=use_fp8, recipe=fp8_recipe):
229
230
231
232
233
234
        te_out = block(te_inp_hidden_states)
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()


235
def _test_sanity_e2e_bert(block, dtype, config, fp8_recipe, skip_wgrad):
236
    te_inp_hidden_states = torch.randn(
237
        (config.max_seqlen_q, config.batch_size, config.hidden_size),
238
239
240
241
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
242

243
244
    te_inp_attn_mask = torch.randint(
        2,
245
        (config.batch_size, 1, 1, config.max_seqlen_q),
246
247
248
        dtype=torch.bool,
        device="cuda",
    )
schetlur-nv's avatar
schetlur-nv committed
249
250
251
252

    if skip_wgrad:
        _disable_wgrads(block)

253
    use_fp8 = fp8_recipe is not None
254
    with autocast(enabled=use_fp8, recipe=fp8_recipe):
255
        te_out = block(te_inp_hidden_states, attention_mask=te_inp_attn_mask)
Przemek Tredak's avatar
Przemek Tredak committed
256
257
258
259
260
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()


261
def _test_sanity_e2e_T5(block, dtype, config, fp8_recipe, skip_wgrad):
Przemek Tredak's avatar
Przemek Tredak committed
262
    te_inp_hidden_states = torch.randn(
263
        (config.max_seqlen_q, config.batch_size, config.hidden_size),
264
265
266
267
268
269
        dtype=dtype,
        device="cuda",
        requires_grad=True,
    )
    te_inp_attn_mask = torch.randint(
        2,
270
        (1, 1, config.max_seqlen_q, config.max_seqlen_kv),
271
272
273
274
275
276
        dtype=torch.bool,
        device="cuda",
    )

    enc_dec_attn_mask = torch.randint(
        2,
277
        (config.batch_size, 1, 1, config.max_seqlen_kv),
278
279
280
        dtype=torch.bool,
        device="cuda",
    )
schetlur-nv's avatar
schetlur-nv committed
281
282
283
284

    if skip_wgrad:
        _disable_wgrads(block)

285
    use_fp8 = fp8_recipe is not None
286
    with autocast(enabled=use_fp8, recipe=fp8_recipe):
Przemek Tredak's avatar
Przemek Tredak committed
287
        te_out = block(
288
289
            te_inp_hidden_states,
            attention_mask=te_inp_attn_mask,
290
291
            encoder_output=te_inp_hidden_states,
            enc_dec_attn_mask=enc_dec_attn_mask,
Przemek Tredak's avatar
Przemek Tredak committed
292
293
294
295
296
297
        )
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()


298
299
300
def _test_sanity_common(
    block, dtype, config, fp8_recipe, skip_wgrad, skip_dgrad, microbatching=True
):
301
302
303
    if skip_dgrad and skip_wgrad:
        pytest.skip("No gradient computation; Skipping to avoid PyTorch RuntimeError.")

Przemek Tredak's avatar
Przemek Tredak committed
304
    te_inp = torch.randn(
305
        (config.max_seqlen_q, config.batch_size, config.hidden_size),
306
307
308
309
        dtype=dtype,
        device="cuda",
        requires_grad=not skip_dgrad,
    )
schetlur-nv's avatar
schetlur-nv committed
310
311
312
313

    if skip_wgrad:
        _disable_wgrads(block)

314
    use_fp8 = fp8_recipe is not None
315
    with autocast(enabled=use_fp8, recipe=fp8_recipe):
316
317
318
319
320
        if not microbatching:
            te_out = block(te_inp)
        else:
            _ = block(te_inp, is_first_microbatch=True)
            te_out = block(te_inp, is_first_microbatch=False)
Przemek Tredak's avatar
Przemek Tredak committed
321
322
323
324
325
326
327
    if isinstance(te_out, tuple):
        te_out = te_out[0]
    loss = te_out.sum()
    loss.backward()
    torch.cuda.synchronize()


328
def _test_sanity_normalization_amp(block, dtype, config, skip_wgrad, skip_dgrad):
329
330
331
332
    if skip_dgrad and skip_wgrad:
        pytest.skip("No gradient computation; Skipping to avoid PyTorch RuntimeError.")

