File size: 13,038 Bytes
dfefe0b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 | diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 269c093aa6c9..286d03a55982 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -2232,7 +2232,7 @@ def _prefetch_next_layer(self, layer_idx: int) -> None:
def _prefetch_layer_in_context(self, layer_idx: int) -> None:
"""Performs the actual copy of the layer to device cache."""
- if len(self.key_cache) >= layer_idx:
+ if len(self.key_cache) > layer_idx:
self.device_key_cache[self.active_device_layer].copy_(self.key_cache[layer_idx], non_blocking=True)
self.device_value_cache[self.active_device_layer].copy_(self.value_cache[layer_idx], non_blocking=True)
# The layer was not yet initialized
diff --git a/src/transformers/integrations/executorch.py b/src/transformers/integrations/executorch.py
index eb17dab55af7..bd4b30a3d125 100644
--- a/src/transformers/integrations/executorch.py
+++ b/src/transformers/integrations/executorch.py
@@ -11,7 +11,6 @@
# specific language governing permissions and limitations under the License.
import logging
-from contextlib import contextmanager
from typing import Callable, Optional
import torch
@@ -110,14 +109,13 @@ def export(
example_input_ids = input_ids if input_ids is not None else torch.tensor([[1]], dtype=torch.long)
example_cache_position = cache_position if cache_position is not None else torch.tensor([0], dtype=torch.long)
- with patch_mask_interface():
- exported_program = torch.export.export(
- self.model,
- args=(example_input_ids, example_cache_position),
- kwargs={},
- dynamic_shapes=dynamic_shapes,
- strict=strict if strict is not None else True,
- )
+ exported_program = torch.export.export(
+ self.model,
+ args=(example_input_ids, example_cache_position),
+ kwargs={},
+ dynamic_shapes=dynamic_shapes,
+ strict=strict if strict is not None else True,
+ )
return exported_program
@staticmethod
@@ -456,24 +454,6 @@ def forward(
return outputs.logits
-@contextmanager
-def patch_mask_interface():
- """
- Context manager to locally use a simple dict instead of `AttentionMaskInterface`, as otherwise export will fail
- with `strict=True` due to dynamo skip rules, i.e. `torch._dynamo.exc.Unsupported: 'inline in skipfiles:
- Mapping.__contains__ | __contains__, skipped according trace_rules.lookup SKIP_DIRS'`.
- Note that this seem to be an issue only for python<3.11.
- """
- import transformers
-
- original = transformers.masking_utils.ALL_MASK_ATTENTION_FUNCTIONS
- transformers.masking_utils.ALL_MASK_ATTENTION_FUNCTIONS = ALL_MASK_ATTENTION_FUNCTIONS._global_mapping
- try:
- yield
- finally:
- transformers.masking_utils.ALL_MASK_ATTENTION_FUNCTIONS = original
-
-
def convert_and_export_with_cache(
model: PreTrainedModel,
example_input_ids: Optional[torch.Tensor] = None,
@@ -515,14 +495,13 @@ def convert_and_export_with_cache(
)
if is_torch_greater_or_equal("2.6.0"):
- with patch_mask_interface():
- exported_program = torch.export.export(
- TorchExportableModuleWithStaticCache(model),
- args=(example_input_ids, example_cache_position),
- kwargs={},
- dynamic_shapes=dynamic_shapes,
- strict=strict if strict is not None else True,
- )
+ exported_program = torch.export.export(
+ TorchExportableModuleWithStaticCache(model),
+ args=(example_input_ids, example_cache_position),
+ kwargs={},
+ dynamic_shapes=dynamic_shapes,
+ strict=strict if strict is not None else True,
+ )
else:
if dynamic_shapes is not None:
logging.warning(
@@ -534,14 +513,13 @@ def convert_and_export_with_cache(
#
# Due to issue https://github.com/pytorch/pytorch/issues/128394, we need to switch to use an internal
# export API and pre_dispatch=False. Switch to use the public API once the issue is included in 2.5 release.
- with patch_mask_interface():
- exported_program = torch.export._trace._export(
- TorchExportableModuleWithStaticCache(model),
- args=(example_input_ids,),
- kwargs={"cache_position": example_cache_position},
- pre_dispatch=False,
- strict=True,
- )
+ exported_program = torch.export._trace._export(
+ TorchExportableModuleWithStaticCache(model),
+ args=(example_input_ids,),
+ kwargs={"cache_position": example_cache_position},
+ pre_dispatch=False,
+ strict=True,
+ )
return exported_program
@@ -634,10 +612,9 @@ def _export_encoder(self, encoder_input_ids):
# Export the encoder
with torch.no_grad():
- with patch_mask_interface():
- exported_encoder = torch.export.export(
- wrapped_encoder, (encoder_input_ids,), dynamic_shapes={"input_ids": {1: seq_len_dim}}, strict=True
- )
+ exported_encoder = torch.export.export(
+ wrapped_encoder, (encoder_input_ids,), dynamic_shapes={"input_ids": {1: seq_len_dim}}, strict=True
+ )
return exported_encoder
@@ -657,17 +634,16 @@ def _export_decoder(self, decoder_input_ids, encoder_hidden_states, cache_positi
# Export the decoder
with torch.no_grad():
- with patch_mask_interface():
- exported_decoder = torch.export.export(
- wrapped_decoder,
- (decoder_input_ids, encoder_hidden_states, cache_position),
- dynamic_shapes={
- "decoder_input_ids": None,
- "encoder_hidden_states": {1: encoder_seq_len_dim},
- "cache_position": None,
- },
- strict=True,
- )
+ exported_decoder = torch.export.export(
+ wrapped_decoder,
+ (decoder_input_ids, encoder_hidden_states, cache_position),
+ dynamic_shapes={
+ "decoder_input_ids": None,
+ "encoder_hidden_states": {1: encoder_seq_len_dim},
+ "cache_position": None,
+ },
+ strict=True,
+ )
return exported_decoder
diff --git a/src/transformers/masking_utils.py b/src/transformers/masking_utils.py
index 36538882af57..53a81e1daaf5 100644
--- a/src/transformers/masking_utils.py
+++ b/src/transformers/masking_utils.py
@@ -623,7 +623,11 @@ def _preprocess_mask_arguments(
return True, attention_mask, None, None
# For TGI/vLLM backends, or other custom attention without equivalent mask creation: we don't need a mask!
- if config._attn_implementation not in ALL_MASK_ATTENTION_FUNCTIONS:
+ # Note: it's not ideal to check the `_global_mapping` attribute instead of the object itself, however otherwise
+ # full graph dynamo tracing (i.e. torch.export or compile with `fullgraph=True`) will fail on Python<3.11
+ # with `torch._dynamo.exc.Unsupported: 'inline in skipfiles:Mapping.__contains__ | __contains__, skipped
+ # according trace_rules.lookup SKIP_DIRS'` -- can be removed when we require Python>=3.11
+ if config._attn_implementation not in ALL_MASK_ATTENTION_FUNCTIONS._global_mapping:
return True, None, None, None
# Move the mask to correct device, and potentially switch dtype for efficiency
diff --git a/tests/models/cohere/test_modeling_cohere.py b/tests/models/cohere/test_modeling_cohere.py
index bebafedc7df8..ff7963ae7e0d 100644
--- a/tests/models/cohere/test_modeling_cohere.py
+++ b/tests/models/cohere/test_modeling_cohere.py
@@ -232,8 +232,8 @@ def test_batched_small_model_logits(self):
EXPECTED_LOGITS = torch.Tensor(
[
- [[0.0000, 0.1866, -0.1997], [0.0000, -0.0736, 0.1785], [0.0000, -0.1965, -0.0569]],
- [[0.0000, -0.0302, 0.1488], [0.0000, -0.0402, 0.1351], [0.0000, -0.0341, 0.1116]],
+ [[0.0000, 0.0285, 0.0322], [0.0000, 0.0011, 0.1105], [0.0000, -0.0018, -0.1019]],
+ [[0.0000, 0.1080, 0.0454], [0.0000, -0.1808, -0.1553], [0.0000, 0.0452, 0.0369]],
]
).to(device=torch_device, dtype=torch.float16)
@@ -251,4 +251,4 @@ def test_batched_small_model_logits(self):
output = model(**inputs)
logits = output.logits
- torch.testing.assert_close(EXPECTED_LOGITS, logits[:, :3, :3], rtol=1e-3, atol=1e-3)
+ torch.testing.assert_close(EXPECTED_LOGITS, logits[:, -3:, :3], rtol=1e-3, atol=1e-3)
diff --git a/tests/models/csm/test_modeling_csm.py b/tests/models/csm/test_modeling_csm.py
index be4ab6a0e2a0..26442ef84588 100644
--- a/tests/models/csm/test_modeling_csm.py
+++ b/tests/models/csm/test_modeling_csm.py
@@ -150,7 +150,6 @@ class CsmForConditionalGenerationTest(ModelTesterMixin, GenerationTesterMixin, u
test_headmasking = False
test_resize_embeddings = False
test_resize_embeddings_untied = False
- test_torch_exportable = True
def setUp(self):
self.model_tester = CsmModelTester(self)
diff --git a/tests/models/mixtral/test_modeling_mixtral.py b/tests/models/mixtral/test_modeling_mixtral.py
index 2d7c95529be2..8f4215c7205f 100644
--- a/tests/models/mixtral/test_modeling_mixtral.py
+++ b/tests/models/mixtral/test_modeling_mixtral.py
@@ -402,24 +402,12 @@ def test_small_model_logits_batched(self):
