mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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1134 lines
43 KiB
Python
1134 lines
43 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 numpy as np
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import tensorrt as trt
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from .._common import default_net, precision
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from .._utils import int32_array
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from ..functional import (ACT2FN, Tensor, allgather, allreduce, cast, concat,
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constant, gpt_attention, matmul, mul, shape, slice,
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softmax, split, where)
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from ..layers.attention import AttentionMaskType, PositionEmbeddingType
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from ..layers.linear import Linear, RowLinear
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from ..module import Module
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from ..parameter import Parameter
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from .functional import (dequantize, quantize, quantize_per_token,
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quantize_tensor, smooth_quant_gemm,
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smooth_quant_layer_norm, smooth_quant_rms_norm,
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weight_only_groupwise_quant_matmul,
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weight_only_quant_matmul)
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from .mode import QuantMode
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class Quantize(Module):
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"""
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Quantize Layer
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For per-tensor mode, the scaling factor is a scalar.
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For per-channel mode, the scaling factor is a vector.
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"""
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def __init__(
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self,
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output_dtype: str = 'int8',
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scaling_factor_dtype: str = 'float32',
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in_channels: int = -1,
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axis=-1,
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) -> None:
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super().__init__()
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self.scaling_factor = Parameter(shape=(in_channels, ) if axis != -1 else
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(),
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dtype=scaling_factor_dtype)
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self.output_dtype = output_dtype
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self.axis = axis
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def forward(self, x):
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return quantize(x, self.scaling_factor.value, self.output_dtype,
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self.axis)
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class QuantizePerToken(Module):
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"""
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Quantize Per Token and compute dynamic scales for SmoothQuant
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"""
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def forward(self, x):
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return quantize_per_token(x)
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class Dequantize(Module):
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"""
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Dequantize Layer.
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"""
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def __init__(self, axis: int = -1) -> None:
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super().__init__()
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self.scaling_factor = Parameter(shape=())
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self.axis = axis
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def forward(self, input):
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return dequantize(input, self.scaling_factor.value, self.axis)
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class SmoothQuantLinear(Module):
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def __init__(self,
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in_features,
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out_features,
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bias=True,
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dtype=None,
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tp_group=None,
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tp_size=1,
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gather_output=True,
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quant_mode=QuantMode(0)):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features // tp_size
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if not quant_mode.has_act_and_weight_quant():
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raise ValueError(
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"SmoothQuant Linear has to have act+weight quantization mode set"
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)
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weights_dtype = dtype
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# Dirty hack to make it work with SmoothQuant int8 weights