    te_inp = torch.randn(
333
        (config.max_seqlen_q, config.batch_size, config.hidden_size),
334
335
336
        device="cuda",
        requires_grad=True,
    )
337
338
339
340
341
342
343
344
345
346
    te_inp.retain_grad()

    with torch.autocast(device_type="cuda", enabled=True, dtype=dtype):
        te_out = block(te_inp)
        loss = te_out.sum()
    loss.backward()

    torch.cuda.synchronize()

    assert te_out.dtype == dtype, "AMP wrong output type."
347
    assert te_inp.grad is not None, "Gradient should not be empty"
348
349
350
351
352
353
354
    assert te_inp.grad.dtype == torch.float32, "AMP wrong dgrad type."
    for name, p in block.named_parameters():
        if p.requires_grad:
            assert p.grad.dtype == torch.float32, f"AMP wrong wgrad type for {name}."


@pytest.mark.parametrize("dtype", param_types)
355
@pytest.mark.parametrize("model", ["small", "weird"])
356
357
358
@pytest.mark.parametrize("skip_wgrad", all_boolean)
@pytest.mark.parametrize("skip_dgrad", all_boolean)
@pytest.mark.parametrize("normalization", all_normalizations)
359
def test_sanity_normalization_amp(dtype, model, skip_wgrad, skip_dgrad, normalization):
360
361
362
    config = model_configs[model]
    module = RMSNorm if normalization == "RMSNorm" else LayerNorm

363
    block = module(config.hidden_size).to(dtype=torch.float32).cuda()
364
    _test_sanity_normalization_amp(block, dtype, config, skip_wgrad, skip_dgrad)
365
366


Przemek Tredak's avatar
Przemek Tredak committed
367
368
@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
369
@pytest.mark.parametrize("model", ["small", "weird"])
370
371
@pytest.mark.parametrize("skip_wgrad", all_boolean)
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
372
@pytest.mark.parametrize("skip_dgrad", all_boolean)
373
@pytest.mark.parametrize("normalization", all_normalizations)
374
@pytest.mark.parametrize("microbatching", all_boolean)
375
def test_sanity_layernorm_linear(
376
377
378
379
380
381
382
383
    dtype,
    fp8_recipe,
    model,
    skip_wgrad,
    zero_centered_gamma,
    skip_dgrad,
    normalization,
    microbatching,
384
):
385
386
387
    config = model_configs[model]

    if fp8_recipe is not None:
388
389
390
391
392
393
394
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if not config.is_fp8_supported():
395
            pytest.skip("Model config does not support FP8")
396
397
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
398

Przemek Tredak's avatar
Przemek Tredak committed
399
400
401
    sigma = 0.023
    init_method = init_method_normal(sigma)

402
403
404
405
406
407
408
409
    block = LayerNormLinear(
        config.hidden_size,
        config.hidden_size * 3,
        init_method=init_method,
        zero_centered_gamma=zero_centered_gamma,
        normalization=normalization,
        params_dtype=dtype,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
410
    )
411
    _test_sanity_common(block, dtype, config, fp8_recipe, skip_wgrad, skip_dgrad, microbatching)
Przemek Tredak's avatar
Przemek Tredak committed
412
413
414
415


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
416
@pytest.mark.parametrize("model", ["small", "weird"])
417
@pytest.mark.parametrize("skip_wgrad", all_boolean)
418
@pytest.mark.parametrize("skip_dgrad", all_boolean)
419
420
@pytest.mark.parametrize("microbatching", all_boolean)
def test_sanity_linear(dtype, fp8_recipe, model, skip_wgrad, skip_dgrad, microbatching):
Przemek Tredak's avatar
Przemek Tredak committed
421
422
    config = model_configs[model]

423
    if fp8_recipe is not None:
424
        if not is_fp8_supported(config):
425
            pytest.skip("Model config does not support FP8")
426
427
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
428

Przemek Tredak's avatar
Przemek Tredak committed
429
430
431
    sigma = 0.023
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

432
433
434
435
436
437
    block = Linear(
        config.hidden_size,
        config.hidden_size,
        init_method=output_layer_init_method,
        params_dtype=dtype,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
438
    )
439
    _test_sanity_common(block, dtype, config, fp8_recipe, skip_wgrad, skip_dgrad, microbatching)
Przemek Tredak's avatar
Przemek Tredak committed
440
441


