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https://github.com/saymrwulf/transformers.git
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fixing_qwen2moe
This commit is contained in:
MekkCyber
parent
32ed852df0
commit
ebad35797f
2 changed files with 180 additions and 240 deletions
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@ -56,6 +56,7 @@ from .configuration_qwen2 import Qwen2Config
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if is_flash_attn_2_available():
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from ...modeling_flash_attention_utils import _flash_attention_forward
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if is_torch_greater_or_equal("2.5"):
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from torch.nn.attention.flex_attention import flex_attention
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@ -490,7 +491,7 @@ class Qwen2FlashAttention2(Qwen2Attention):
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self.config._attn_implementation = "flash_attention_2"
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logger.warning_once(
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"The `Qwen2FlashAttention2` class is deprecated in favor of simply modifying the `config._attn_implementation`"
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"attribute of the `GemmaAttention` class! It will be removed in v4.48"
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"attribute of the `Qwen2Attention` class! It will be removed in v4.48"
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)
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@ -499,7 +500,7 @@ class Qwen2SdpaAttention(Qwen2Attention):
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super().__init__(config, layer_idx)
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self.config._attn_implementation = "sdpa"
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logger.warning_once(
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"The `Qwen2FlashAttention2` class is deprecated in favor of simply modifying the `config._attn_implementation`"
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"The `Qwen2SdpaAttention` class is deprecated in favor of simply modifying the `config._attn_implementation`"
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"attribute of the `Qwen2Attention` class! It will be removed in v4.48"
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)
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@ -46,6 +46,7 @@ from ...utils import (
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add_start_docstrings_to_model_forward,
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is_flash_attn_2_available,
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is_flash_attn_greater_or_equal_2_10,
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is_torch_greater_or_equal,
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logging,
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replace_return_docstrings,
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)
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@ -55,6 +56,10 @@ from .configuration_qwen2_moe import Qwen2MoeConfig
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if is_flash_attn_2_available():
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from ...modeling_flash_attention_utils import _flash_attention_forward
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if is_torch_greater_or_equal("2.5"):
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from torch.nn.attention.flex_attention import flex_attention
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logger = logging.get_logger(__name__)
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_CHECKPOINT_FOR_DOC = "Qwen/Qwen2-57B-A14B"
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@ -318,6 +323,139 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
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return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
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def eager_attention_forward(config, query, key, value, mask, **_kwargs):
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key_states = repeat_kv(key, config.num_key_value_groups)
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value_states = repeat_kv(value, config.num_key_value_groups)
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attn_weights = torch.matmul(query, key_states.transpose(2, 3)) / math.sqrt(config.head_dim)
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if mask is not None: # no matter the length, we just slice it
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causal_mask = mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=config.attention_dropout, training=config.training)
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attn_output = torch.matmul(attn_weights, value_states)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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def flash_attention_forward(config, query, key, value, mask, target_dtype=torch.float16, **_kwargs):
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key_states = repeat_kv(key, config.num_key_value_groups)
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value_states = repeat_kv(value, config.num_key_value_groups)
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dropout_rate = 0.0 if not config.training else config.attention_dropout
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in float16 just to be sure everything works as expected.
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input_dtype = query.dtype
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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elif hasattr(config.config, "_pre_quantization_dtype"):
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target_dtype = config.config._pre_quantization_dtype
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else:
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target_dtype = config.q_proj.weight.dtype
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logger.warning_once(
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f"The input hidden states seems to be silently casted in float32, this might be related to"
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query = query.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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# Reashape to the expected shape for Flash Attention
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query_states = query.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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position_ids = _kwargs["position_ids"]
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attn_output = _flash_attention_forward(
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query_states,
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key_states,
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value_states,
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mask,
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query_states.shape[1],
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position_ids=position_ids,
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dropout=dropout_rate,
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sliding_window=config.sliding_window,
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is_causal=config.is_causal,
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use_top_left_mask=config._flash_attn_uses_top_left_mask,
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)
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return attn_output, None
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def sdpa_attention_forward(config, query, key, value, mask, **_kwargs):
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key = repeat_kv(key, config.num_key_value_groups)
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value = repeat_kv(value, config.num_key_value_groups)
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q_len = query.shape[-2]
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causal_mask = mask
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if mask is not None:
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causal_mask = mask[:, :, :, : key.shape[-2]]
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# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
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# Reference: https://github.com/pytorch/pytorch/issues/112577.