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# reinterpreted as fp32 weights due to the int8 TRT plugin limitation.
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if quant_mode.has_act_and_weight_quant():
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assert self.in_features % 4 == 0
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self.in_features = self.in_features // 4
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weights_dtype = "float32"
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self.weight = Parameter(shape=(self.out_features, self.in_features),
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dtype=weights_dtype)
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if quant_mode.has_act_and_weight_quant():
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scale_shape = (1, self.out_features
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) if quant_mode.has_per_channel_scaling() else (1, 1)
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self.per_channel_scale = Parameter(shape=scale_shape,
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dtype="float32")
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if quant_mode.has_act_static_scaling():
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self.act_scale = Parameter(shape=(1, 1), dtype="float32")
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self.tp_size = tp_size
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self.tp_group = tp_group
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self.gather_output = gather_output
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self.quant_mode = quant_mode
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if bias:
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self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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def forward(self, x):
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if self.quant_mode.has_act_static_scaling():
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per_token_scale = self.act_scale.value
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else:
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# If we are in SmoothQuant with dynamic activation scaling,
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# input x has to be a tuple of int8 tensor and fp32 scaling factors
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x, per_token_scale = x
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x = smooth_quant_gemm(x, self.weight.value, per_token_scale,
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self.per_channel_scale.value,
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self.quant_mode.has_per_token_dynamic_scaling(),
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self.quant_mode.has_per_channel_scaling())
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if self.bias is not None:
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x = x + self.bias.value
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if self.gather_output and self.tp_size > 1 and self.tp_group is not None:
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# 1. [dim0, local_dim] -> [dim0 * tp_size, local_dim]
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x = allgather(x, self.tp_group)
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# 2. [dim0 * tp_size, local_dim] -> [dim0, local_dim * tp_size]
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# 2.1 split
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split_size = shape(x, dim=0) / self.tp_size
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ndim = x.ndim()
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starts = [constant(int32_array([0])) for _ in range(ndim)]
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sizes = [shape(x, dim=d) for d in range(ndim)]
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sizes[0] = split_size
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sections = []
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for i in range(self.tp_size):
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starts[0] = split_size * i
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sections.append(slice(x, concat(starts), concat(sizes)))
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# 2.2 concat
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x = concat(sections, dim=1)
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return x
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SmoothQuantColumnLinear = SmoothQuantLinear
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class SmoothQuantRowLinear(Module):
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def __init__(self,
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in_features,
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out_features,
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bias=True,
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dtype=None,
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tp_group=None,
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tp_size=1,
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quant_mode=QuantMode(0)):
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super().__init__()
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self.in_features = in_features // tp_size
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self.out_features = out_features
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if not quant_mode.has_act_and_weight_quant():
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raise ValueError(
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"SmoothQuant Linear has to have act+weight quantization mode set"
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)
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weights_dtype = dtype
