mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-06-19 15:52:08 +00:00
feat(server): add flash attention llama
This commit is contained in:
OlivierDehaene
parent
71402ed4c7
commit
cd5d0a96ba
@ -19,7 +19,7 @@ from text_generation_server.models.t5 import T5Sharded
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try:
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from text_generation_server.models.flash_neox import FlashNeoX, FlashNeoXSharded
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from text_generation_server.models.flash_santacoder import FlashSantacoder
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from text_generation_server.models.flash_llama import FlashLlama
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from text_generation_server.models.flash_llama import FlashLlama, FlashLlamaSharded
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FLASH_ATTENTION = (
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torch.cuda.is_available() and int(os.environ.get("FLASH_ATTENTION", 0)) == 1
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@ -95,7 +95,11 @@ def get_model(
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if model_type == "llama":
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if sharded:
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raise NotImplementedError
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if FLASH_ATTENTION:
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return FlashLlamaSharded(model_id, revision, quantize=quantize)
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raise NotImplementedError(
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"sharded is not supported for llama when FLASH_ATTENTION=0"
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)
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else:
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llama_cls = FlashLlama if FLASH_ATTENTION else CausalLM
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return llama_cls(model_id, revision, quantize=quantize)
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@ -1,3 +1,23 @@
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# coding=utf-8
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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import torch.distributed
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@ -23,15 +43,45 @@ class LlamaRMSNorm(nn.Module):
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self.weight = nn.Parameter(torch.ones(hidden_size))
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self.variance_epsilon = eps
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def forward(self, hidden_states):
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
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def forward(self, hidden_states, residual=None):
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if hidden_states.shape[-1] > 6144:
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if residual is not None:
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hidden_states += residual
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residual = hidden_states
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# convert into half-precision if necessary
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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hidden_states = hidden_states.to(self.weight.dtype)
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variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
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hidden_states = hidden_states * torch.rsqrt(
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variance + self.variance_epsilon
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)
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return self.weight * hidden_states
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# convert into half-precision if necessary
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if self.weight.dtype in [torch.float16, torch.bfloat16]:
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hidden_states = hidden_states.to(self.weight.dtype)
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return self.weight * hidden_states, residual
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else:
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# faster post attention rms norm
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normed_hidden_states, res, *rest = dropout_layer_norm.dropout_add_ln_fwd(
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hidden_states,
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residual,
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self.weight,
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None,
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None,
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None,
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None,
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None,
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0.0,
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self.variance_epsilon,
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1.0,
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0,
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None,
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False,
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True, # Activate RMSNorm
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)
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if res is None:
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res = hidden_states
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return normed_hidden_states, res
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class FastLinear(nn.Linear):
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@ -183,8 +233,6 @@ class PositionRotaryEmbedding(RotaryEmbedding):
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):
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self._seq_len_cached = seqlen
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t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
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# Don't do einsum, it converts fp32 to fp16
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# freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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freqs = torch.outer(t, self.inv_freq.to(device=t.device))
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self._cos_cached = torch.cos(freqs).to(dtype)
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self._sin_cached = torch.sin(freqs).to(dtype)
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@ -245,16 +293,6 @@ class FlashLlamaAttention(torch.nn.Module):
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process_group=process_group,
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)
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def shuffle_qkv_dims(self):
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"""Swap dims to avoid an additional permute"""
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self.query_key_value.weight = torch.nn.Parameter(
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self.query_key_value.weight.view(
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self.num_heads, 3, self.head_size, self.hidden_size
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)
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.permute(1, 0, 2, 3)
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.reshape(-1, self.hidden_size)
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)
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def forward(
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self,
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hidden_states,
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@ -333,7 +371,6 @@ class LlamaMLP(nn.Module):
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if "gelu" not in act
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else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
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)
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self.intermediate_size = intermediate_size
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if process_group is None:
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# Fuse gate and up proj
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@ -341,6 +378,7 @@ class LlamaMLP(nn.Module):
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hidden_size, 2 * intermediate_size, bias=False
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)
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self.down_proj = FastLinear(intermediate_size, hidden_size, bias=False)
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self.intermediate_size = intermediate_size
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else:
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# Fuse gate and up proj
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self.gate_up_proj = TensorParallelColumnLinear(
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@ -356,6 +394,8 @@ class LlamaMLP(nn.Module):
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process_group=process_group,
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reduce=True,
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)
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self.intermediate_size = self.down_proj.in_features
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self.process_group = process_group
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def forward(self, hidden_states):
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@ -394,27 +434,11 @@ class FlashLlamaLayer(nn.Module):
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layer_past_present_indices,
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cu_seqlens_q,
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):
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# faster input rms norm
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hidden_states, residual, *rest = dropout_layer_norm.dropout_add_ln_fwd(