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if query.device.type == "cuda" and mask is not None:
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query = query.contiguous()
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key = key.contiguous()
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value = value.contiguous()
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is_causal = True if causal_mask is None and q_len > 1 else False
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attn_output = torch.nn.functional.scaled_dot_product_attention(
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query,
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key,
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value,
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attn_mask=causal_mask,
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dropout_p=config.attention_dropout if config.training else 0.0,
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is_causal=is_causal,
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, None
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def flex_attention_forward(config, query, key, value, mask, output_attentions=False, **_kwargs):
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causal_mask = mask
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if causal_mask is not None:
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causal_mask = causal_mask[:, :, :, : key.shape[-2]]
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def causal_mod(score, b, h, q_idx, kv_idx):
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if causal_mask is not None:
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score += causal_mask[b][0][q_idx][kv_idx]
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return score
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attn_output, attn_weights = flex_attention(
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query,
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key,
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value,
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score_mod=causal_mod,
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enable_gqa=True,
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return_lse=True,
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)
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attn_weights = attn_weights.to(value.dtype)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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QWEN2_ATTENTION_FUNCTION = {
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"flash_attention_2": flash_attention_forward,
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"flex_attention": flex_attention_forward,
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"eager": eager_attention_forward,
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"sdpa": sdpa_attention_forward,
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}
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# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2Attention with Qwen2->Qwen2Moe
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class Qwen2MoeAttention(nn.Module):
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"""
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@ -346,6 +484,15 @@ class Qwen2MoeAttention(nn.Module):
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self.is_causal = True
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self.attention_dropout = config.attention_dropout
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if (
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self.config.use_sliding_window
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and getattr(self.config, "sliding_window", None) is not None
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and self.layer_idx >= self.config.max_window_layers
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):
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self.sliding_window = self.config.sliding_window
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else:
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self.sliding_window = None
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if (self.head_dim * self.num_heads) != self.hidden_size:
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raise ValueError(
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f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
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@ -358,94 +505,6 @@ class Qwen2MoeAttention(nn.Module):
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self.rotary_emb = Qwen2MoeRotaryEmbedding(config=self.config)
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# Ignore copy
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def forward(
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self,
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hidden_states: torch.Tensor,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_value: Optional[Cache] = None,
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output_attentions: bool = False,
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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key_states = self.k_proj(hidden_states)
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value_states = self.v_proj(hidden_states)
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query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
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key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
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value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
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if position_embeddings is None:
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logger.warning_once(
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"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
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"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
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"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
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"removed and `position_embeddings` will be mandatory."
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)
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cos, sin = self.rotary_emb(value_states, position_ids)
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else:
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
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if past_key_value is not None:
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
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if attention_mask is not None: # no matter the length, we just slice it
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causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
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attn_weights = attn_weights + causal_mask
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# upcast attention to fp32
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
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attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
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attn_output = torch.matmul(attn_weights, value_states)
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if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
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raise ValueError(
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f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
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f" {attn_output.size()}"
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)
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attn_output = attn_output.transpose(1, 2).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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attn_weights = None
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return attn_output, attn_weights, past_key_value
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# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2FlashAttention2 with Qwen2->Qwen2Moe
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class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
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"""
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Qwen2Moe flash attention module, following Qwen2Moe attention module. This module inherits from `Qwen2MoeAttention`
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as the weights of the module stays untouched. The only required change would be on the forward pass
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where it needs to correctly call the public API of flash attention and deal with padding tokens
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in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
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config.max_window_layers layers.
