mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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f7eca56161
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com> Co-authored-by: zhang-ge-hao <842720660@qq.com>
642 lines
26 KiB
Python
642 lines
26 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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 math
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import tensorrt as trt
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from ..._common import default_net
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from ..._utils import pad_vocab_size, str_dtype_to_trt
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from ...functional import (RotaryScalingType, Tensor, gather_last_token_logits,
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gpt_attention, partial, recv, send, unary)
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from ...layers import (AttentionMaskType, AttentionParams, ColumnLinear,
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Embedding, GatedMLP, KeyValueCacheParams,
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PositionEmbeddingType, RmsNorm, RowLinear)
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from ...mapping import Mapping
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from ...module import Module, ModuleList
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from ...parameter import Parameter
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from ...quantization import QuantMode
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from ...quantization.layers import FP8Linear, FP8RowLinear
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from ..generation_mixin import GenerationMixin
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log = partial(unary, op=trt.UnaryOperation.LOG)
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ceil = partial(unary, op=trt.UnaryOperation.CEIL)
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class QWenAttention(Module):
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def __init__(
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self,
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hidden_size,
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num_attention_heads,
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max_position_embeddings,
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seq_length, # 2048
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num_kv_heads=None,
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num_layers=1,
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apply_query_key_layer_scaling=False,
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attention_mask_type=AttentionMaskType.causal,
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bias=True,
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dtype=None,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_embedding_base=10000.0,
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rotary_embedding_scaling=None,
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neox_rotary_style=False,
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rotary_embedding_percentage=1.0,
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tp_group=None,
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tp_size=1,
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quant_mode: QuantMode = QuantMode(0),
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q_scaling=1.0,
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cross_attention=False,
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relative_attention=False,
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max_distance=0,
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num_buckets=0,
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instance_id: int = 0,
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use_dynamic_ntk=True,
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use_logn_attn=True,
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):
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super().__init__()
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self.cross_attention = cross_attention
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self.seq_length = seq_length
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self.hidden_size = hidden_size
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self.num_heads = num_attention_heads
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self.attention_mask_type = attention_mask_type
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self.bias = bias
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self.attention_head_size = hidden_size // num_attention_heads
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self.num_attention_heads = num_attention_heads // tp_size
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self.num_attention_kv_heads = (
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num_kv_heads + tp_size - 1
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) // tp_size if num_kv_heads is not None else self.num_attention_heads
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self.hidden_size = hidden_size // tp_size
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self.max_position_embeddings = max_position_embeddings
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self.num_layers = num_layers
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.norm_factor = math.sqrt(self.attention_head_size)
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self.q_scaling = q_scaling
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if self.apply_query_key_layer_scaling:
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self.norm_factor *= self.num_layers
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self.q_scaling *= self.num_layers
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self.position_embedding_type = position_embedding_type
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self.relative_attention = relative_attention
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self.max_distance = max_distance
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self.rotary_embedding_base = rotary_embedding_base
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self.rotary_embedding_scale_type = RotaryScalingType.none
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self.rotary_embedding_scale = 1.0
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if rotary_embedding_scaling is not None:
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assert rotary_embedding_scaling["type"] in ["linear", "dynamic"]
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self.rotary_embedding_scale_type = RotaryScalingType.linear if rotary_embedding_scaling[
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"type"] == "linear" else RotaryScalingType.dynamic
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self.rotary_embedding_scale = rotary_embedding_scaling["factor"]
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assert self.rotary_embedding_scale > 1.0
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self.rotary_embedding_dim = 0
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self.neox_rotary_style = neox_rotary_style
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if self.position_embedding_type == PositionEmbeddingType.rope_gpt_neox:
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self.rotary_embedding_dim = int(self.attention_head_size *
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rotary_embedding_percentage)
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self.dtype = dtype
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self.quant_mode = quant_mode
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self.use_int8_kv_cache = self.quant_mode.has_int8_kv_cache()
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if self.use_int8_kv_cache:
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self.kv_orig_quant_scale = Parameter(shape=(1, ), dtype='float32')
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self.kv_quant_orig_scale = Parameter(shape=(1, ), dtype='float32')
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else:
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self.register_parameter('kv_orig_quant_scale', None)
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self.register_parameter('kv_quant_orig_scale', None)
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self.use_fp8_qdq = self.quant_mode.has_fp8_qdq()
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if self.use_fp8_qdq:
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self.qkv = FP8Linear(hidden_size,
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hidden_size +
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(2 * tp_size * self.num_attention_kv_heads *
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self.attention_head_size),
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bias=True,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False)
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self.dense = FP8RowLinear(hidden_size,
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hidden_size,
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bias=bias,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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instance_id=instance_id)
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else:
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self.qkv = ColumnLinear(hidden_size,
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hidden_size +
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(2 * tp_size * self.num_attention_kv_heads *
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self.attention_head_size),
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bias=True,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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gather_output=False)
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self.dense = RowLinear(hidden_size,
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hidden_size,
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bias=bias,
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dtype=dtype,
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tp_group=tp_group,
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tp_size=tp_size,
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instance_id=instance_id)
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if relative_attention:
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self.rel_attn_table = Parameter(shape=(num_attention_heads //
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tp_size, num_buckets),
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dtype=dtype)
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self.use_dynamic_ntk = use_dynamic_ntk
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self.use_logn_attn = use_logn_attn
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def forward(
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self,
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hidden_states: Tensor,
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use_cache=False,
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kv_cache_params=None,
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attention_params=None,
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workspace=None,
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):
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if not default_net().plugin_config.gpt_attention_plugin:
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raise ValueError('QWen is only supported with GPTAttention plugin')
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assert isinstance(hidden_states, Tensor)
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qkv = self.qkv(hidden_states)
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kv_orig_quant_scale = self.kv_orig_quant_scale.value if self.use_int8_kv_cache else None
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kv_quant_orig_scale = self.kv_quant_orig_scale.value if self.use_int8_kv_cache else None
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# return outputs
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context, past_key_value = gpt_attention(
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qkv=qkv,
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past_key_value=kv_cache_params.get_first_past_key_value(),
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sequence_length=attention_params.sequence_length,
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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host_max_attention_window_sizes=kv_cache_params.
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host_max_attention_window_sizes,
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context_lengths=attention_params.context_lengths,
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cache_indirection=kv_cache_params.cache_indirection,
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host_request_types=attention_params.host_request_types,
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num_heads=self.num_attention_heads,
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num_kv_heads=self.num_attention_kv_heads,
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hidden_size_per_head=self.attention_head_size,
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q_scaling=self.q_scaling,
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rotary_embedding_dim=self.
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rotary_embedding_dim, # when we use it 0, we will not use rotary embedding in plugin
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rotary_embedding_scale_type=self.neox_rotary_style,
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rotary_embedding_max_positions=self.max_position_embeddings,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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kv_orig_quant_scale=kv_orig_quant_scale,
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kv_quant_orig_scale=kv_quant_orig_scale,
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kv_cache_quant_mode=QuantMode.from_description(
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use_int8_kv_cache=self.use_int8_kv_cache),
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kv_cache_block_pointers=kv_cache_params.
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get_first_kv_cache_block_pointers(),
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host_kv_cache_block_pointers=kv_cache_params.
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get_first_host_kv_cache_block_pointers(),
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max_context_length=attention_params.max_context_length,
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mask_type=self.attention_mask_type.value,
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host_context_lengths=attention_params.host_context_lengths)
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context = self.dense(context, workspace=workspace)
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if use_cache:
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return (context, past_key_value)
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else:
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return context
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class QWenBlock(Module):
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def __init__(self,
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layer_id,
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hidden_size,
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seq_length,
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num_attention_heads,
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max_position_embeddings,
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num_layers,
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dtype=None,
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attention_mask_type=AttentionMaskType.causal,
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apply_query_key_layer_scaling=False,
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hidden_act='silu',
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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rotary_base=10000.0,
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rotary_scaling=None,
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quant_mode=QuantMode(0),
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mlp_hidden_size=None,
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neox_rotary_style=True,
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bias=False,
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tp_group=None,
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tp_size=1,
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rms_norm_eps=1e-06):
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super().__init__()
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self._layer_id = layer_id # useful for debugging
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self.hidden_size = hidden_size
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self.seq_length = seq_length
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self.mlp_hidden_size = mlp_hidden_size
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self.neox_rotary_style = neox_rotary_style
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self.bias = bias
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self.hidden_act = hidden_act
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self.dtype = dtype
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self.attention_mask_type = attention_mask_type
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.tp_group = tp_group
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self.tp_size = tp_size
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self.num_attention_heads = num_attention_heads
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self.max_position_embeddings = max_position_embeddings
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self.num_layers = num_layers
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self.position_embedding_type = position_embedding_type
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self.ln_1 = RmsNorm(normalized_shape=hidden_size,
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eps=rms_norm_eps,
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dtype=dtype)
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self.attention = QWenAttention(
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hidden_size=self.hidden_size,
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num_attention_heads=self.num_attention_heads,
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max_position_embeddings=self.max_position_embeddings,
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num_layers=self.num_layers,
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seq_length=self.seq_length,
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dtype=self.dtype,
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attention_mask_type=self.attention_mask_type,
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bias=bias,
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position_embedding_type=self.position_embedding_type,
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rotary_embedding_base=rotary_base,
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rotary_embedding_scaling=rotary_scaling,
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neox_rotary_style=neox_rotary_style,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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quant_mode=quant_mode,
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)
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if not mlp_hidden_size:
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mlp_hidden_size = hidden_size * 4
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self.mlp = GatedMLP(hidden_size=hidden_size,
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ffn_hidden_size=mlp_hidden_size // 2,
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hidden_act=hidden_act,
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dtype=dtype,
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bias=False,
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tp_group=tp_group,
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tp_size=tp_size,
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quant_mode=quant_mode,
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instance_id=2 * layer_id + 1)
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self.ln_2 = RmsNorm(normalized_shape=hidden_size,
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eps=rms_norm_eps,
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dtype=dtype)
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def forward(
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self,
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hidden_states: Tensor,
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use_cache=False,
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kv_cache_params=None,
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attention_params=None,
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all_reduce_workspace=None,
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):
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residual = hidden_states
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hidden_states = self.ln_1(hidden_states)
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attention_output = self.attention(
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hidden_states,
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use_cache=use_cache,
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kv_cache_params=kv_cache_params,
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attention_params=attention_params,
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workspace=all_reduce_workspace,
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)
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if use_cache:
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attention_output, presents = attention_output
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hidden_states = residual + attention_output
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residual = hidden_states
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hidden_states = self.ln_2(hidden_states)
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hidden_states = self.mlp(hidden_states)
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hidden_states = residual + hidden_states
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if use_cache:
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return (hidden_states, presents)
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return hidden_states
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class QWenModel(Module):
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def __init__(
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self,
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num_layers,
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num_heads,
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hidden_size,
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seq_length,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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dtype,
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mlp_hidden_size=None,
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position_embedding_type=PositionEmbeddingType.rope_gpt_neox,
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neox_rotary_style=True,
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bias=False,
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rotary_base=10000.0,
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rotary_scaling=None,
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mapping=Mapping(),
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quant_mode=QuantMode(0),
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use_parallel_embedding=False,
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embedding_sharding_dim=0,
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rms_norm_eps=1e-06,
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):
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super().__init__()
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self.mapping = mapping
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if self.mapping.is_first_pp_rank():
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self.vocab_embedding = Embedding(
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num_embeddings=vocab_size,
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embedding_dim=hidden_size,
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dtype=dtype,
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tp_size=mapping.tp_size if use_parallel_embedding else 1,
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tp_group=mapping.tp_group if use_parallel_embedding else None,
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sharding_dim=embedding_sharding_dim,
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tp_rank=mapping.tp_rank)
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self.layers = ModuleList([
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QWenBlock(layer_id=i,
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hidden_size=hidden_size,
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seq_length=seq_length,
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num_attention_heads=num_heads,
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num_layers=num_layers,
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max_position_embeddings=max_position_embeddings,
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dtype=dtype,
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hidden_act=hidden_act,
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quant_mode=quant_mode,
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mlp_hidden_size=mlp_hidden_size,
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position_embedding_type=position_embedding_type,
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rotary_base=rotary_base,
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rotary_scaling=rotary_scaling,
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neox_rotary_style=neox_rotary_style,
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bias=bias,
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tp_group=mapping.tp_group,
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tp_size=mapping.tp_size,
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rms_norm_eps=rms_norm_eps)
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for i in self.mapping.pp_layers(num_layers)
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])
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self.ln_f = RmsNorm(normalized_shape=hidden_size,
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eps=rms_norm_eps,
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dtype=dtype)
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def forward(self,
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input_ids,
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position_ids=None,
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use_cache=False,
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kv_cache_params=None,
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attention_params=None,
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hidden_states=None,
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all_reduce_workspace=None):
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if kv_cache_params.past_key_value is None:
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tuple([None] * len(self.layers))
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kv_cache_params.fill_none_tensor_list(len(self.layers))
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if use_cache:
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presents = []
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if self.mapping.is_first_pp_rank():
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hidden_states = self.vocab_embedding(input_ids)
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else:
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hidden_states = recv(hidden_states, self.mapping.prev_pp_rank())
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self.register_network_output(f"embd", hidden_states)
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for layer, past, pointer, host_pointer, max_attention_window_size in zip(
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self.layers, kv_cache_params.past_key_value,
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kv_cache_params.kv_cache_block_pointers,
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kv_cache_params.host_kv_cache_block_pointers,
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kv_cache_params.host_max_attention_window_sizes):
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hidden_states = layer(
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hidden_states,
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use_cache=use_cache,
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kv_cache_params=KeyValueCacheParams(
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past_key_value=[past],
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host_past_key_value_lengths=kv_cache_params.
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host_past_key_value_lengths,
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host_max_attention_window_sizes=max_attention_window_size,
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kv_cache_block_pointers=[pointer],
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host_kv_cache_block_pointers=[host_pointer],
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cache_indirection=kv_cache_params.cache_indirection),
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attention_params=attention_params,
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all_reduce_workspace=all_reduce_workspace)
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if use_cache:
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presents.append(hidden_states[1])
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hidden_states = hidden_states[0]
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if self.mapping.is_last_pp_rank():
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hidden_states = self.ln_f(hidden_states)
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else:
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hidden_states = send(hidden_states, self.mapping.next_pp_rank())
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if use_cache:
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return (hidden_states, tuple(presents))
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return hidden_states
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class QWenForCausalLM(QWenModel, GenerationMixin):
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def __init__(
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self,
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num_layers,
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num_heads,
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num_kv_heads,
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hidden_size,
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seq_length,
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vocab_size,
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hidden_act,
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max_position_embeddings,
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dtype,
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logits_dtype="float32",
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mlp_hidden_size=None,
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neox_rotary_style=True,
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rotary_base=10000.0,
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rotary_scaling=None,
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mapping=Mapping(),
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quant_mode=QuantMode(0),
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|
use_parallel_embedding=False,
|
|
embedding_sharding_dim=0,
|
|
rms_norm_eps=1e-06,
|
|
):
|
|
self.mapping = mapping
|
|
if isinstance(dtype, str):
|
|
self.dtype = str_dtype_to_trt(dtype)
|
|
else:
|
|
assert isinstance(dtype, trt.DataType)
|
|
self.dtype = dtype
|
|
if isinstance(logits_dtype, str):
|
|
self.logits_dtype = str_dtype_to_trt(logits_dtype)
|
|
else:
|
|
assert isinstance(logits_dtype, trt.DataType)
|
|
self.logits_dtype = logits_dtype
|
|
self.num_layers = num_layers
|
|
self.num_heads = num_heads
|
|
if num_kv_heads is None or num_kv_heads <= 0:
|
|
num_kv_heads = num_heads
|
|
self.num_kv_heads = num_kv_heads
|
|
self.hidden_size = hidden_size
|
|
self.vocab_size = vocab_size
|
|
self.tp_size = mapping.tp_size
|
|
|
|
self.kv_dtype = self.dtype
|
|
if quant_mode.has_int8_kv_cache():
|
|
self.kv_dtype = str_dtype_to_trt('int8')
|
|
elif quant_mode.has_fp8_kv_cache():
|
|
self.kv_dtype = str_dtype_to_trt('fp8')
|
|
self.quant_mode = quant_mode
|
|
self.use_parallel_embedding = use_parallel_embedding
|
|
self.embedding_sharding_dim = embedding_sharding_dim
|
|
|
|
super().__init__(num_layers=num_layers,
|
|
num_heads=num_heads,
|
|
hidden_size=hidden_size,
|
|
seq_length=seq_length,
|
|
vocab_size=vocab_size,
|
|
hidden_act=hidden_act,
|
|
max_position_embeddings=max_position_embeddings,
|
|
dtype=dtype,
|
|
mlp_hidden_size=mlp_hidden_size,
|
|
neox_rotary_style=neox_rotary_style,
|
|
rotary_base=rotary_base,
|
|
rotary_scaling=rotary_scaling,
|
|
mapping=mapping,
|
|
quant_mode=quant_mode,
|
|
use_parallel_embedding=use_parallel_embedding,
|
|
embedding_sharding_dim=embedding_sharding_dim,
|
|
rms_norm_eps=rms_norm_eps)
|
|
vocab_size_padded = pad_vocab_size(vocab_size, mapping.tp_size)
|
|
if self.mapping.is_last_pp_rank():
|
|
self.lm_head = ColumnLinear(hidden_size,
|
|
vocab_size_padded,
|
|
bias=False,
|
|
dtype=dtype,
|
|
tp_group=mapping.tp_group,
|
|
tp_size=mapping.tp_size,
|
|
gather_output=True)
|
|
|
|
def forward(self,
|
|
input_ids,
|
|
position_ids=None,
|
|
use_cache=False,
|
|
last_token_ids=None,
|
|
kv_cache_params=None,
|
|
attention_params=None,
|
|
hidden_states=None,
|
|
all_reduce_workspace=None):
|
|
hidden_states = super().forward(input_ids, position_ids, use_cache,
|
|
kv_cache_params, attention_params,
|
|
hidden_states, all_reduce_workspace)
|
|
if use_cache:
|
|
hidden_states, presents = hidden_states
|
|
|
|
if self.mapping.is_last_pp_rank():
|
|
hidden_states = gather_last_token_logits(
|
|
hidden_states, last_token_ids,
|
|
default_net().plugin_config.remove_input_padding)
|
|
|
|
# [batch_size, hidden_size] -> [batch_size, vocab_size]
|
|
lm_logits = self.lm_head(hidden_states)
|
|
lm_logits.mark_output('logits', self.logits_dtype)
|
|
else:
|
|
hidden_states.mark_output('hidden_states_output', self.dtype)
|
|
|
|
if use_cache and default_net().plugin_config.paged_kv_cache == False:
|
|
for i, present in zip(self.mapping.pp_layers(self.num_layers),
|
|
presents):
|
|
present.mark_output(f'present_key_value_{i}', self.kv_dtype)
|
|
if self.mapping.is_last_pp_rank():
|
|
return (lm_logits, presents)
|
|
return (hidden_states, presents)
|
|
else:
|
|
if self.mapping.is_last_pp_rank():
|
|
return lm_logits
|
|
return hidden_states
|
|
|
|
def prepare_inputs(
|
|
self,
|
|
max_batch_size,
|
|
max_input_len,
|
|
max_new_tokens,
|
|
use_cache,
|
|
max_beam_width: int = 1,
|
|
max_num_tokens: int = None,
|
|
):
|
|
'''@brief: Prepare inputs Tensors for the model, the given sizes are used to determine the
|
|
ranges of the dimensions of when using TRT dynamic shapes.
|
|
|
|
@return: a list contains values which can be fed into the self.forward()
|
|
'''
|
|
|
|
# Prepare inputs
|
|
head_size = self.hidden_size // self.num_heads
|
|
remove_input_padding = default_net().plugin_config.remove_input_padding
|
|
use_gpt_attention_plugin = default_net(
|
|
).plugin_config.gpt_attention_plugin
|
|
use_gemm_plugin = default_net().plugin_config.gemm_plugin
|
|
paged_kv_cache = default_net().plugin_config.paged_kv_cache
|
|
tokens_per_block = default_net().plugin_config.tokens_per_block
|
|
use_custom_all_reduce = default_net(
|
|
).plugin_config.use_custom_all_reduce
|
|
|
|
model_inputs = self.prepare_basic_inputs(
|
|
max_batch_size,
|
|
max_beam_width,
|
|
max_input_len,
|
|
max_new_tokens,
|
|
self.num_kv_heads,
|
|
head_size,
|
|
self.num_layers,
|
|
self.kv_dtype,
|
|
remove_input_padding=remove_input_padding,
|
|
use_gpt_attention_plugin=use_gpt_attention_plugin,
|
|
use_gemm_plugin=use_gemm_plugin,
|
|
use_custom_all_reduce=use_custom_all_reduce,
|
|
paged_kv_cache=paged_kv_cache,
|
|
tokens_per_block=tokens_per_block,
|
|
dtype=self.dtype,
|
|
num_heads=self.num_heads,
|
|
mapping=self.mapping,
|
|
max_num_tokens=max_num_tokens,
|
|
)
|
|
|
|
return (model_inputs['input_ids'], model_inputs['position_ids'], True,
|
|
model_inputs['last_token_ids'],
|
|
KeyValueCacheParams(
|
|
past_key_value=model_inputs['past_key_value'],
|
|
host_past_key_value_lengths=model_inputs[
|
|
'host_past_key_value_lengths'],
|
|
host_max_attention_window_sizes=model_inputs[
|
|
'host_max_attention_window_sizes'],
|
|
kv_cache_block_pointers=model_inputs[
|
|
'kv_cache_block_pointers_list'],
|
|
host_kv_cache_block_pointers=model_inputs[
|
|
'host_kv_cache_block_pointers_list'],
|
|
cache_indirection=model_inputs['cache_indirection'],
|
|
),
|
|
AttentionParams(
|
|
sequence_length=model_inputs['sequence_length'],
|
|
context_lengths=model_inputs['context_lengths'],
|
|
host_context_lengths=model_inputs['host_context_lengths'],
|
|
max_context_length=max_input_len,
|
|
host_request_types=model_inputs['host_request_types']),
|
|
model_inputs['hidden_states_input'],
|
|
model_inputs['all_reduce_workspace'])
|