#
# Note: Key 9 is currently set for MI300, but may need potential future adjustments for H100s,
# considering differences in hardware processing and potential deviations in generated text.
- EXPECTED_LOGITS_LEFT = {
- 7: torch.Tensor(
- [[0.1904, 0.0500, 0.7187], [0.1933, 0.0515, 0.7187], [0.2001, 0.0559, 0.7148]],
- ).to(torch_device),
- 8: torch.Tensor([[0.1914, 0.0508, 0.7188], [0.1953, 0.0510, 0.7227], [0.1973, 0.0562, 0.7148]]).to(
- torch_device
- ),
- 9: torch.Tensor([[0.1904, 0.0513, 0.7227], [0.1943, 0.0518, 0.7227], [0.1982, 0.0557, 0.7148]]).to(
- torch_device
- ),
- }
-
EXPECTED_LOGITS_LEFT_UNPADDED = {
7: torch.Tensor(
[[0.2236, 0.5195, -0.3828], [0.8203, -0.2275, 0.6054], [0.2656, -0.7070, 0.2460]],
).to(torch_device),
- 8: torch.Tensor([[0.2217, 0.5195, -0.3828], [0.8203, -0.2295, 0.6055], [0.2676, -0.7109, 0.2461]]).to(
- torch_device
+ 8: torch.Tensor([[0.2207, 0.5234, -0.3828], [0.8203, -0.2285, 0.6055], [0.2656, -0.7109, 0.2451]]).to(
+ torch_device,
),
9: torch.Tensor([[0.2236, 0.5195, -0.3828], [0.8203, -0.2285, 0.6055], [0.2637, -0.7109, 0.2451]]).to(
torch_device
@@ -430,8 +418,8 @@ def test_small_model_logits_batched(self):
7: torch.Tensor([[0.2167, 0.1269, -0.1640], [-0.3496, 0.2988, -1.0312], [0.0688, 0.7929, 0.8007]]).to(
torch_device
),
- 8: torch.Tensor([[0.2178, 0.1260, -0.1621], [-0.3496, 0.2988, -1.0312], [0.0693, 0.7930, 0.8008]]).to(
- torch_device
+ 8: torch.Tensor([[0.2178, 0.1270, -0.1621], [-0.3496, 0.3008, -1.0312], [0.0693, 0.7930, 0.7969]]).to(
+ torch_device,
),
9: torch.Tensor([[0.2197, 0.1250, -0.1611], [-0.3516, 0.3008, -1.0312], [0.0684, 0.7930, 0.8008]]).to(
torch_device
@@ -442,9 +430,6 @@ def test_small_model_logits_batched(self):
logits = model(dummy_input, attention_mask=attention_mask).logits
logits = logits.float()
- torch.testing.assert_close(
- logits[0, :3, :3], EXPECTED_LOGITS_LEFT[self.cuda_compute_capability_major_version], atol=1e-3, rtol=1e-3
- )
torch.testing.assert_close(
logits[0, -3:, -3:],
EXPECTED_LOGITS_LEFT_UNPADDED[self.cuda_compute_capability_major_version],
diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py
index a85c9e7e6256..ddef77eef13e 100755
--- a/tests/test_modeling_common.py
+++ b/tests/test_modeling_common.py
@@ -4461,6 +4461,7 @@ def test_torch_compile_for_training(self):
del loss
model = torch.compile(model, fullgraph=True, mode="reduce-overhead")
+
# forward compilation
set_seed(42)
loss = model(**inputs).loss
|