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"""
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# Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
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# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
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# Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
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self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
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def forward(
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@ -458,7 +517,7 @@ class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
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use_cache: bool = False,
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cache_position: Optional[torch.LongTensor] = None,
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
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):
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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bsz, q_len, _ = hidden_states.size()
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query_states = self.q_proj(hidden_states)
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@ -485,62 +544,23 @@ class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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# repeat k/v heads if n_kv_heads < n_heads
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key_states = repeat_kv(key_states, self.num_key_value_groups)
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value_states = repeat_kv(value_states, self.num_key_value_groups)
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dropout_rate = 0.0 if not self.training else self.attention_dropout
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# In PEFT, usually we cast the layer norms in float32 for training stability reasons
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# therefore the input hidden states gets silently casted in float32. Hence, we need
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# cast them back in float16 just to be sure everything works as expected.
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input_dtype = query_states.dtype
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if input_dtype == torch.float32:
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if torch.is_autocast_enabled():
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target_dtype = torch.get_autocast_gpu_dtype()
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# Handle the case where the model is quantized
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elif hasattr(self.config, "_pre_quantization_dtype"):
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target_dtype = self.config._pre_quantization_dtype
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else:
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target_dtype = self.q_proj.weight.dtype
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logger.warning_once(
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f"The input hidden states seems to be silently casted in float32, this might be related to"
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f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
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f" {target_dtype}."
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)
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query_states = query_states.to(target_dtype)
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key_states = key_states.to(target_dtype)
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value_states = value_states.to(target_dtype)
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# Reashape to the expected shape for Flash Attention
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query_states = query_states.transpose(1, 2)
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key_states = key_states.transpose(1, 2)
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value_states = value_states.transpose(1, 2)
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if (
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self.config.use_sliding_window
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and getattr(self.config, "sliding_window", None) is not None
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and self.layer_idx >= self.config.max_window_layers
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):
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sliding_window = self.config.sliding_window
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if output_attentions and self.config._attn_implementation in ["sdpa", "flash_attention_2"]:
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logger.warning_once("Setting `attention_type` to `flex_attention` because `output_attentions=True`")
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attention_type = "flex_attention"
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else:
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sliding_window = None
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attn_output = _flash_attention_forward(
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attention_type = self.config._attn_implementation
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attn_output, attn_weights = QWEN2_ATTENTION_FUNCTION[attention_type](
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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q_len,
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output_attentions=output_attentions,
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position_ids=position_ids,
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dropout=dropout_rate,
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sliding_window=sliding_window,
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is_causal=self.is_causal,
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use_top_left_mask=self._flash_attn_uses_top_left_mask,
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)
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
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attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
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attn_output = self.o_proj(attn_output)
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if not output_attentions:
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@ -549,108 +569,26 @@ class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
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return attn_output, attn_weights, past_key_value
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# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2SdpaAttention with Qwen2->Qwen2Moe
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class Qwen2MoeSdpaAttention(Qwen2MoeAttention):
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"""
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Qwen2Moe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
|
||||
`Qwen2MoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
|
||||
SDPA API.
|
||||
"""
|
||||
|
||||
# Adapted from Qwen2MoeAttention.forward
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_value: Optional[Cache] = None,
|
||||
output_attentions: bool = False,
|
||||
use_cache: bool = False,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # will become mandatory in v4.46
|
||||
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
|
||||
if output_attentions:
|
||||
# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
|
||||
logger.warning_once(
|
||||
"Qwen2MoeModel is using Qwen2MoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
|
||||
'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
|
||||
)
|
||||
return super().forward(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
bsz, q_len, _ = hidden_states.size()
|
||||
|
||||
query_states = self.q_proj(hidden_states)
|
||||
key_states = self.k_proj(hidden_states)
|
||||
value_states = self.v_proj(hidden_states)
|
||||
|
||||
query_states = query_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
|
||||
key_states = key_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
|
||||
value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)
|
||||
|
||||
if position_embeddings is None:
|
||||
logger.warning_once(
|
||||
"The attention layers in this model are transitioning from computing the RoPE embeddings internally "
|
||||
"through `position_ids` (2D tensor with the indexes of the tokens), to using externally computed "
|
||||
"`position_embeddings` (Tuple of tensors, containing cos and sin). In v4.46 `position_ids` will be "
|
||||
"removed and `position_embeddings` will be mandatory."