442
443
444
445
446
447
448
@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes_with_zero)
@pytest.mark.parametrize("model", ["small", "weird"])
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
@pytest.mark.parametrize("fp8_model_params", all_boolean)
@pytest.mark.parametrize("use_bias", all_boolean)
def test_sanity_linear_with_zero_tokens(dtype, bs, model, fp8_recipe, fp8_model_params, use_bias):
449
450
    if NVTE_TEST_NVINSPECT_ENABLED and fp8_model_params:
        pytest.skip("Quantized model parameters are not supported in debug mode.")
451
452
    config = model_configs[model]
    ffn_hidden_size = 4 * config.hidden_size
453
    num_tokens = bs * config.max_seqlen_q
454
455

    if fp8_recipe is not None:
456
457
458
459
460
461
462
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if not config.is_fp8_supported():
463
            pytest.skip("Model config does not support FP8")
464
465
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
466
467

    use_fp8 = fp8_recipe is not None
468
    with quantized_model_init(enabled=use_fp8 and fp8_model_params, recipe=fp8_recipe):
469
470
471
        te_linear = Linear(
            config.hidden_size, ffn_hidden_size, bias=use_bias, params_dtype=dtype
        ).cuda()
472
473
474
475

    inp_hidden_states = torch.randn(
        num_tokens, config.hidden_size, dtype=dtype, requires_grad=True
    ).cuda()
476
    with autocast(enabled=use_fp8, recipe=fp8_recipe):
477
478
479
480
481
482
        out = te_linear(inp_hidden_states)
    loss = out.sum()
    loss.backward()
    assert out.shape == (num_tokens, ffn_hidden_size)


483
484
485
486
487
488
489
490
491
492
493
@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("bs", batch_sizes_with_zero)
@pytest.mark.parametrize("model", ["small", "weird"])
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
@pytest.mark.parametrize("fp8_model_params", all_boolean)
@pytest.mark.parametrize("use_bias", all_boolean)
@pytest.mark.parametrize("empty_split", ["first", "last", "middle"])
@pytest.mark.parametrize("num_gemms", [4])
def test_sanity_grouped_linear(
    dtype, bs, model, fp8_recipe, fp8_model_params, use_bias, num_gemms, empty_split
):
494
495
    if NVTE_TEST_NVINSPECT_ENABLED and fp8_model_params:
        pytest.skip("FP8 model parameters are not supported in debug mode.")
496
497
498
499
    config = model_configs[model]
    ffn_hidden_size = 4 * config.hidden_size
    # Small batch size used to catch bug from https://github.com/NVIDIA/TransformerEngine/pull/1527.
    bs = bs * 16
500
    num_tokens = bs * config.max_seqlen_q * (num_gemms - 1)
501
502

    if fp8_recipe is not None:
503
        if not is_fp8_supported(config):
504
            pytest.skip("Model config does not support FP8")
505
506
        if fp8_recipe.nvfp4():
            pytest.skip("NVFP4 not supported for grouped linear")
507
508

    use_fp8 = fp8_recipe is not None
509
    with quantized_model_init(enabled=use_fp8 and fp8_model_params, recipe=fp8_recipe):
510
511
512
513
514
515
516
        te_grouped_linear = GroupedLinear(
            num_gemms, config.hidden_size, ffn_hidden_size, bias=use_bias, params_dtype=dtype
        ).cuda()

    inp_hidden_states = torch.randn(
        num_tokens, config.hidden_size, dtype=dtype, requires_grad=True
    ).cuda()
517
    m_splits = [bs * config.max_seqlen_q] * num_gemms
518
519
520
521
522
523
524
    if empty_split == "first":
        m_splits[0] = 0
    elif empty_split == "last":
        m_splits[-1] = 0
    elif empty_split == "middle":
        m_splits[num_gemms // 2] = 0

525
    with autocast(enabled=use_fp8, recipe=fp8_recipe):
526
527
528
529
530
531
        out = te_grouped_linear(inp_hidden_states, m_splits)
    loss = out.sum()
    loss.backward()
    assert out.shape == (num_tokens, ffn_hidden_size)