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# Dirty hack to make it work with SmoothQuant int8 weights
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# reinterpreted as fp32 weights due to the int8 TRT plugin limitation.
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if quant_mode.has_act_and_weight_quant():
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assert self.in_features % 4 == 0
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self.in_features = self.in_features // 4
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weights_dtype = "float32"
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self.weight = Parameter(shape=(self.out_features, self.in_features),
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dtype=weights_dtype)
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self.smoother = Parameter(shape=(1, self.in_features * 4),
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dtype="float32")
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if quant_mode.has_act_and_weight_quant():
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scale_shape = (1, self.out_features
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) if quant_mode.has_per_channel_scaling() else (1, 1)
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self.per_channel_scale = Parameter(shape=scale_shape,
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dtype="float32")
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if quant_mode.has_act_static_scaling():
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self.act_scale = Parameter(shape=(1, 1), dtype="float32")
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if bias:
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self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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self.tp_group = tp_group
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self.tp_size = tp_size
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self.quant_mode = quant_mode
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def forward(self, x):
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if self.quant_mode.has_act_static_scaling():
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per_token_scale = self.act_scale.value
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else:
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x, per_token_scale = x
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x = smooth_quant_gemm(x, self.weight.value, per_token_scale,
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self.per_channel_scale.value,
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self.quant_mode.has_per_token_dynamic_scaling(),
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self.quant_mode.has_per_channel_scaling())
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if self.tp_size > 1 and self.tp_group is not None:
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x = allreduce(x, self.tp_group)
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if self.bias is not None:
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x = x + self.bias.value
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return x
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class SmoothQuantLayerNorm(Module):
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def __init__(self,
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normalized_shape,
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eps=1e-05,
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elementwise_affine=True,
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dtype=None,
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quant_mode=QuantMode(0)):
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super().__init__()
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if isinstance(normalized_shape, int):
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normalized_shape = (normalized_shape, )
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if not quant_mode.has_act_and_weight_quant():
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raise ValueError(
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"SmoothQuant layer norm has to have some quantization mode set")
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self.normalized_shape = tuple(normalized_shape)
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self.elementwise_affine = elementwise_affine
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if self.elementwise_affine:
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self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
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self.bias = Parameter(shape=self.normalized_shape, dtype=dtype)
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else:
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self.register_parameter('weight', None)
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self.register_parameter('bias', None)
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self.eps = eps
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self.quant_mode = quant_mode
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if self.quant_mode.has_act_and_weight_quant():
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self.scale_to_int = Parameter(shape=(1, ), dtype=dtype)
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else:
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self.register_parameter('scale_to_int', None)
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def forward(self, x):
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weight = None if self.weight is None else self.weight.value
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bias = None if self.bias is None else self.bias.value
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scale = None if self.scale_to_int is None else self.scale_to_int.value
