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[Train] Support custom attention with context parallel #910
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213 changes: 213 additions & 0 deletions
213
flagscale/backends/Megatron-LM/megatron/core/transformer/flex_attention.py.patch
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,213 @@ | ||
| diff --git a/megatron/core/transformer/flex_attention.py b/megatron/core/transformer/flex_attention.py | ||
| new file mode 100644 | ||
| index 000000000..7f51015b9 | ||
| --- /dev/null | ||
| +++ b/megatron/core/transformer/flex_attention.py | ||
| @@ -0,0 +1,207 @@ | ||
| +# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved. | ||
| + | ||
| +from abc import ABC, abstractmethod | ||
| +from dataclasses import dataclass | ||
| +from typing import NoReturn, Optional, Tuple, Union | ||
| + | ||
| +import torch | ||
| +from torch import Tensor | ||
| + | ||
| +from megatron.core import tensor_parallel | ||
| +from megatron.core.inference.contexts import BaseInferenceContext | ||
| +from megatron.core.models.common.embeddings.rope_utils import ( | ||
| + apply_rotary_pos_emb, | ||
| + apply_rotary_pos_emb_with_cos_sin, | ||
| +) | ||
| +from megatron.core.packed_seq_params import PackedSeqParams | ||
| +from megatron.core.parallel_state import ( | ||
| + get_context_parallel_group, | ||
| +) | ||
| +from megatron.core.utils import ( | ||
| + deprecate_inference_params, | ||
| + divide, | ||
| + get_pg_size, | ||
| + is_fa_min_version, | ||
| + is_te_min_version, | ||
| + nvtx_range_pop, | ||
| + nvtx_range_push, | ||
| +) | ||
| +from .enums import AttnMaskType | ||
| +from .transformer_config import TransformerConfig | ||
| +from megatron.core.process_groups_config import ProcessGroupCollection | ||
| +from megatron.core.transformer.attention import SelfAttention | ||
| +from megatron.core.transformer.attention import SelfAttentionSubmodules | ||
| + | ||
| +from megatron.core.transformer.ring_attention import ring_attn | ||
| + | ||
| + | ||
| +class FlexAttention(SelfAttention): | ||
| + def __init__( | ||
| + self, | ||
| + config: TransformerConfig, | ||
| + submodules: SelfAttentionSubmodules, | ||
| + layer_number: int, | ||
| + attn_mask_type=AttnMaskType.padding, | ||
| + cp_comm_type: str = None, | ||
| + pg_collection: ProcessGroupCollection = None, | ||
| + ): | ||
| + super().__init__( | ||
| + config=config, | ||
| + submodules=submodules, | ||
| + layer_number=layer_number, | ||
| + attn_mask_type=attn_mask_type, | ||
| + cp_comm_type=cp_comm_type, | ||
| + pg_collection=pg_collection, | ||
| + ) | ||
| + | ||
| + self.config = config | ||
| + | ||
| + def forward( | ||
| + self, | ||
| + hidden_states: Tensor, | ||
| + attention_mask: Tensor, | ||
| + key_value_states: Optional[Tensor] = None, | ||
| + inference_context: Optional[BaseInferenceContext] = None, | ||
| + rotary_pos_emb: Optional[Union[Tensor, Tuple[Tensor, Tensor]]] = None, | ||
| + rotary_pos_cos: Optional[Tensor] = None, | ||
| + rotary_pos_sin: Optional[Tensor] = None, | ||
| + attention_bias: Optional[Tensor] = None, | ||
| + packed_seq_params: Optional[PackedSeqParams] = None, | ||
| + sequence_len_offset: Optional[int] = None, | ||
| + *, | ||
| + inference_params: Optional[BaseInferenceContext] = None, | ||
| + ) -> Tuple[Tensor, Tensor]: | ||
| + """ | ||
| + Perform a forward pass through the attention module. | ||
| + | ||
| + Args: | ||
| + hidden_states (Tensor): Hidden states. | ||
| + attention_mask (Tensor): Attention mask. | ||
| + key_value_states (Optional[Tensor]): Key/value states (for cross attention). | ||
| + inference_context (Optional[BaseInferenceContext]): Inference context that manages | ||
| + KV cache. | ||
| + rotary_pos_emb (Optional[Union[Tensor, Tuple[Tensor, Tensor]]]): Rotary | ||
| + embedding tensor(s). | ||
| + rotary_pos_cos (Optional[Tensor]): Rotary embedding cosine. | ||
| + rotary_pos_sin (Optional[Tensor]): Rotary embedding sine. | ||
| + attention_bias (Optional[Tensor]): Attention bias. | ||
| + packed_seq_params (Optional[PackedSeqparams]): Parameters used for THD format. | ||
| + sequence_len_offset (Optional[int]): Sequence length offset used for | ||
| + inference CUDA graphs. | ||
| + | ||
| + Return: | ||
| + (Tuple[Tensor, Tensor]) Attention output and bias. | ||
| + | ||
| + """ | ||
| + # Check if we need to skip RoPE | ||
| + # no_rope is 0-indexed array and self.layer_number is 1-indexed | ||
| + assert ( | ||
| + attention_mask is not None | ||
| + ), "Flex attention is used for customed attention mask, which must be provided" | ||
| + | ||
| + no_rope = ( | ||