Przemek Tredak's avatar
Przemek Tredak committed
532
533
@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
534
@pytest.mark.parametrize("model", ["small", "weird"])
535
536
@pytest.mark.parametrize("skip_wgrad", all_boolean)
@pytest.mark.parametrize("zero_centered_gamma", all_boolean)
537
@pytest.mark.parametrize("skip_dgrad", all_boolean)
538
@pytest.mark.parametrize("activation", all_activations)
539
@pytest.mark.parametrize("normalization", all_normalizations)
540
@pytest.mark.parametrize("microbatching", all_boolean)
541
def test_sanity_layernorm_mlp(
542
543
544
545
546
547
548
549
550
    dtype,
    fp8_recipe,
    model,
    skip_wgrad,
    zero_centered_gamma,
    skip_dgrad,
    activation,
    normalization,
    microbatching,
551
):
552
553
554
    config = model_configs[model]

    if fp8_recipe is not None:
555
556
557
558
559
560
561
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if not config.is_fp8_supported():
562
            pytest.skip("Model config does not support FP8")
563
564
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
565

Przemek Tredak's avatar
Przemek Tredak committed
566
567
568
569
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

570
571
572
573
574
575
576
577
578
579
    block = LayerNormMLP(
        config.hidden_size,
        4 * config.hidden_size,
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        zero_centered_gamma=zero_centered_gamma,
        activation=activation,
        normalization=normalization,
        params_dtype=dtype,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
580
    )
581
    _test_sanity_common(block, dtype, config, fp8_recipe, skip_wgrad, skip_dgrad, microbatching)
Przemek Tredak's avatar
Przemek Tredak committed
582
583
584
585


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
586
@pytest.mark.parametrize("model", ["small"])
587
@pytest.mark.parametrize("skip_wgrad", all_boolean)
ngoyal2707's avatar
ngoyal2707 committed
588
@pytest.mark.parametrize("bias", all_boolean)
589
@pytest.mark.parametrize("activation", ["gelu", "swiglu"])
590
@pytest.mark.parametrize("normalization", all_normalizations)
591
@pytest.mark.parametrize("parallel_attention_mlp", all_boolean)
592
593
594
595
596
597
598
599
600
601
def test_sanity_gpt(
    dtype,
    fp8_recipe,
    model,
    skip_wgrad,
    bias,
    activation,
    normalization,
    parallel_attention_mlp,
):
602
603
604
    config = model_configs[model]

    if fp8_recipe is not None:
605
606
607
608
609
610
611
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if not config.is_fp8_supported():
612
            pytest.skip("Model config does not support FP8")
613
614
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
615

Przemek Tredak's avatar
Przemek Tredak committed
616
617
618
619
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

620
621
622
    block = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
623
        config.num_heads,
624
625
626
627
628
629
630
631
632
633
634
635
636
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        bias=bias,
        activation=activation,
        normalization=normalization,
        device="cuda",
        parallel_attention_mlp=parallel_attention_mlp,
Przemek Tredak's avatar
Przemek Tredak committed
637
638
    )

639
    _test_sanity_e2e(block, dtype, config, fp8_recipe, skip_wgrad)
640
641
642
643
644
645


def test_sanity_gpt_126m():
    fp8_recipe = None
    if fp8_available:
        fp8_recipe = recipe.DelayedScaling(
646
647
            margin=0,
            fp8_format=recipe.Format.E4M3,
648
649
650
651
652
653
654
655
656
657
658
659
660
            amax_history_len=16,
            amax_compute_algo="most_recent",
        )
    test_sanity_gpt(
        dtype=param_types[-1],
        fp8_recipe=fp8_recipe,
        model="126m",
        skip_wgrad=False,
        bias=True,
        activation="gelu",
        normalization="LayerNorm",
        parallel_attention_mlp=False,
    )
Przemek Tredak's avatar
Przemek Tredak committed
661
662
663
664


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
665
@pytest.mark.parametrize("model", ["small"])
666
@pytest.mark.parametrize("skip_wgrad", all_boolean)
667
@pytest.mark.parametrize("normalization", all_normalizations)
668
def test_sanity_bert(dtype, fp8_recipe, model, skip_wgrad, normalization):
669
670
671
672
673
    config = model_configs[model]

    if fp8_recipe is not None:
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
674
        if not is_fp8_supported(config):
675
            pytest.skip("Model config does not support FP8")
676
677
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
678

Przemek Tredak's avatar
Przemek Tredak committed
679
680
681
682
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

683
684
685
    block = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
686
        config.num_heads,
687
688
689
690
691
692
693
694
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=True,
        output_layernorm=True,
695
        self_attn_mask_type="causal",
696
697
        normalization=normalization,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
698
699
    )