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hidden_states,
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residual,
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self.input_layernorm.weight,
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None,
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None,
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None,
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None,
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None,
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0.0,
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self.input_layernorm.variance_epsilon,
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1.0,
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0,
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None,
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False,
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True,
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)
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normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
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hidden_states = self.self_attn(
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hidden_states,
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# Self Attention
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attn_output = self.self_attn(
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normed_hidden_states,
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cos,
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sin,
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cu_seqlens,
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@ -425,27 +449,13 @@ class FlashLlamaLayer(nn.Module):
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)
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# faster post attention rms norm
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hidden_states, residual, *rest = dropout_layer_norm.dropout_add_ln_fwd(
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hidden_states,
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residual,
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self.post_attention_layernorm.weight,
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None,
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None,
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None,
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None,
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None,
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0.0,
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self.post_attention_layernorm.variance_epsilon,
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1.0,
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0,
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None,
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False,
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True,
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normed_attn_res_output, attn_res = self.post_attention_layernorm(
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attn_output, res
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)
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mlp_output = self.mlp(hidden_states)
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mlp_output = self.mlp(normed_attn_res_output)
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return mlp_output, residual
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return mlp_output, attn_res
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class FlashLlamaModel(torch.nn.Module):
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@ -492,7 +502,6 @@ class FlashLlamaModel(torch.nn.Module):
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self.embed_tokens.add_null_idx()
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for layer in self.layers:
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layer: FlashLlamaLayer
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layer.self_attn.shuffle_qkv_dims()
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layer.self_attn.query_key_value.transpose_weight()
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layer.self_attn.o_proj.transpose_weight()
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layer.mlp.gate_up_proj.transpose_weight()
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@ -550,24 +559,7 @@ class FlashLlamaModel(torch.nn.Module):
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cu_seqlens_q,
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)
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# Faster final layer norm
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hidden_states, *rest = dropout_layer_norm.dropout_add_ln_fwd(
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hidden_states,
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residual,
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self.norm.weight,
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None,
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None,
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None,
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None,
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None,
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0.0,
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self.norm.variance_epsilon,
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1.0,
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0,
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None,
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False,
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True,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states, past_key_values
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@ -32,10 +32,10 @@ class FlashLlama(FlashCausalLM):
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device = torch.device("cuda")
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dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
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else:
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raise NotImplementedError("FlashCausalLM is only available on GPU")
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raise NotImplementedError("FlashLlama is only available on GPU")
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if quantize:
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raise NotImplementedError("FlashCausalLM does not support quantization")
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raise NotImplementedError("FlashLlama does not support quantization")
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tokenizer = AutoTokenizer.from_pretrained(
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model_id, revision=revision, padding_side="left"
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@ -45,6 +45,7 @@ class FlashLlama(FlashCausalLM):
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model_id, revision=revision, tp_parallel=True
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)
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# We do not use from_pretrained as we modified the model internal module layout
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try:
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filenames = weight_files(model_id, revision, ".bin")
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# Local files not found
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@ -71,73 +72,65 @@ class FlashLlama(FlashCausalLM):
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model,
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filenames: List[Path],
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):
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final_state_dict = {}
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for filename in filenames:
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state_dict = torch.load(filename, map_location="cpu")
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for key, value in state_dict.items():
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layer_name = ".".join(key.split(".")[:4])
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if "q_proj" in key:
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# Fused qkv
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if "q_proj" in key or "k_proj" in key or "v_proj" in key:
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final_key = layer_name + ".query_key_value.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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final_state_dict[final_key][: value.shape[0]] = value
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elif "k_proj" in key:
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final_key = layer_name + ".query_key_value.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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final_state_dict[final_key][
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value.shape[0] : value.shape[0] * 2
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] = value
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elif "v_proj" in key:
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final_key = layer_name + ".query_key_value.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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final_state_dict[final_key][value.shape[0] * 2 :] = value
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elif "gate_proj" in key:
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# Fused gate and up projs
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elif "gate_proj" in key or "up_proj" in key:
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final_key = layer_name + ".gate_up_proj.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 2, value.shape[1])
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)
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final_state_dict[final_key][: value.shape[0]] = value
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elif "up_proj" in key:
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final_key = layer_name + ".gate_up_proj.weight"
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if final_key not in final_state_dict:
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final_state_dict[final_key] = value.new_empty(
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(value.shape[0] * 2, value.shape[1])
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)
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final_state_dict[final_key][value.shape[0] :] = value
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else:
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final_state_dict[key] = value
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del state_dict
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final_key = key
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parameters = dict(model.named_parameters())
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for key, value in final_state_dict.items():
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current_parameter_tensor = parameters.get(key, None)
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module_name, param_name = key.rsplit(".", 1)
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module = model.get_submodule(module_name)
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module_name, param_name = final_key.rsplit(".", 1)
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module = model.get_submodule(module_name)
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if (
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current_parameter_tensor is not None
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and current_parameter_tensor.shape != value.shape
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):
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raise ValueError(
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f"Name {key} -- Current {current_parameter_tensor.shape} and got {value.shape}"
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)
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try:
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current_parameter_tensor = module._parameters[param_name]
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except KeyError:
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current_parameter_tensor = None
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value = value.contiguous()
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if current_parameter_tensor is not None:
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if current_parameter_tensor.device == torch.device("meta"):
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# Init qkv
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if "query_key_value" in final_key:
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module._parameters[param_name] = value.new_empty(
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(value.shape[0] * 3, value.shape[1])
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)
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# Init gate and up proj
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elif "gate_up_proj" in final_key:
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module._parameters[param_name] = value.new_empty(
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(value.shape[0] * 2, value.shape[1])
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)
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if current_parameter_tensor is not None:
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module._parameters[param_name] = value
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else:
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module._buffers[param_name] = value
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# Copy to correct slice
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if "q_proj" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "k_proj" in key:
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module._parameters[param_name][
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value.shape[0] : value.shape[0] * 2
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] = value
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elif "v_proj" in key:
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module._parameters[param_name][value.shape[0] * 2 :] = value
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elif "gate_proj" in key:
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module._parameters[param_name][: value.shape[0]] = value
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elif "up_proj" in key:
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module._parameters[param_name][value.shape[0] :] = value
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else:
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if current_parameter_tensor.shape != value.shape:
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raise ValueError(
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f"Name {final_key} -- Current {current_parameter_tensor.shape} and got {value.shape}"
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)
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module._parameters[param_name] = value
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else:
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module._buffers[param_name] = value
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torch.cuda.empty_cache()
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model.post_load_weights()
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@ -168,7 +161,7 @@ class FlashLlamaSharded(FlashLlama):
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filenames = weight_files(model_id, revision=revision, extension=".safetensors")
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with init_empty_weights():
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model = FlashGPTNeoXForCausalLM(config)
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model = FlashLlamaForCausalLM(config, process_group=self.process_group)
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torch.distributed.barrier(group=self.process_group)
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self.load_weights(
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@ -179,7 +172,6 @@ class FlashLlamaSharded(FlashLlama):
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rank=self.rank,
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world_size=self.world_size,
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)
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model.post_load_weights()
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self.model = model.eval().to(dtype)
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torch.distributed.barrier(group=self.process_group)
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super(FlashCausalLM, self).__init__(
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@ -196,19 +188,28 @@ class FlashLlamaSharded(FlashLlama):
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rank: int,
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world_size: int,
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):
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parameters = dict(model.named_parameters())
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for file in filenames:
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with safe_open(
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file, framework="pt", device=str(device) if not quantize else "cpu"
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) as f:
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for name in f.keys():
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module_name, param_name = name.rsplit(".", 1)
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module = model.get_submodule(module_name)
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current_parameter_tensor = parameters.get(name, None)
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slice_ = f.get_slice(name)
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layer_name = ".".join(name.split(".")[:4])
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# Fused qkv
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if "q_proj" in name or "k_proj" in name or "v_proj" in name:
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final_name = layer_name + ".query_key_value.weight"
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# Fused gate and up projs
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elif "gate_proj" in name or "up_proj" in name:
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final_name = layer_name + ".gate_up_proj.weight"
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else:
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final_name = name
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module_name, param_name = final_name.rsplit(".", 1)
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module = model.get_submodule(module_name)
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if isinstance(module, TensorParallelColumnLinear):
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size = slice_.get_shape()[0]
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block_size = size // world_size
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@ -216,24 +217,18 @@ class FlashLlamaSharded(FlashLlama):
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stop = (rank + 1) * block_size
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tensor = slice_[start:stop]
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elif isinstance(module, TensorParallelRowLinear):
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if param_name == "weight":
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size = slice_.get_shape()[1]
|
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block_size = size // world_size
|
||||
start = rank * block_size
|
||||
stop = (rank + 1) * block_size
|
||||
tensor = slice_[:, start:stop]
|
||||
else:
|
||||
tensor = slice_[:]
|
||||
# XXX: Hack for Rowlinear to add the bias only once.
|
||||
if rank != 0:
|
||||
tensor = torch.zeros_like(tensor)
|
||||
size = slice_.get_shape()[1]
|
||||
block_size = size // world_size
|
||||
start = rank * block_size
|
||||
stop = (rank + 1) * block_size
|
||||
tensor = slice_[:, start:stop]
|
||||
elif isinstance(module, TensorParallelEmbedding):
|
||||
size = slice_.get_shape()[0]
|
||||
block_size = size // world_size
|
||||
start = rank * block_size
|
||||
stop = (rank + 1) * block_size
|
||||
tensor = slice_[start:stop]
|
||||
elif name == "embed_out.weight" and model.gpt_neox.tp_embeddings:
|
||||
elif name == "lm_head.weight" and model.model.tp_embeddings:
|
||||
size = slice_.get_shape()[0]
|
||||
block_size = size // world_size
|
||||
start = rank * block_size
|
||||
@ -245,20 +240,53 @@ class FlashLlamaSharded(FlashLlama):
|
||||
except:
|
||||
tensor = f.get_tensor(name)
|
||||
|
||||
if (
|
||||
current_parameter_tensor is not None
|
||||
and current_parameter_tensor.shape != tensor.shape
|
||||
):
|
||||
raise ValueError(
|
||||
f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
|
||||
)
|
||||
|
||||
tensor = tensor.contiguous()
|
||||
|
||||
try:
|
||||
current_parameter_tensor = module._parameters[param_name]
|
||||
except KeyError:
|
||||
current_parameter_tensor = None
|
||||
|
||||
if current_parameter_tensor is not None:
|
||||
module._parameters[param_name] = tensor
|
||||
if current_parameter_tensor.device == torch.device("meta"):
|
||||
# Init qkv
|
||||
if "query_key_value" in final_name:
|
||||
module._parameters[param_name] = tensor.new_empty(
|
||||
(tensor.shape[0] * 3, tensor.shape[1])
|
||||
)
|
||||
# Init gate and up proj
|
||||
elif "gate_up_proj" in final_name:
|
||||
module._parameters[param_name] = tensor.new_empty(
|
||||
(tensor.shape[0] * 2, tensor.shape[1])
|
||||
)
|
||||
|
||||
# Init gate and up proj
|
||||
if "q_proj" in name:
|
||||
module._parameters[param_name][: tensor.shape[0]] = tensor
|
||||
elif "k_proj" in name:
|
||||
module._parameters[param_name][
|
||||
tensor.shape[0] : tensor.shape[0] * 2
|
||||
] = tensor
|
||||
elif "v_proj" in name:
|
||||
module._parameters[param_name][
|
||||
tensor.shape[0] * 2 :
|
||||
] = tensor
|
||||
elif "gate_proj" in name:
|
||||
module._parameters[param_name][: tensor.shape[0]] = tensor
|
||||
elif "up_proj" in name:
|
||||
module._parameters[param_name][tensor.shape[0] :] = tensor
|
||||
else:
|
||||
if current_parameter_tensor.shape != tensor.shape:
|
||||
raise ValueError(
|
||||
f"Name {name} -- Current {current_parameter_tensor.shape} and got {tensor.shape}"
|
||||
)
|
||||
|
||||
module._parameters[param_name] = tensor
|
||||
|
||||
else:
|
||||
module._buffers[param_name] = tensor
|
||||
torch.cuda.empty_cache()
|
||||
model.post_load_weights()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
@ -268,7 +296,7 @@ class FlashLlamaSharded(FlashLlama):
|
||||
max_s: int,
|
||||
past_key_values: Optional = None,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
if self.model.gpt_neox.tp_embeddings:
|
||||
if self.model.model.tp_embeddings:
|
||||
logits, present = self.model.forward(
|
||||
input_ids=input_ids,
|
||||
position_ids=position_ids,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user