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return smooth_quant_layer_norm(
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x,
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self.normalized_shape,
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weight,
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bias,
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scale,
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self.eps,
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dynamic_act_scaling=self.quant_mode.has_per_token_dynamic_scaling())
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class SmoothQuantRmsNorm(Module):
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def __init__(self,
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normalized_shape,
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eps=1e-06,
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elementwise_affine=True,
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dtype=None,
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quant_mode=QuantMode(0),
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bias=False):
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super().__init__()
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if isinstance(normalized_shape, int):
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normalized_shape = (normalized_shape, )
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if not quant_mode.has_act_and_weight_quant():
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raise ValueError(
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"SmoothQuant Rms norm has to have some quantization mode set")
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self.normalized_shape = tuple(normalized_shape)
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self.elementwise_affine = elementwise_affine
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if self.elementwise_affine:
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self.weight = Parameter(shape=self.normalized_shape, dtype=dtype)
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else:
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self.register_parameter('weight', None)
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if bias:
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self.bias = Parameter(shape=self.normalized_shape, dtype=dtype)
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else:
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self.register_parameter('bias', None)
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self.eps = eps
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self.quant_mode = quant_mode
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if self.quant_mode.has_act_and_weight_quant():
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self.scale_to_int = Parameter(shape=(1, ), dtype=dtype)
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else:
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self.register_parameter('scale_to_int', None)
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def forward(self, x):
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weight = None if self.weight is None else self.weight.value
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bias = None if self.bias is None else self.bias.value
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scale = None if self.scale_to_int is None else self.scale_to_int.value
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return smooth_quant_rms_norm(
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x,
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self.normalized_shape,
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weight,
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bias,
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scale,
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self.eps,
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dynamic_act_scaling=self.quant_mode.has_per_token_dynamic_scaling())
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class WeightOnlyQuantLinear(Module):
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def __init__(self,
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in_features,
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out_features,
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bias=True,
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dtype=None,
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tp_group=None,
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tp_size=1,
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gather_output=True,
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quant_mode=QuantMode.use_weight_only()):
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super().__init__()
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if quant_mode.is_int8_weight_only():
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self.weight_only_quant_mode = 1
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quant_type_size_in_bits = 8
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elif quant_mode.is_int4_weight_only():
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self.weight_only_quant_mode = 2
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quant_type_size_in_bits = 4
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self.in_features = in_features
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self.out_features = out_features // tp_size
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# we use a fake tensor with data_type = float
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self.weight = Parameter(shape=(self.in_features,
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int(self.out_features *
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quant_type_size_in_bits / 32)),
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dtype="float32")
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scale_shape = (self.out_features, )
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self.per_channel_scale = Parameter(shape=scale_shape, dtype=dtype)
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self.tp_size = tp_size
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self.tp_group = tp_group
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self.gather_output = gather_output
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if bias:
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self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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def forward(self, x):
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x = weight_only_quant_matmul(x, self.weight.value,
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self.per_channel_scale.value,
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self.weight_only_quant_mode)
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if self.bias is not None:
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x = x + self.bias.value
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if self.gather_output and self.tp_size > 1 and self.tp_group is not None:
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# 1. [dim0, local_dim] -> [dim0 * tp_size, local_dim]
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x = allgather(x, self.tp_group)
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# 2. [dim0 * tp_size, local_dim] -> [dim0, local_dim * tp_size]
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# 2.1 split
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split_size = shape(x, dim=0) / self.tp_size
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ndim = x.ndim()
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starts = [constant(int32_array([0])) for _ in range(ndim)]
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sizes = [shape(x, dim=d) for d in range(ndim)]
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sizes[0] = split_size
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sections = []
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for i in range(self.tp_size):
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starts[0] = split_size * i
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sections.append(slice(x, concat(starts), concat(sizes)))
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# 2.2 concat
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x = concat(sections, dim=1)
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return x
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WeightOnlyQuantColumnLinear = WeightOnlyQuantLinear
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class WeightOnlyQuantRowLinear(Module):
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def __init__(self,
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in_features,
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out_features,
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bias=True,
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dtype=None,
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tp_group=None,
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tp_size=1,
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quant_mode=QuantMode.use_weight_only()):
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super().__init__()
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if quant_mode.is_int8_weight_only():
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self.weight_only_quant_mode = 1
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elif quant_mode.is_int4_weight_only():
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self.weight_only_quant_mode = 2
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self.in_features = in_features // tp_size
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self.out_features = out_features
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#we use a fake tensor with data_type = float
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self.weight = Parameter(shape=(self.in_features,
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int(self.out_features / 4 /
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self.weight_only_quant_mode)),
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dtype="float32")
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self.per_channel_scale = Parameter(shape=(self.out_features, ),
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dtype=dtype)
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if bias:
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self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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self.tp_group = tp_group
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self.tp_size = tp_size
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def forward(self, x):
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x = weight_only_quant_matmul(x, self.weight.value,
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self.per_channel_scale.value,
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self.weight_only_quant_mode)
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if self.tp_size > 1 and self.tp_group is not None:
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x = allreduce(x, self.tp_group)
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if self.bias is not None:
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x = x + self.bias.value
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return x
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class WeightOnlyGroupwiseQuantLinear(Module):
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def __init__(self,
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in_features,
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out_features,
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group_size=128,
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pre_quant_scale=False,
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zero=False,
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bias=False,
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dtype=None,
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tp_group=None,
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tp_size=1,
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gather_output=True):
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super().__init__()
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# Flags for indicating whether the corresponding inputs are applied in quant_algo
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BIAS = 1
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ZER0 = 2
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PRE_SCALE_QUANT = 4
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self.quant_algo = pre_quant_scale * PRE_SCALE_QUANT + zero * ZER0 + bias * BIAS
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self.group_size = group_size
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self.in_features = in_features
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self.out_features = out_features // tp_size
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self.qweight = Parameter(shape=(self.in_features,
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self.out_features // 8),
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dtype="float32")
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scale_shape = (self.in_features // group_size, self.out_features)
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self.scale = Parameter(shape=scale_shape, dtype=dtype)
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if pre_quant_scale:
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self.pre_quant_scale = Parameter(shape=(1, self.in_features),
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dtype=dtype)
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else:
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self.register_parameter('pre_quant_scale', None)
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if zero:
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self.zero = Parameter(shape=scale_shape, dtype=dtype)
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else:
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self.register_parameter('zero', None)
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if bias:
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self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
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else:
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self.register_parameter('bias', None)
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self.tp_size = tp_size
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self.tp_group = tp_group
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self.gather_output = gather_output
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def forward(self, x):
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pre_quant_scale = self.pre_quant_scale.value if self.pre_quant_scale else None
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zero = self.zero.value if self.zero else None
|
|
bias = self.bias.value if self.bias else None
|
|
|
|
x = weight_only_groupwise_quant_matmul(x, pre_quant_scale,
|
|
self.qweight.value,
|
|
self.scale.value, zero, bias,
|
|
self.quant_algo, self.group_size)
|
|
|
|
if self.gather_output and self.tp_size > 1 and self.tp_group is not None:
|
|
# 1. [dim0, local_dim] -> [dim0 * tp_size, local_dim]
|
|
x = allgather(x, self.tp_group)
|
|
|
|
# 2. [dim0 * tp_size, local_dim] -> [dim0, local_dim * tp_size]
|
|
# 2.1 split
|
|
split_size = shape(x, dim=0) / self.tp_size
|
|
ndim = x.ndim()
|
|
starts = [constant(int32_array([0])) for _ in range(ndim)]
|
|
sizes = [shape(x, dim=d) for d in range(ndim)]
|
|
sizes[0] = split_size
|
|
sections = []
|
|
for i in range(self.tp_size):
|
|
starts[0] = split_size * i
|
|
sections.append(slice(x, concat(starts), concat(sizes)))
|
|
# 2.2 concat
|
|
x = concat(sections, dim=1)
|
|
|
|
return x
|
|
|
|
|
|
WeightOnlyGroupwiseQuantColumnLinear = WeightOnlyGroupwiseQuantLinear
|
|
|
|
|
|
class WeightOnlyGroupwiseQuantRowLinear(Module):
|
|
|
|
def __init__(self,
|
|
in_features,
|
|
out_features,
|
|
group_size=128,
|
|
pre_quant_scale=False,
|
|
zero=False,
|
|
bias=False,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1):
|
|
super().__init__()
|
|
|
|
# Flags for indicating whether the corresponding inputs are applied in quant_algo
|
|
BIAS = 1
|
|
ZER0 = 2
|
|
PRE_SCALE_QUANT = 4
|
|
|
|
self.quant_algo = pre_quant_scale * PRE_SCALE_QUANT + zero * ZER0 + bias * BIAS
|
|
self.group_size = group_size
|
|
self.in_features = in_features // tp_size
|
|
self.out_features = out_features
|
|
self.qweight = Parameter(shape=(self.in_features,
|
|
self.out_features // 8),
|
|
dtype="float32")
|
|
|
|
scale_shape = (self.in_features // group_size, self.out_features)
|
|
self.scale = Parameter(shape=scale_shape, dtype=dtype)
|
|
|
|
if pre_quant_scale:
|
|
self.pre_quant_scale = Parameter(shape=(1, self.in_features),
|
|
dtype=dtype)
|
|
else:
|
|
self.register_parameter('pre_quant_scale', None)
|
|
|
|
if zero:
|
|
self.zero = Parameter(shape=scale_shape, dtype=dtype)
|
|
else:
|
|
self.register_parameter('zero', None)
|
|
|
|
if bias:
|
|
self.bias = Parameter(shape=(self.out_features, ), dtype=dtype)
|
|
else:
|
|
self.register_parameter('bias', None)
|
|
|
|
self.tp_size = tp_size
|
|
self.tp_group = tp_group
|
|
|
|
def forward(self, x):
|
|
pre_quant_scale = self.pre_quant_scale.value if self.pre_quant_scale else None
|
|
zero = self.zero.value if self.zero else None
|
|
bias = self.bias.value if self.bias else None