700
701
702
703
704
    _test_sanity_e2e_bert(block, dtype, config, fp8_recipe, skip_wgrad)


def test_sanity_bert_126m():
    fp8_recipe = recipe.DelayedScaling(
705
706
        margin=0,
        fp8_format=recipe.Format.E4M3,
707
708
709
710
711
712
713
714
715
716
        amax_history_len=1,
        amax_compute_algo="most_recent",
    )
    test_sanity_bert(
        dtype=param_types[-1],
        fp8_recipe=fp8_recipe,
        model="126m",
        skip_wgrad=False,
        normalization="LayerNorm",
    )
Przemek Tredak's avatar
Przemek Tredak committed
717
718
719
720


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
721
@pytest.mark.parametrize("model", ["small"])
722
@pytest.mark.parametrize("skip_wgrad", all_boolean)
723
@pytest.mark.parametrize("normalization", all_normalizations)
724
def test_sanity_T5(dtype, fp8_recipe, model, skip_wgrad, normalization):
725
726
727
728
729
    config = model_configs[model]

    if fp8_recipe is not None:
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
730
        if not is_fp8_supported(config):
731
            pytest.skip("Model config does not support FP8")
732
733
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
734

Przemek Tredak's avatar
Przemek Tredak committed
735
736
737
738
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

739
740
741
    block = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
742
        config.num_heads,
743
744
745
746
747
748
749
750
751
752
753
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        layer_type="decoder",
        normalization=normalization,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
754
755
    )

756
757
758
759
760
    _test_sanity_e2e_T5(block, dtype, config, fp8_recipe, skip_wgrad)


def test_sanity_T5_126m():
    fp8_recipe = recipe.DelayedScaling(
761
762
        margin=0,
        fp8_format=recipe.Format.E4M3,
763
764
765
766
767
768
769
770
771
772
        amax_history_len=1,
        amax_compute_algo="most_recent",
    )
    test_sanity_T5(
        dtype=param_types[-1],
        fp8_recipe=fp8_recipe,
        model="126m",
        skip_wgrad=False,
        normalization="LayerNorm",
    )
Przemek Tredak's avatar
Przemek Tredak committed
773
774
775
776


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
777
@pytest.mark.parametrize("model", ["small"])
778
@pytest.mark.parametrize("skip_wgrad", all_boolean)
779
def test_sanity_amp_and_nvfuser(dtype, fp8_recipe, model, skip_wgrad):
Przemek Tredak's avatar
Przemek Tredak committed
780
781
    config = model_configs[model]

782
    if fp8_recipe is not None:
783
784
785
786
787
788
789
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if not config.is_fp8_supported():
790
            pytest.skip("Model config does not support FP8")
791
792
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
793

Przemek Tredak's avatar
Przemek Tredak committed
794
795
796
797
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

798
799
800
    block = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
801
        config.num_heads,
802
803
804
805
806
807
808
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=torch.float32,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
809
810
    )

811
    _test_sanity_e2e_amp(block, dtype, config, fp8_recipe, skip_wgrad)
Przemek Tredak's avatar
Przemek Tredak committed
812
813
814
815


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
816
@pytest.mark.parametrize("model", ["small"])
817
def test_sanity_drop_path(dtype, fp8_recipe, model):
Przemek Tredak's avatar
Przemek Tredak committed
818
819
    config = model_configs[model]

820
    if fp8_recipe is not None:
821
822
823
824
825
826
827
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if not config.is_fp8_supported():
828
            pytest.skip("Model config does not support FP8")
829
830
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
831

Przemek Tredak's avatar
Przemek Tredak committed
832
833
834
835
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

836
837
838
    block = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
839
        config.num_heads,
840
841
842
843
844
845
846
847
848
849
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        drop_path_rate=1.0,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
850
851
    )

852
    _test_sanity_e2e(block, dtype, config, fp8_recipe, False)
Przemek Tredak's avatar
Przemek Tredak committed
853
854
855
856


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
857
@pytest.mark.parametrize("model", ["small"])
858
@pytest.mark.parametrize("skip_wgrad", all_boolean)
859
def test_sanity_fused_qkv_params(dtype, fp8_recipe, model, skip_wgrad):
Przemek Tredak's avatar
Przemek Tredak committed
860
861
    config = model_configs[model]

