[XPU] llama add xpu support #8282
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,49 @@ | ||
| # 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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| from paddle.distributed.fleet.meta_parallel import ( | ||
| ColumnParallelLinear, | ||
| RowParallelLinear, | ||
| ) | ||
| from paddle.distributed.fleet.utils.sequence_parallel_utils import ( | ||
| ColumnSequenceParallelLinear, | ||
| RowSequenceParallelLinear, | ||
| ) | ||
| from paddle.nn import Linear | ||
|
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| from paddlenlp.utils.tools import get_env_device | ||
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| from .mc2_parallel_linear import MC2ColumnSeqParallelLinear, MC2RowSeqParallelLinear | ||
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| if MC2ColumnSeqParallelLinear is not None and MC2RowSeqParallelLinear is not None: | ||
| ColumnSequenceParallelLinear = MC2ColumnSeqParallelLinear # noqa: F811 | ||
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| RowSequenceParallelLinear = MC2RowSeqParallelLinear # noqa: F811 | ||
| elif get_env_device() == "xpu": | ||
| try: | ||
| from paddle_xpu.layers.nn import ColumnParallelLinear as XPUColumnParallelLinear | ||
| from paddle_xpu.layers.nn import Linear as XPULinear # noqa: F401 | ||
| from paddle_xpu.layers.nn import RowParallelLinear as XPURowParallelLinear | ||
| from paddle_xpu.layers.nn.sequence_parallel import ( # noqa: F401 | ||
| XPUColumnSequenceParallelLinear, | ||
| XPURowSequenceParallelLinear, | ||
| ) | ||
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| Linear = XPULinear # noqa: F811 | ||
| ColumnParallelLinear = XPUColumnParallelLinear # noqa: F811 | ||
| RowParallelLinear = XPURowParallelLinear # noqa: F811 | ||
| ColumnSequenceParallelLinear = XPUColumnSequenceParallelLinear # noqa: F811 | ||
| RowSequenceParallelLinear = XPURowSequenceParallelLinear # noqa: F811 | ||
| except ImportError: | ||
| # It's OK, just use paddle's Linear layers | ||
| pass | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -62,10 +62,6 @@ | |
| init_name_mappings, | ||
| ) | ||
| from paddlenlp.transformers.long_sequence_strategies import LongSequenceStrategies | ||
| from paddlenlp.transformers.model_outputs import ( | ||
| BaseModelOutputWithPastAndCrossAttentions, | ||
| CausalLMOutputWithCrossAttentions, | ||
|
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@@ -74,6 +70,8 @@ | |
| from paddlenlp.utils.log import logger | ||
| from paddlenlp.utils.tools import get_env_device | ||
|
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||
| from .. import linear_utils | ||
| from ..linear_utils import Linear | ||
| from ..segment_parallel_utils import ReshardLayer | ||
| from .configuration import ( | ||
| LLAMA_PRETRAINED_INIT_CONFIGURATION, | ||
|
|
@@ -410,6 +408,15 @@ | |
| if self.config.use_fused_rms_norm: | ||
| if get_env_device() == "npu": | ||
| return core.eager._run_custom_op("rms_norm_npu", hidden_states, self.weight, self.variance_epsilon)[0] | ||
| elif get_env_device() == "xpu": | ||
| try: | ||
| import paddle_xpu_nn # noqa: F821 | ||
|
|
||
| return paddle_xpu_nn.xpu_rms_norm(hidden_states, self.weight, self.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, self.weight, self.variance_epsilon) | ||
|
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||
| if paddle.in_dynamic_mode(): | ||
|
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@@ -571,15 +578,11 @@ | |
| self.fuse_attention_ffn = config.fuse_attention_ffn | ||
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| if config.sequence_parallel: | ||
| ColumnParallelLinear = linear_utils.ColumnSequenceParallelLinear | ||
| RowParallelLinear = linear_utils.RowSequenceParallelLinear | ||
| else: | ||
| ColumnParallelLinear = linear_utils.ColumnParallelLinear | ||
| RowParallelLinear = linear_utils.RowParallelLinear | ||
|
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||
| if config.tensor_parallel_degree > 1: | ||
| if config.fuse_attention_ffn: | ||
|
|
@@ -611,15 +614,29 @@ | |
| ) | ||
| else: | ||
| if config.fuse_attention_ffn: | ||
| self.gate_up_fused_proj = Linear(self.hidden_size, self.intermediate_size * 2, bias_attr=False) | ||
| else: | ||
| self.gate_proj = Linear(self.hidden_size, self.intermediate_size, bias_attr=False) | ||
| self.up_proj = Linear(self.hidden_size, self.intermediate_size, bias_attr=False) | ||
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| self.down_proj = Linear(self.intermediate_size, self.hidden_size, bias_attr=False) | ||
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| def forward(self, x): | ||
| if self.fuse_attention_ffn: | ||
| # FIXME(yangjianbang): use paddle's native swiglu | ||
| if get_env_device() == "xpu": | ||
| try: | ||
| import paddle_xpu_nn # noqa: F821 | ||
|
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||
| out = self.gate_up_fused_proj(x) | ||
| out = paddle_xpu_nn.xpu_swiglu(out, axis=-1, turn=True) | ||
| out = self.down_proj(out) | ||
| return out | ||
| except ImportError: | ||
| gate_out, up_out = paddle.chunk(self.gate_up_fused_proj(x), chunks=2, axis=-1) | ||
| out = self.down_proj(F.silu(gate_out) * up_out) | ||
| return out | ||
|
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||
| x = swiglu(self.gate_up_fused_proj(x)) | ||
| else: | ||
| x = swiglu(self.gate_proj(x), self.up_proj(x)) | ||
|
|
@@ -680,7 +697,7 @@ | |
| ) | ||
|
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| self.use_fused_rope = config.use_fused_rope | ||
| if self.use_fused_rope and get_env_device() not in ["npu", "xpu"]: | ||
| if "gpu" not in paddle.device.get_device() or fused_rotary_position_embedding is None: | ||
| warnings.warn( | ||
| "Enable fuse rope in the config, but fuse rope is not available. " | ||
|
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@@ -689,15 +706,11 @@ | |
| self.use_fused_rope = False | ||
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| if config.sequence_parallel: | ||
| ColumnParallelLinear = linear_utils.ColumnSequenceParallelLinear | ||
| RowParallelLinear = linear_utils.RowSequenceParallelLinear | ||
| else: | ||
| ColumnParallelLinear = linear_utils.ColumnParallelLinear | ||
| RowParallelLinear = linear_utils.RowParallelLinear | ||
|
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| if config.tensor_parallel_degree > 1: | ||
| if self.fuse_attention_qkv: | ||
|
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@@ -728,36 +741,36 @@ | |
| gather_output=False, | ||
| ) | ||
| else: | ||
| self.k_proj = Linear( | ||
| self.hidden_size, | ||
| self.config.num_key_value_heads * self.head_dim, | ||
| bias_attr=False, | ||
| ) | ||
| self.v_proj = Linear( | ||
| self.hidden_size, | ||
| self.config.num_key_value_heads * self.head_dim, | ||
| bias_attr=False, | ||
| ) | ||
|
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||
| else: | ||
| if self.fuse_attention_qkv: | ||
| self.qkv_proj = Linear( | ||
| self.hidden_size, | ||
| self.hidden_size + 2 * self.config.num_key_value_heads * self.head_dim, | ||
| bias_attr=False, | ||
| ) | ||
| else: | ||
| self.q_proj = Linear( | ||
| self.hidden_size, | ||
| self.hidden_size, | ||
| bias_attr=False, | ||
| ) | ||
| self.k_proj = Linear( | ||
| self.hidden_size, | ||
| self.config.num_key_value_heads * self.head_dim, | ||
| bias_attr=False, | ||
| ) | ||
| self.v_proj = Linear( | ||
| self.hidden_size, | ||
| self.config.num_key_value_heads * self.head_dim, | ||
| bias_attr=False, | ||
|
|
@@ -771,7 +784,7 @@ | |
| input_is_parallel=True, | ||
| ) | ||
| else: | ||
| self.o_proj = Linear( | ||
| self.hidden_size, | ||
| self.hidden_size, | ||
| bias_attr=False, | ||
|
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@@ -1419,6 +1432,11 @@ | |
| y = paddle.to_tensor(paddle.finfo(dtype).min, dtype="float16") | ||
| expanded_attn_mask = expanded_attn_mask.astype("float16") | ||
| expanded_attn_mask = paddle.where(expanded_attn_mask, x, y).astype(dtype) | ||
| elif get_env_device() == "xpu": | ||
| x = paddle.to_tensor(0.0, dtype=dtype) | ||
| y = paddle.to_tensor(paddle.finfo(dtype).min, dtype=dtype) | ||
| expanded_attn_mask = expanded_attn_mask.astype(dtype) | ||
| expanded_attn_mask = paddle.where(expanded_attn_mask, x, y).astype(dtype) | ||
|
There was a problem hiding this comment. 当传入的 There was a problem hiding this comment. 理论上是可以复用的，但是npu里面写死了dtype是 There was a problem hiding this comment. 根据 @wuhuachaocoding 意见，还是分成if elif两个单独的分支 |
||
| else: | ||
| expanded_attn_mask = paddle.where(expanded_attn_mask, 0.0, paddle.finfo(dtype).min).astype(dtype) | ||
| return expanded_attn_mask | ||
|
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@@ -1698,6 +1716,15 @@ | |
| self.weight.is_distributed = True if (vocab_size != config.vocab_size) else False | ||
| if self.weight.is_distributed: | ||
| self.weight.split_axis = 1 | ||
| if get_env_device() == "xpu": | ||
| try: | ||
| from paddle_xpu.layers.nn import ( # noqa: F401 | ||
| parallel_matmul as xpu_parallel_matmul, | ||
| ) | ||
|
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| self.xpu_parallel_matmul = xpu_parallel_matmul() | ||
| except ImportError: | ||
| self.xpu_parallel_matmul = None | ||
|
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| def forward(self, hidden_states, tensor_parallel_output=None): | ||
| if self.config.sequence_parallel: | ||
|
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@@ -1711,7 +1738,12 @@ | |
| if tensor_parallel_output is None: | ||
| tensor_parallel_output = self.config.tensor_parallel_output | ||
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| if get_env_device() == "xpu" and self.xpu_parallel_matmul is not None: | ||
| logits = self.xpu_parallel_matmul( | ||
| hidden_states, self.weight, tensor_parallel_output=tensor_parallel_output, training=self.training | ||
|
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There was a problem hiding this comment. training 参数必须要吗？如果参数能一样的话，是不是 把 parallel_matmul 的实现在xpu下替换就好了？ There was a problem hiding this comment. 这里面有两个原因：
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| ) | ||
| else: | ||
| logits = parallel_matmul(hidden_states, self.weight, tensor_parallel_output=tensor_parallel_output) | ||
| return logits | ||
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这个是做什么的？
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XPU针对accumulate_steps > 1的场景进行优化，配合下面的paddle_xpu里面的Linear层进行使用