| + self.config.no_rope_freq[self.layer_number - 1] if self.config.no_rope_freq else False | ||
| + ) | ||
| + if no_rope: | ||
| + rotary_pos_emb = None | ||
| + | ||
| + inference_context = deprecate_inference_params(inference_context, inference_params) | ||
| + | ||
| + if inference_context and inference_context.is_dynamic_batching(): | ||
| + assert HAVE_FA3 or is_fa_min_version( | ||
| + "2.7.3" | ||
| + ), "flash attn verion v2.7.3 and above is required for dynamic batching." | ||
| + | ||
| + # hidden_states: [sq, b, h] | ||
| + if self.config.flash_decode and not self.training and inference_context is not None: | ||
| + rotary_pos_emb = None | ||
| + else: | ||
| + assert rotary_pos_cos is None and rotary_pos_sin is None | ||
| + | ||
| + # For self attention we just duplicate the rotary_pos_emb if it isn't already | ||
| + if rotary_pos_emb is not None and not isinstance(rotary_pos_emb, tuple): | ||
| + rotary_pos_emb = (rotary_pos_emb,) * 2 | ||
| + | ||
| + # ===================== | ||
| + # Query, Key, and Value | ||
| + # ===================== | ||
| + # Get the query, key and value tensors based on the type of attention - | ||
| + # self or cross attn. | ||
| + nvtx_range_push(suffix="qkv") | ||
| + query, key, value = self.get_query_key_value_tensors(hidden_states, key_value_states) | ||
| + nvtx_range_pop(suffix="qkv") | ||
| + | ||
| + | ||
| + # ================================================ | ||
| + # relative positional embedding (rotary embedding) | ||
| + # ================================================ | ||
| + nvtx_range_push(suffix="rotary_pos_emb") | ||
| + if rotary_pos_emb is not None and not self.config.flash_decode: | ||
| + q_pos_emb, k_pos_emb = rotary_pos_emb | ||
| + | ||
| + if packed_seq_params is not None: | ||
| + if packed_seq_params.cu_seqlens_q_padded is not None: | ||
| + cu_seqlens_q = packed_seq_params.cu_seqlens_q_padded | ||
| + else: | ||
| + cu_seqlens_q = packed_seq_params.cu_seqlens_q | ||
| + if packed_seq_params.cu_seqlens_kv_padded is not None: | ||
| + cu_seqlens_kv = packed_seq_params.cu_seqlens_kv_padded | ||
| + else: | ||
| + cu_seqlens_kv = packed_seq_params.cu_seqlens_kv | ||
| + else: | ||
| + cu_seqlens_q = cu_seqlens_kv = None | ||
| + | ||
| + if q_pos_emb is not None: | ||
| + # TODO VIJAY: simplify | ||
| + if inference_context is None or inference_context.is_static_batching(): | ||
| + query = apply_rotary_pos_emb( | ||
| + query, | ||
| + q_pos_emb, | ||
| + config=self.config, | ||
| + cu_seqlens=cu_seqlens_q, | ||
| + cp_group=self.pg_collection.cp, | ||
| + ) | ||
| + else: | ||
| + query = inference_context.apply_rotary_emb_query( | ||
| + query, q_pos_emb, self.config, cu_seqlens_q, self.pg_collection.cp | ||
| + ) | ||
| + if k_pos_emb is not None: | ||
| + key = apply_rotary_pos_emb( | ||
| + key, | ||
| + k_pos_emb, | ||
| + config=self.config, | ||
| + cu_seqlens=cu_seqlens_kv, | ||
| + cp_group=self.pg_collection.cp, | ||
| + ) | ||
| + | ||
| + # TODO, can apply positional embedding to value_layer so it has | ||
| + # absolute positional embedding. | ||
| + # otherwise, only relative positional embedding takes effect | ||
| + # value_layer = apply_rotary_pos_emb(value_layer, k_pos_emb) | ||
| + nvtx_range_pop(suffix="rotary_pos_emb") | ||
| + | ||
| + # ================================== | ||
| + # flex attention computation | ||
| + # ================================== | ||
| + query = query.permute( | ||
| + 1, 2, 0, 3 | ||
| + ).contiguous() # [seq_len, batch_size, num_heads/tp, head_dim] -> [batch_size, num_heads/tp, seq_len, head_dim] | ||
| + key = key.permute(1, 2, 0, 3).contiguous() | ||
| + value = value.permute(1, 2, 0, 3).contiguous() | ||
| + attention_mask = attention_mask.contiguous() | ||
| + core_attn_out, _ = ring_attn( | ||
| + query, key, value, attention_mask, group=get_context_parallel_group(), | ||
| + ) | ||
| + core_attn_out = core_attn_out.permute( | ||
| + 2, 0, 1, 3 | ||
| + ).contiguous() | ||
| + core_attn_out = core_attn_out.view(core_attn_out.shape[0], core_attn_out.shape[1], -1) | ||
| + | ||
| + | ||
| + # ================= | ||
| + # Output. [sq, b, h] | ||
| + # ================= | ||
| + | ||
| + output, bias = self.linear_proj(core_attn_out) | ||
| + | ||
| + return output, bias | ||
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The variable
HAVE_FA3is used here but it's not defined or imported, which will lead to aNameErrorat runtime. It seems you intended to check for the availability of flash-attention v3.You should define
HAVE_FA3at the top of this file, similar to how it's done inmegatron/core/transformer/attention.py, by adding the followingtry-exceptblock: