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Unverified Commit acab997b authored by Arthur's avatar Arthur Committed by GitHub
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Revert "Re-enable SDPA's FA2 path (#30070)" (#30314) 

* Revert "Re-enable SDPA's FA2 path (#30070)"

This reverts commit 05bdef16.

* Revert "Fix quality Olmo + SDPA (#30302)"

This reverts commit ec92f983.
parent 7509a0ad
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Showing with 69 additions and 201 deletions
src/transformers/modeling_attn_mask_utils.py
View file @ acab997b
......@@ -234,59 +234,6 @@ class AttentionMaskConverter:
return expanded_mask.mul(~torch.all(expanded_mask == min_dtype, dim=-1, keepdim=True))
@staticmethod
def _ignore_causal_mask_sdpa(
attention_mask: Optional[torch.Tensor],
inputs_embeds: torch.Tensor,
past_key_values_length: int,
sliding_window: Optional[int] = None,
) -> bool:
"""
Detects whether the optional user-specified attention_mask & the automatically created causal mask can be ignored in case PyTorch's SDPA is used, rather relying on SDPA's `is_causal` argument.
In case no token is masked in the `attention_mask` argument, if `query_length == 1` or
`key_value_length == query_length`, we rather rely on SDPA `is_causal` argument to use causal/non-causal masks,
allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
"""
batch_size, query_length = inputs_embeds.shape[0], inputs_embeds.shape[1]
key_value_length = query_length + past_key_values_length
is_tracing = (
torch.jit.is_tracing()
or isinstance(inputs_embeds, torch.fx.Proxy)
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
)
ignore_causal_mask = False
if attention_mask is None:
# TODO: When tracing with TorchDynamo with fullgraph=True, the model is recompiled depending on the input shape, thus SDPA's `is_causal` argument is rightfully updated (see https://gist.github.com/fxmarty/1313f39037fc1c112508989628c57363). However, when using `torch.export` or
# or `torch.onnx.dynamo_export`, we must pass an example input, and `is_causal` behavior is hard-coded. If a user exports a model with q_len > 1, the exported model will hard-code `is_causal=True` which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108).
# Thus, we currently can NOT set `ignore_causal_mask = True` here. We would need a `torch._dynamo.is_exporting()` flag.
#
# Besides, jit.trace can not handle the `q_len > 1` condition for `is_causal` (`TypeError: scaled_dot_product_attention(): argument 'is_causal' must be bool, not Tensor`).
if sliding_window is None or key_value_length < sliding_window:
ignore_causal_mask = not is_tracing
elif sliding_window is None or key_value_length < sliding_window:
if len(attention_mask.shape) == 4:
expected_shape = (batch_size, 1, query_length, key_value_length)
if tuple(attention_mask.shape) != expected_shape:
raise ValueError(
f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
)
elif not is_tracing and torch.all(attention_mask == 1):
if query_length == 1 or key_value_length == query_length:
# For query_length == 1, causal attention and bi-directional attention are the same.
ignore_causal_mask = True
# Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
# may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
# Reference: https://github.com/pytorch/pytorch/issues/108108
# TODO: maybe revisit this with https://github.com/pytorch/pytorch/pull/114823 in PyTorch 2.3.
return ignore_causal_mask
def _prepare_4d_causal_attention_mask(
attention_mask: Optional[torch.Tensor],
......@@ -358,6 +305,7 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
attn_mask_converter = AttentionMaskConverter(is_causal=True, sliding_window=sliding_window)
key_value_length = input_shape[-1] + past_key_values_length
_, query_length = input_shape
# torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
# used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
......@@ -368,12 +316,37 @@ def _prepare_4d_causal_attention_mask_for_sdpa(
or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
)
ignore_causal_mask = AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
past_key_values_length=past_key_values_length,
sliding_window=sliding_window,
)
ignore_causal_mask = False
if attention_mask is None:
if sliding_window is None or key_value_length < sliding_window:
ignore_causal_mask = not is_tracing
elif sliding_window is None or key_value_length < sliding_window:
# 4d mask is passed through
if len(attention_mask.shape) == 4:
expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
if tuple(attention_mask.shape) != expected_shape:
raise ValueError(
f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
)
else:
# if the 4D mask has correct shape - invert it and fill with negative infinity
inverted_mask = 1.0 - attention_mask.to(inputs_embeds.dtype)
attention_mask = inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
)
return attention_mask
elif not is_tracing and torch.all(attention_mask == 1):
if query_length == 1:
# For query_length == 1, causal attention and bi-directional attention are the same.
ignore_causal_mask = True
elif key_value_length == query_length:
ignore_causal_mask = True
# Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
# may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
# Reference: https://github.com/pytorch/pytorch/issues/108108
if ignore_causal_mask:
expanded_4d_mask = None
......
src/transformers/models/cohere/modeling_cohere.py
View file @ acab997b
......@@ -590,15 +590,12 @@ class CohereSdpaAttention(CohereAttention):
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# In case we are not compiling, we may set `causal_mask` to None, which is required to dispatch to SDPA's Flash Attention 2 backend, rather
# relying on the `is_causal` argument.
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=causal_mask is None and q_len > 1,
)
attn_output = attn_output.transpose(1, 2).contiguous()
......@@ -911,7 +908,9 @@ class CohereModel(CoherePreTrainedModel):
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_seen_tokens + inputs_embeds.shape[1]
)
# embed positions
hidden_states = inputs_embeds
......@@ -975,31 +974,16 @@ class CohereModel(CoherePreTrainedModel):
attentions=all_self_attns,
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_seen_tokens: int,
):
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
def _update_causal_mask(self, attention_mask, input_tensor, cache_position, current_length):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
if self.config._attn_implementation == "sdpa":
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument,
# in order to dispatch on Flash Attention 2.
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
......@@ -1007,9 +991,7 @@ class CohereModel(CoherePreTrainedModel):
target_length = self.config.max_position_embeddings
else: # dynamic cache
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else current_length + 1
)
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
......
src/transformers/models/gemma/modeling_gemma.py
View file @ acab997b
......@@ -570,15 +570,12 @@ class GemmaSdpaAttention(GemmaAttention):
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# In case we are not compiling, we may set `causal_mask` to None, which is required to dispatch to SDPA's Flash Attention 2 backend, rather
# relying on the `is_causal` argument.
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=causal_mask is None and q_len > 1,
)
attn_output = attn_output.transpose(1, 2).contiguous()
......@@ -891,7 +888,9 @@ class GemmaModel(GemmaPreTrainedModel):
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_seen_tokens + inputs_embeds.shape[1]
)
# embed positions
hidden_states = inputs_embeds
......@@ -961,31 +960,16 @@ class GemmaModel(GemmaPreTrainedModel):
attentions=all_self_attns,
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_seen_tokens: int,
):
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
def _update_causal_mask(self, attention_mask, input_tensor, cache_position, current_length):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
if self.config._attn_implementation == "sdpa":
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument,
# in order to dispatch on Flash Attention 2.
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
......@@ -993,9 +977,7 @@ class GemmaModel(GemmaPreTrainedModel):
target_length = self.config.max_position_embeddings
else: # dynamic cache
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else current_length + 1
)
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
......
src/transformers/models/llama/modeling_llama.py
View file @ acab997b
......@@ -656,6 +656,7 @@ class LlamaSdpaAttention(LlamaAttention):
value_states = repeat_kv(value_states, self.num_key_value_groups)
causal_mask = attention_mask
# if attention_mask is not None and cache_position is not None:
if attention_mask is not None:
causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
......@@ -666,15 +667,12 @@ class LlamaSdpaAttention(LlamaAttention):
key_states = key_states.contiguous()
value_states = value_states.contiguous()
# In case we are not compiling, we may set `causal_mask` to None, which is required to dispatch to SDPA's Flash Attention 2 backend, rather
# relying on the `is_causal` argument.
attn_output = torch.nn.functional.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=causal_mask is None and q_len > 1,
)
attn_output = attn_output.transpose(1, 2).contiguous()
......@@ -989,7 +987,9 @@ class LlamaModel(LlamaPreTrainedModel):
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_seen_tokens + inputs_embeds.shape[1]
)
# embed positions
hidden_states = inputs_embeds
......@@ -1053,31 +1053,16 @@ class LlamaModel(LlamaPreTrainedModel):
attentions=all_self_attns,
)
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_seen_tokens: int,
):
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
def _update_causal_mask(self, attention_mask, input_tensor, cache_position, current_length):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
if self.config._attn_implementation == "sdpa":