|
||||
)
|
||||
cos, sin = self.rotary_emb(value_states, position_ids)
|
||||
else:
|
||||
cos, sin = position_embeddings
|
||||
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
|
||||
|
||||
if past_key_value is not None:
|
||||
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} # Specific to RoPE models
|
||||
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
|
||||
|
||||
key_states = repeat_kv(key_states, self.num_key_value_groups)
|
||||
value_states = repeat_kv(value_states, self.num_key_value_groups)
|
||||
|
||||
causal_mask = attention_mask
|
||||
if attention_mask is not None: # no matter the length, we just slice it
|
||||
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
|
||||
|
||||
# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
|
||||
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||||
if query_states.device.type == "cuda" and attention_mask is not None:
|
||||
query_states = query_states.contiguous()
|
||||
key_states = key_states.contiguous()
|
||||
value_states = value_states.contiguous()
|
||||
|
||||
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
|
||||
# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
|
||||
# The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
|
||||
is_causal = True if causal_mask is None and q_len > 1 else False
|
||||
|
||||
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=is_causal,
|
||||
# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2FlashAttention2 with Qwen2->Qwen2Moe
|
||||
class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
|
||||
def __init__(self, config: Qwen2MoeConfig, layer_idx: Optional[int] = None):
|
||||
super().__init__(config, layer_idx)
|
||||
self.config._attn_implementation = "flash_attention_2"
|
||||
logger.warning_once(
|
||||
"The `Qwen2MoeFlashAttention2` class is deprecated in favor of simply modifying the `config._attn_implementation`"
|
||||
"attribute of the `Qwen2MoeAttention` class! It will be removed in v4.48"
|
||||
)
|
||||
|
||||
attn_output = attn_output.transpose(1, 2).contiguous()
|
||||
attn_output = attn_output.view(bsz, q_len, self.hidden_size)
|
||||
|
||||
attn_output = self.o_proj(attn_output)
|
||||
|
||||
return attn_output, None, past_key_value
|
||||
|
||||
|
||||
QWEN2MOE_ATTENTION_CLASSES = {
|
||||
"eager": Qwen2MoeAttention,
|
||||
"flash_attention_2": Qwen2MoeFlashAttention2,
|
||||
"sdpa": Qwen2MoeSdpaAttention,
|
||||
}
|
||||
# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2SdpaAttention with Qwen2->Qwen2Moe
|
||||
class Qwen2MoeSdpaAttention(Qwen2MoeAttention):
|
||||
def __init__(self, config: Qwen2MoeConfig, layer_idx: Optional[int] = None):
|
||||
super().__init__(config, layer_idx)
|
||||
self.config._attn_implementation = "sdpa"
|
||||
logger.warning_once(
|
||||
"The `Qwen2MoeSdpaAttention` class is deprecated in favor of simply modifying the `config._attn_implementation`"
|
||||
"attribute of the `Qwen2MoeAttention` class! It will be removed in v4.48"
|
||||
)
|
||||
|
||||
|
||||
class Qwen2MoeSparseMoeBlock(nn.Module):
|
||||
|
|
@ -719,8 +657,8 @@ class Qwen2MoeDecoderLayer(nn.Module):
|
|||
def __init__(self, config: Qwen2MoeConfig, layer_idx: int):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
|
||||
self.self_attn = QWEN2MOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
|
||||
self.config = config
|
||||
self.self_attn = Qwen2MoeAttention(config=config, layer_idx=layer_idx)
|
||||
|
||||
if (layer_idx not in config.mlp_only_layers) and (
|
||||
config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
|
||||
|
|
@ -843,6 +781,7 @@ class Qwen2MoePreTrainedModel(PreTrainedModel):
|
|||
_supports_flash_attn_2 = True
|
||||
_supports_sdpa = True
|
||||
_supports_cache_class = True
|
||||
_supports_flex_attn = True
|
||||
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
|
|
|
|||
Loading…
Reference in a new issue