|
|
|
|
x = weight_only_groupwise_quant_matmul(x, pre_quant_scale,
|
|
self.qweight.value,
|
|
self.scale.value, zero, bias,
|
|
self.quant_algo, self.group_size)
|
|
if self.tp_size > 1 and self.tp_group is not None:
|
|
x = allreduce(x, self.tp_group)
|
|
|
|
return x
|
|
|
|
|
|
class SmoothQuantMLP(Module):
|
|
|
|
def __init__(self,
|
|
hidden_size,
|
|
ffn_hidden_size,
|
|
hidden_act,
|
|
bias=True,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1,
|
|
quant_mode=QuantMode(0)):
|
|
super().__init__()
|
|
if hidden_act not in ACT2FN:
|
|
raise ValueError(
|
|
'unsupported activation function: {}'.format(hidden_act))
|
|
self.fc = SmoothQuantColumnLinear(hidden_size,
|
|
ffn_hidden_size,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
gather_output=False,
|
|
quant_mode=quant_mode)
|
|
|
|
self.proj = SmoothQuantRowLinear(ffn_hidden_size,
|
|
hidden_size,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
quant_mode=quant_mode)
|
|
|
|
self.hidden_act = hidden_act
|
|
self.quant_mode = quant_mode
|
|
|
|
if self.quant_mode.has_act_static_scaling():
|
|
self.quantization_scaling_factor = Parameter(shape=(1, ),
|
|
dtype='float32')
|
|
else:
|
|
self.register_parameter('quantization_scaling_factor', None)
|
|
|
|
def forward(self, hidden_states):
|
|
inter = self.fc(hidden_states)
|
|
inter = ACT2FN[self.hidden_act](inter)
|
|
inter = inter / self.proj.smoother.value
|
|
if self.quant_mode.has_act_and_weight_quant():
|
|
if self.quant_mode.has_act_static_scaling():
|
|
# Avoid quantiztion layers as it breaks int8 plugins
|
|
inter = quantize_tensor(inter,
|
|
self.quantization_scaling_factor.value)
|
|
else:
|
|
# Quantize per token outputs tuple:
|
|
# quantized tensor and scaling factors per token
|
|
inter = quantize_per_token(inter)
|
|
output = self.proj(inter)
|
|
return output
|
|
|
|
|
|
class FP8RowLinear(RowLinear):
|
|
|
|
def __init__(self,
|
|
in_features,
|
|
out_features,
|
|
bias=True,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1):
|
|
super().__init__(in_features,
|
|
out_features,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size)
|
|
self.activation_scaling_factor = Parameter(shape=(1, ),
|
|
dtype=trt.float32)
|
|
self.weights_scaling_factor = Parameter(shape=(1, ), dtype=trt.float32)
|
|
|
|
def forward(self, x):
|
|
act_cast_out = cast(x, 'float32')
|
|
|
|
quantized_out = quantize(act_cast_out,
|
|
self.activation_scaling_factor.value, 'fp8')
|
|
dequantized_out = dequantize(quantized_out,
|
|
self.activation_scaling_factor.value)
|
|
|
|
w_cast_out = cast(self.weight.value, 'float32')
|
|
|
|
w_quant_out = quantize(w_cast_out, self.weights_scaling_factor.value,
|
|
'fp8')
|
|
w_deq_out = dequantize(w_quant_out, self.weights_scaling_factor.value)
|
|
|
|
return self.multiply_reduce(dequantized_out, w_deq_out, False)
|
|
|
|
|
|
class Int8SmoothQuantRowLinear(RowLinear):
|
|
|
|
def __init__(self,
|
|
in_features,
|
|
out_features,
|
|
bias=True,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1):
|
|
super().__init__(in_features,
|
|
out_features,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size)
|
|
self.activation_scaling_factor = Parameter(shape=(1, ),
|
|
dtype=trt.float32)
|
|
self.weights_scaling_factor = Parameter(shape=(self.out_features, ),
|
|
dtype=trt.float32)
|
|
self.prequant_scaling_factor = Parameter(shape=(self.in_features, ),
|
|
dtype=dtype)
|
|
|
|
def forward(self, x):
|
|
|
|
act_cast_out = cast(x, 'float32')
|
|
|
|
scaled_act_cast_out = mul(act_cast_out,
|
|
self.prequant_scaling_factor.value)
|
|
|
|
quantized_out = quantize(scaled_act_cast_out,
|
|
self.activation_scaling_factor.value, 'int8')
|
|
|
|
dequantized_out = dequantize(quantized_out,
|
|
self.activation_scaling_factor.value)
|
|
|
|
w_cast_out = cast(self.weight.value, 'float32')
|
|
|
|
w_quant_out = quantize(w_cast_out,
|
|
self.weights_scaling_factor.value,
|
|
'int8',
|
|
axis=0)
|
|
w_deq_out = dequantize(w_quant_out,
|
|
self.weights_scaling_factor.value,
|
|
axis=0)
|
|
|
|
return super().multiply_reduce(dequantized_out, w_deq_out, False)
|
|
|
|
|
|
class Int8SmoothQuantLinear(Linear):
|
|
|
|
def __init__(self,
|
|
in_features,
|
|
out_features,
|
|
bias=True,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1,
|
|
gather_output=True):
|
|
super().__init__(in_features,
|
|
out_features,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
gather_output=gather_output)
|
|
self.activation_scaling_factor = Parameter(shape=(1, ),
|
|
dtype=trt.float32)
|
|
|
|
self.weights_scaling_factor = Parameter(shape=(self.out_features, ),
|
|
dtype=trt.float32)
|
|
self.prequant_scaling_factor = Parameter(shape=(self.in_features, ),
|
|
dtype=dtype)
|
|
|
|
def forward(self, x):
|
|
act_cast_out = cast(x, 'float32')
|
|
scaled_act_cast_out = mul(act_cast_out,
|
|
self.prequant_scaling_factor.value)
|
|
|
|
quantized_out = quantize(scaled_act_cast_out,
|
|
self.activation_scaling_factor.value, 'int8')
|
|
dequantized_out = dequantize(quantized_out,
|
|
self.activation_scaling_factor.value)
|
|
|
|
w_cast_out = cast(self.weight.value, 'float32')
|
|
w_quant_out = quantize(w_cast_out,
|
|
self.weights_scaling_factor.value,
|
|
'int8',
|
|
axis=0)
|
|
w_deq_out = dequantize(w_quant_out,
|
|
self.weights_scaling_factor.value,
|
|
axis=0)
|
|
|
|
return super().multiply_gather(dequantized_out, w_deq_out, False)
|
|
|
|
|
|
class FP8Linear(Linear):
|
|
|
|
def __init__(self,
|
|
in_features,
|
|
out_features,
|
|
bias=True,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1,
|
|
gather_output=True):
|
|
super().__init__(in_features,
|
|
out_features,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
gather_output=gather_output)
|
|
self.activation_scaling_factor = Parameter(shape=(1, ),
|
|
dtype=trt.float32)
|
|
self.weights_scaling_factor = Parameter(shape=(1, ), dtype=trt.float32)
|
|
|
|
def forward(self, x):
|
|
act_cast_out = cast(x, 'float32')
|
|
quantized_out = quantize(act_cast_out,
|
|
self.activation_scaling_factor.value, 'fp8')
