[NPU]Custom fusion operator unification #8431
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| # Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| import os | ||
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| import paddle | ||
| import paddle.nn.functional as F | ||
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| try: | ||
| from paddle.incubate.nn.functional import fused_rotary_position_embedding | ||
| except ImportError: | ||
| fused_rotary_position_embedding = None | ||
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| try: | ||
| from paddle.incubate.nn.functional import swiglu | ||
| except ImportError: | ||
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| def swiglu(x, y=None): | ||
| if y is None: | ||
| x, y = paddle.chunk(x, chunks=2, axis=-1) | ||
| return F.silu(x) * y | ||
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| from paddle.utils import try_import | ||
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| from paddlenlp.utils.tools import get_env_device | ||
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| try: | ||
| from paddle.incubate.nn.functional import fused_rotary_position_embedding | ||
| except ImportError: | ||
| fused_rotary_position_embedding = None | ||
| try: | ||
| if get_env_device() == "npu": | ||
| from paddle.base import core | ||
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| for lib in os.listdir(os.getenv("CUSTOM_DEVICE_ROOT")): | ||
| if lib.endswith(".so"): | ||
| paddle.utils.cpp_extension.extension_utils.load_op_meta_info_and_register_op(lib) | ||
| from paddle.nn.functional.flash_attention import flash_attention | ||
| except: | ||
| flash_attention = None | ||
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| def fusion_rope(query_states, key_states, value_states, hidden_states, position_ids, past_key_value, rotary_emb): | ||
| assert past_key_value is None, "fuse rotary not support cache kv for now" | ||
| batch_size, seq_length, num_heads, head_dim = query_states.shape | ||
| _, kv_seq_len, num_key_value_heads, _ = key_states.shape | ||
| cos, sin = rotary_emb(value_states, seq_len=kv_seq_len) | ||
| if get_env_device() == "npu": | ||
| query_states = core.eager._run_custom_op("fused_rope", query_states, cos, sin)[0] | ||
| key_states = core.eager._run_custom_op("fused_rope", key_states, cos, sin)[0] | ||
| else: | ||
| # paddle version > 2.6 or develop support q and k/v with different num_heads | ||
| paddle_version = float(paddle.__version__[:3]) | ||
| if ((paddle_version != 0.0) and (paddle_version <= 2.6)) and (num_heads != num_key_value_heads): | ||
| query_states, _, _ = fused_rotary_position_embedding( | ||
| query_states, | ||
| None, | ||
| None, | ||
| sin=sin, | ||
| cos=cos, | ||
| position_ids=position_ids, | ||
| use_neox_rotary_style=False, | ||
| ) | ||
| key_states, _, _ = fused_rotary_position_embedding( | ||
| key_states, | ||
| None, | ||
| None, | ||
| sin=sin, | ||
| cos=cos, | ||
| position_ids=position_ids, | ||
| use_neox_rotary_style=False, | ||
| ) | ||
| else: | ||
| query_states, key_states, _ = fused_rotary_position_embedding( | ||
| query_states, | ||
| key_states, | ||
| v=None, | ||
| sin=sin, | ||
| cos=cos, | ||
| position_ids=position_ids, | ||
| use_neox_rotary_style=False, | ||
| ) | ||
| return query_states, key_states | ||
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| def rms_norm_fused(x_in, w, eps): | ||
| fused_ln = try_import("fused_ln") | ||
| return fused_ln.fused_rms_norm(x_in, w, eps)[0] | ||
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| def fusion_rms_norm(hidden_states, weight, variance_epsilon): | ||
| if get_env_device() == "npu": | ||
| return core.eager._run_custom_op("rms_norm_npu", hidden_states, weight, variance_epsilon)[0] | ||
| elif get_env_device() == "xpu": | ||
| try: | ||
| import paddle_xpu_nn # noqa: F821 | ||
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| return paddle_xpu_nn.xpu_rms_norm(hidden_states, weight, variance_epsilon)[0] | ||
| except ImportError: | ||
| raise NotImplementedError( | ||
| f"Implementation of fused_rms_norm is not available on {get_env_device()}. Please install paddle_xpu to use this feature" | ||
| ) | ||
| return rms_norm_fused(hidden_states, weight, variance_epsilon) | ||
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| def fusion_flash_attention( | ||
| query_states, | ||
| config, | ||
| key_states, | ||
| value_states, | ||
| attention_mask, | ||
| output_attentions, | ||
| alibi=None, | ||
| sequence_parallel=False, | ||
| reshard_layer=None, | ||
| npu_is_casual=False, | ||
| ): | ||
| bsz, q_len, num_heads, head_dim = query_states.shape | ||
| _, kv_seq_len, _, _ = value_states.shape | ||
| version = paddle.version.full_version | ||
| if version != "0.0.0" and version <= "2.5.2": | ||
| if alibi is not None: | ||
| raise ValueError("Flash Attention doesn't support alibi") | ||
| attn_output, attn_weights = flash_attention( | ||
| query_states, | ||
| key_states, | ||
| value_states, | ||
| causal=True, | ||
| return_softmax=output_attentions, | ||
| ) | ||
| else: | ||
| if alibi is not None: | ||
| alibi = alibi.reshape([bsz, num_heads, 1, -1]) | ||
| attention_mask = attention_mask.cast(alibi.dtype) + alibi | ||
| if get_env_device() == "npu": | ||
| attn_output = core.eager._run_custom_op( | ||
| "flash_attention_npu", | ||
| query_states, | ||
| key_states, | ||
| value_states, | ||
| None, | ||
| attention_mask, | ||
| 0.0, | ||
| attention_mask is None, | ||
| True, | ||
| False, | ||
| npu_is_casual, | ||
| )[0] | ||
| else: | ||
| attn_output = F.scaled_dot_product_attention( | ||
| query_states, | ||
| key_states, | ||
| value_states, | ||
| attn_mask=attention_mask, | ||
| is_causal=attention_mask is None, | ||
| ) | ||
| attn_weights = None | ||
|
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| if reshard_layer is not None: | ||
| # attn_output shape: [bs, seqlen, num_head/sep, head_dim] | ||
| attn_output = reshard_layer( | ||
| attn_output, | ||
| split_axis=1, | ||
| concat_axis=2, | ||
| ) | ||
| # attn_output shape: [bs, seqlen/sep, num_head, head_dim] | ||
| assert ( | ||
| config.sep_parallel_degree > 1 and q_len % config.sep_parallel_degree == 0 | ||
| ), f"q_len:{q_len}, config.sep_parallel_degree:{config.sep_parallel_degree}" | ||
| q_len = q_len // config.sep_parallel_degree | ||
| num_heads = num_heads * config.sep_parallel_degree | ||
|
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| if sequence_parallel: | ||
| attn_output = attn_output.reshape([bsz * q_len, head_dim * num_heads]) | ||
| else: | ||
| attn_output = attn_output.reshape([bsz, q_len, head_dim * num_heads]) | ||
| return (attn_output, attn_weights) if output_attentions else attn_output | ||
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注意看是不是有不需要的代码，注意删除掉。