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument,
# in order to dispatch on Flash Attention 2.
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
......@@ -1085,9 +1070,7 @@ class LlamaModel(LlamaPreTrainedModel):
target_length = self.config.max_position_embeddings
else: # dynamic cache
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else current_length + 1
)
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
......
src/transformers/models/olmo/modeling_olmo.py
View file @ acab997b
......@@ -653,7 +653,6 @@ class OlmoSdpaAttention(OlmoAttention):
value_states,
attn_mask=causal_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=causal_mask is None and q_len > 1,
)
attn_output = attn_output.transpose(1, 2).contiguous()
......@@ -971,7 +970,9 @@ class OlmoModel(OlmoPreTrainedModel):
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
causal_mask = self._update_causal_mask(attention_mask, inputs_embeds, cache_position, past_seen_tokens)
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_seen_tokens + inputs_embeds.shape[1]
)
# embed positions
hidden_states = inputs_embeds
......@@ -1035,32 +1036,17 @@ class OlmoModel(OlmoPreTrainedModel):
attentions=all_self_attns,
)
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
# Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_seen_tokens: int,
):
# TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
# KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
# (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
# `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
def _update_causal_mask(self, attention_mask, input_tensor, cache_position, current_length):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask is not None and 0.0 in attention_mask:
return attention_mask
return None
if self.config._attn_implementation == "sdpa":
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument,
# in order to dispatch on Flash Attention 2.
if AttentionMaskConverter._ignore_causal_mask_sdpa(
attention_mask, inputs_embeds=input_tensor, past_key_values_length=past_seen_tokens
):
return None
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
......@@ -1068,9 +1054,7 @@ class OlmoModel(OlmoPreTrainedModel):
target_length = self.config.max_position_embeddings
else: # dynamic cache
target_length = (
attention_mask.shape[-1]
if isinstance(attention_mask, torch.Tensor)
else past_seen_tokens + sequence_length + 1
attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else current_length + 1
)
causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device)
......
tests/test_modeling_common.py
View file @ acab997b
......@@ -3772,42 +3772,6 @@ class ModelTesterMixin:
self.assertTrue(len(fail_cases) == 0, "\n".join(fail_cases))
@require_torch_sdpa
@require_torch_gpu
@slow
def test_sdpa_can_dispatch_on_flash(self):
compute_capability = torch.cuda.get_device_capability()
major, _ = compute_capability
if not torch.version.cuda or major < 8:
self.skipTest("This test requires an NVIDIA GPU with compute capability >= 8.0")
for model_class in self.all_model_classes:
if not model_class._supports_sdpa:
self.skipTest(f"{model_class.__name__} does not support SDPA")
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if config.model_type in ["llava", "llava_next", "vipllava"]:
self.skipTest("Llava-like models currently (transformers==4.39.1) requires an attention_mask input")
if config.model_type in ["idefics"]:
self.skipTest("Idefics currently (transformers==4.39.1) requires an image_attention_mask input")
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model = model_class.from_pretrained(tmpdirname, torch_dtype=torch.float16, attn_implementation="sdpa")
model.to(torch_device)
inputs_dict.pop("attention_mask", None)
inputs_dict.pop("decoder_attention_mask", None)
for name, inp in inputs_dict.items():
if isinstance(inp, torch.Tensor) and inp.dtype in [torch.float32, torch.float16]:
inputs_dict[name] = inp.to(torch.float16)
with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
_ = model(**inputs_dict)
@require_torch_sdpa
@slow
def test_eager_matches_sdpa_generate(self):
......
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