862
    if fp8_recipe is not None:
863
864
865
866
867
868
869
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if not config.is_fp8_supported():
870
            pytest.skip("Model config does not support FP8")
871
872
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
873

Przemek Tredak's avatar
Przemek Tredak committed
874
875
876
877
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

878
879
880
    block = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
881
        config.num_heads,
882
883
884
885
886
887
888
889
890
891
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        fuse_qkv_params=True,
        device="cuda",
Przemek Tredak's avatar
Przemek Tredak committed
892
893
    )

894
    _test_sanity_e2e(block, dtype, config, fp8_recipe, skip_wgrad)
895
896
897
898


@pytest.mark.parametrize("dtype", param_types)
@pytest.mark.parametrize("fp8_recipe", fp8_recipes)
899
@pytest.mark.parametrize("model", ["small"])
900
@pytest.mark.parametrize("skip_wgrad", all_boolean)
901
def test_sanity_gradient_accumulation_fusion(dtype, fp8_recipe, model, skip_wgrad):
902
903
    config = model_configs[model]

904
    if fp8_recipe is not None:
905
906
907
908
909
910
911
        if not fp8_available:
            pytest.skip(reason_for_no_fp8)
        if fp8_recipe.float8_block_scaling() and not fp8_block_scaling_available:
            pytest.skip(reason_for_no_fp8_block_scaling)
        if fp8_recipe.mxfp8() and not mxfp8_available:
            pytest.skip(reason_for_no_mxfp8)
        if not config.is_fp8_supported():
912
            pytest.skip("Model config does not support FP8")
913
914
        if fp8_recipe.nvfp4() and dtype == torch.float16:
            pytest.skip("FP16 output for NVFP4 not supported")
915

916
917
918
919
    sigma = 0.023
    init_method = init_method_normal(sigma)
    output_layer_init_method = scaled_init_method_normal(sigma, config.num_layers)

920
921
922
    block = TransformerLayer(
        config.hidden_size,
        4 * config.hidden_size,
923
        config.num_heads,
924
925
926
927
928
929
930
931
932
933
934
        init_method=init_method,
        output_layer_init_method=output_layer_init_method,
        hidden_dropout=0.1,
        attention_dropout=0.1,
        kv_channels=config.kv_channels,
        params_dtype=dtype,
        apply_residual_connection_post_layernorm=False,
        output_layernorm=False,
        fuse_qkv_params=True,
        fuse_wgrad_accumulation=True,
        device="cuda",
935
936
    )

937
    _test_sanity_e2e_gradient_accumulation_fusion(block, dtype, config, fp8_recipe, skip_wgrad)
938
939


940
def test_model_multiple_cast():
941
942
    a = torch.zeros((16, 16), device="cuda")
    m = Linear(16, 32)
943
944
945
946
947
948
949
950
951

    y = m(a)
    assert y.dtype == torch.float32

    m.half()
    a = a.half()

    y2 = m(a)
    assert y2.dtype == torch.float16
952
953
954
955
956
957


@pytest.mark.parametrize("N", [32])
@pytest.mark.parametrize("offset", [1, 3, 5])
@pytest.mark.parametrize("datatype", param_types)
def test_sanity_gemm_with_unalignment(N, offset, datatype):
958
    scratchpad = torch.randn(N * N + 2 * offset, device="cuda", dtype=datatype)
959
    inp = torch.reshape(scratchpad[offset:-offset], (N, N))
960
    weight = torch.reshape(scratchpad[offset * 2 :], (N, N))
961

962
    _ = general_gemm(A=weight, B=inp, workspace=get_workspace())
963
964
965
966
967
968
969
970
    torch.cuda.synchronize()


@pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8)
@pytest.mark.parametrize("N", [32])
@pytest.mark.parametrize("datatype", [torch.float16, torch.bfloat16])
def test_sanity_fp8_gemm_with_unalignment(N, datatype):
    offset = 16
971
    scratchpad = torch.randn(N, N * N + offset, device="cuda", dtype=datatype)
972

973
974
975
976
    scales = torch.ones(1).cuda().squeeze()
    amaxes = torch.ones(1).cuda().squeeze()
    dtype = tex.DType.kFloat8E4M3
    fp8_quantizer = Float8Quantizer(scales, amaxes, dtype)
977
978
979

    outp_type = datatype

980
981
982
983
    scratchpad_fp8 = fp8_quantizer(scratchpad)
    inp_fp8 = torch.reshape(scratchpad_fp8[0][:-offset], (N, N))
    weight_fp8 = torch.reshape(scratchpad_fp8[0][offset:], (N, N))
    general_gemm(
984
985
986
        weight_fp8,
        inp_fp8,
        get_workspace(),
987
        outp_type,
988
989
990
        bias=None,
        use_split_accumulator=False,
    )
991
    torch.cuda.synchronize()
992
993


994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
@pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8)
def test_replace_raw_data_for_float8tensor():
    """Test the functionality of replace_raw_data"""
    torch.manual_seed(12345)
    torch.cuda.manual_seed(12345)

    fp8_quantizer = Float8CurrentScalingQuantizer(fp8_dtype=tex.DType.kFloat8E4M3, device="cuda")
    fp8_tensor = fp8_quantizer.make_empty([128, 128], dtype=torch.bfloat16, device="cuda")
    random_bf16_data = torch.randn(fp8_tensor.shape, dtype=torch.bfloat16, device="cuda")
    fp8_quantizer.update_quantized(random_bf16_data, fp8_tensor)

    attrs_to_check = ["_quantizer", "_fp8_dtype", "_scale_inv", "_transpose", "_transpose_invalid"]
    attrs = {}
    for attr in attrs_to_check:
        attrs[attr] = getattr(fp8_tensor, attr)

    old_data = fp8_tensor._data
    new_data = torch.empty_like(old_data)
    replace_raw_data(fp8_tensor, new_data)

    # Make sure the new_data is properly assigned.
    assert fp8_tensor._data.data_ptr() != old_data.data_ptr()
    assert fp8_tensor._data.data_ptr() == new_data.data_ptr()
    # Make sure the values are not changed.
    torch.testing.assert_close(old_data, fp8_tensor._data, atol=0, rtol=0)
    # Make sure other attributes are not changed (totally identical)
    for attr in attrs_to_check:
        assert id(getattr(fp8_tensor, attr)) == id(attrs[attr])


@pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8)
1025
1026
1027
def test_quantized_model_init_high_precision_init_val():
    """Test quantized_model_init with preserve_high_precision_init_val=True"""
    with quantized_model_init(preserve_high_precision_init_val=True):
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
        model = Linear(768, 768)

    weight = model.weight

    assert isinstance(weight, QuantizedTensor), "Weight should be QuantizedTensor"
    assert hasattr(weight, "_high_precision_init_val"), "_high_precision_init_val not found"
    assert hasattr(weight, "get_high_precision_init_val"), "get_high_precision_init_val() not found"
    assert hasattr(
        weight, "clear_high_precision_init_val"
    ), "clear_high_precision_init_val() not found"

    high_precision = weight.get_high_precision_init_val()
    assert high_precision.device.type == "cpu", "high_precision_init_val is not on the CPU"

    new_weight = weight._get_quantizer().make_empty(
        shape=weight.shape, dtype=weight.dtype, device=weight.device
    )
    weight._get_quantizer().update_quantized(high_precision.to(weight.device), new_weight)

    torch.testing.assert_close(
        new_weight.dequantize(dtype=weight.dtype),
        weight.dequantize(dtype=weight.dtype),
        rtol=0,
        atol=0,
    )

    weight.clear_high_precision_init_val()
    assert weight.get_high_precision_init_val() is None, "clear_high_precision_init_val() not work"
    assert not hasattr(
        weight, "._high_precision_init_val"
    ), "clear_high_precision_init_val() not work"
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086


def test_sanity_checkpointing_on_callables():
    """Test that TE checkpointing works correctly on callable modules."""

    # torch.autograf.function
    class MyFunction(torch.autograd.Function):
        @staticmethod
        def forward(ctx, inp):
            return inp

        @staticmethod
        def backward(ctx, grad_output):
            return grad_output

    module = MyFunction.apply
    inp = torch.randn(10, 10, device="cuda", requires_grad=True)

    out_checkpoint = checkpoint(module, inp)
    out_checkpoint.sum().backward()
    grad_checkpoint = inp.grad

    out_standard = module(inp)
    out_standard.sum().backward()
    grad_standard = inp.grad

    # Assert that gradients are the same
    torch.testing.assert_close(grad_checkpoint, grad_standard)
1087
1088


1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
@pytest.mark.skipif(not fp8_available, reason=reason_for_no_fp8)
def test_linear_frozen_weights_memory_default_recipe():
    """Test that memory usage is optimized when weights are frozen for MXFP8."""
    dim = 1024
    linear = Linear(dim, dim, bias=False)
    x = torch.randn(dim, dim, requires_grad=True, device="cuda")

    # Freeze weights
    linear.weight.requires_grad = False

    # Forward and backward pass with FP8
1100
    with autocast():
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
        o = linear(x)
        g_o = torch.randn_like(o)

    max_memory_before_backward = torch.cuda.max_memory_allocated()
    o.backward(g_o)
    max_memory_after_backward = torch.cuda.max_memory_allocated()

    memory_diff = (max_memory_after_backward - max_memory_before_backward) / 1e6
    assert memory_diff < 5.5, (
        f"Memory usage with frozen weights ({memory_diff}MB) should be less than 5.5MB as the"
        " grad_output should be quantized only columnwise."
    )


1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
@pytest.mark.parametrize(
    "module_name",
    ("Linear", "LayerNormLinear", "LayerNormMLP", "GroupedLinear", "ops.Linear"),
)
@pytest.mark.parametrize(
    "quantization",
    (None, "fp8_delayed_scaling", "fp8_current_scaling", "mxfp8"),
)
def test_inference_mode(
    module_name: str,
    quantization: Optional[str],
) -> None:
    """Test heuristics for initializing quantized weights"""
1128
1129
    if NVTE_TEST_NVINSPECT_ENABLED and quantization is not None:
        pytest.skip("Quantized model parameters are not supported in debug mode.")
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153

    # Tensor dimensions
    sequence_length = 32
    hidden_size = 32

    # Skip invalid configurations
    if quantization in ("fp8_delayed_scaling", "fp8_current_scaling") and not fp8_available:
        pytest.skip(reason_for_no_fp8)
    if quantization == "mxfp8" and not mxfp8_available:
        pytest.skip(reason_for_no_mxfp8)

    # Construct quantization recipe
    with_quantization = quantization not in (None, "None")
    quantization_recipe = None
    if quantization == "fp8_delayed_scaling":
        quantization_recipe = recipe.DelayedScaling()
    elif quantization == "fp8_current_scaling":
        quantization_recipe = recipe.Float8CurrentScaling()
    elif quantization == "mxfp8":
        quantization_recipe = recipe.MXFP8BlockScaling()

    # Construct module
    module = None
    with torch.no_grad():
1154
        with quantized_model_init(enabled=with_quantization, recipe=quantization_recipe):
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
            if module_name == "Linear":
                module = Linear(hidden_size, hidden_size)
            elif module_name == "LayerNormLinear":
                module = LayerNormLinear(hidden_size, hidden_size)
            elif module_name == "LayerNormMLP":
                module = LayerNormMLP(hidden_size, hidden_size)
            elif module_name == "GroupedLinear":
                module = GroupedLinear(1, hidden_size, hidden_size)
            elif module_name == "ops.Linear":
                module = transformer_engine.pytorch.ops.Linear(hidden_size, hidden_size)

    def check_weights():
        """Helper function to check that weight parameters have expected data"""
        for param in module.parameters():
            if isinstance(param, Float8Tensor):
                assert param._data is not None, "Missing FP8 data"
                assert (
                    param._transpose is None and param._transpose_invalid
                ), "FP8 transpose is not expected for inference"
            if isinstance(param, MXFP8Tensor):
                assert param._rowwise_data is not None, "Missing row-wise MXFP8 data"
                assert (
                    param._columnwise_data is None
                ), "Column-wise MXFP8 data is not expected for inference"

    # Check that modules have expected weights after initialization
    check_weights()

    # Check that modules have expected weights after forward pass
    with torch.inference_mode():
        x = torch.zeros(sequence_length, hidden_size, device="cuda")
        kwargs = {}
        if module_name == "GroupedLinear":
            kwargs["m_splits"] = [sequence_length]
1189
        with autocast(enabled=with_quantization, recipe=quantization_recipe):
1190
1191
            y = module(x, **kwargs)
    check_weights()