|
|
dequantized_out = dequantize(quantized_out,
|
|
self.activation_scaling_factor.value)
|
|
|
|
w_cast_out = cast(self.weight.value, 'float32')
|
|
w_quant_out = quantize(w_cast_out, self.weights_scaling_factor.value,
|
|
'fp8')
|
|
w_deq_out = dequantize(w_quant_out, self.weights_scaling_factor.value)
|
|
|
|
return self.multiply_gather(dequantized_out, w_deq_out, False)
|
|
|
|
|
|
class SmoothQuantGatedMLP(SmoothQuantMLP):
|
|
|
|
def __init__(self,
|
|
hidden_size,
|
|
ffn_hidden_size,
|
|
hidden_act,
|
|
bias=True,
|
|
dtype=None,
|
|
tp_group=None,
|
|
tp_size=1,
|
|
quant_mode=QuantMode(0)):
|
|
super().__init__(hidden_size,
|
|
ffn_hidden_size,
|
|
hidden_act,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
quant_mode=quant_mode)
|
|
if hidden_act not in ACT2FN:
|
|
raise ValueError(
|
|
'unsupported activation function: {}'.format(hidden_act))
|
|
self.gate = SmoothQuantColumnLinear(hidden_size,
|
|
ffn_hidden_size,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
gather_output=False,
|
|
quant_mode=quant_mode)
|
|
|
|
if self.quant_mode.has_act_static_scaling():
|
|
self.quantization_scaling_factor = Parameter(shape=(1, ),
|
|
dtype='float32')
|
|
else:
|
|
self.register_parameter('quantization_scaling_factor', None)
|
|
|
|
def forward(self, hidden_states):
|
|
inter = self.fc(hidden_states)
|
|
inter = ACT2FN[self.hidden_act](inter)
|
|
gate = self.gate(hidden_states)
|
|
inter_x_gate = inter * gate
|
|
inter_x_gate = inter_x_gate / self.proj.smoother.value
|
|
if self.quant_mode.has_act_and_weight_quant():
|
|
if self.quant_mode.has_act_static_scaling():
|
|
# Avoid quantiztion layers as it breaks int8 plugins
|
|
inter_x_gate = quantize_tensor(
|
|
inter_x_gate, self.quantization_scaling_factor.value)
|
|
else:
|
|
# Quantize per token outputs tuple:
|
|
# quantized tensor and scaling factors per token
|
|
inter_x_gate = quantize_per_token(inter_x_gate)
|
|
|
|
output = self.proj(inter_x_gate)
|
|
return output
|
|
|
|
|
|
class SmoothQuantAttention(Module):
|
|
|
|
def __init__(self,
|
|
hidden_size,
|
|
num_attention_heads,
|
|
num_kv_heads=None,
|
|
max_position_embeddings=1024,
|
|
num_layers=1,
|
|
apply_query_key_layer_scaling=False,
|
|
attention_mask_type=AttentionMaskType.padding,
|
|
bias=True,
|
|
dtype=None,
|
|
position_embedding_type=PositionEmbeddingType.learned_absolute,
|
|
tp_group=None,
|
|
tp_size=1,
|
|
multi_block_mode=False,
|
|
paged_kv_cache=False,
|
|
quant_mode=QuantMode(0)):
|
|
super().__init__()
|
|
self.attention_mask_type = attention_mask_type
|
|
self.attention_head_size = hidden_size // num_attention_heads
|
|
self.num_attention_heads = num_attention_heads // tp_size
|
|
self.num_kv_heads = (
|
|
num_kv_heads + tp_size - 1
|
|
) // tp_size if num_kv_heads is not None else self.num_attention_heads
|
|
self.hidden_size = hidden_size // tp_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
|
|
self.num_layers = num_layers
|
|
self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
|
|
self.norm_factor = math.sqrt(self.attention_head_size)
|
|
self.q_scaling = 1
|
|
if self.apply_query_key_layer_scaling:
|
|
self.norm_factor *= self.num_layers
|
|
self.q_scaling *= self.num_layers
|
|
|
|
self.position_embedding_type = position_embedding_type
|
|
self.multi_block_mode = multi_block_mode
|
|
self.paged_kv_cache = paged_kv_cache
|
|
|
|
self.rotary_embedding_dim = 0
|
|
if self.position_embedding_type.is_rope():
|
|
self.rotary_embedding_dim = hidden_size // num_attention_heads
|
|
|
|
self.quant_mode = quant_mode
|
|
self.dtype = dtype
|
|
|
|
if self.quant_mode.has_act_static_scaling():
|
|
self.quantization_scaling_factor = Parameter(shape=(1, ),
|
|
dtype='float32')
|
|
else:
|
|
self.register_parameter('quantization_scaling_factor', None)
|
|
|
|
qkv_quant_mode = quant_mode
|
|
if self.quant_mode.has_act_and_weight_quant():
|
|
# We need to hijack quant_mode for QKV because QKV always uses per channel scaling
|
|
qkv_quant_mode = QuantMode.from_description(
|
|
True, True, quant_mode.has_per_token_dynamic_scaling(), True)
|
|
|
|
if self.quant_mode.has_int8_kv_cache():
|
|
self.kv_orig_quant_scale = Parameter(shape=(1, ), dtype='float32')
|
|
self.kv_quant_orig_scale = Parameter(shape=(1, ), dtype='float32')
|
|
else:
|
|
self.register_parameter('kv_orig_quant_scale', None)
|
|
self.register_parameter('kv_quant_orig_scale', None)
|
|
|
|
self.qkv = SmoothQuantColumnLinear(
|
|
hidden_size,
|
|
hidden_size +
|
|
2 * self.num_kv_heads * tp_size * self.attention_head_size,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
gather_output=False,
|
|
quant_mode=qkv_quant_mode)
|
|
|
|
self.dense = SmoothQuantRowLinear(hidden_size,
|
|
hidden_size,
|
|
bias=bias,
|
|
dtype=dtype,
|
|
tp_group=tp_group,
|
|
tp_size=tp_size,
|
|
quant_mode=quant_mode)
|
|
|
|
def forward(self,
|
|
hidden_states: Tensor,
|
|
attention_mask=None,
|
|
past_key_value=None,
|
|
sequence_length=None,
|
|
host_past_key_value_lengths=None,
|
|
use_cache=False,
|
|
cache_indirection=None,
|
|
kv_cache_block_pointers=None,
|
|
context_lengths=None,
|
|
host_context_lengths=None,
|
|
host_request_types=None,
|
|
max_context_length=None):
|
|
# TODO(nkorobov) add in-flight batching to SmoothQuant
|
|
if default_net().plugin_config.smooth_quant_gemm_plugin:
|
|
qkv = self.qkv(hidden_states)
|
|
else:
|
|
raise ValueError("smooth_quant_gemm_plugin is not set")
|
|
if not default_net().plugin_config.gpt_attention_plugin:
|
|
raise ValueError("gpt_attention_plugin is not set")
|
|
|
|
if default_net().plugin_config.gpt_attention_plugin:
|
|
assert sequence_length is not None
|
|
assert host_past_key_value_lengths is not None
|
|
assert self.attention_mask_type == AttentionMaskType.causal, \
|
|
'Plugin only support masked MHA.'
|
|
assert context_lengths is not None
|
|
assert host_request_types is not None
|
|
if default_net().plugin_config.remove_input_padding:
|
|
assert host_context_lengths is not None
|
|
|
|
kv_quant_scale = self.kv_orig_quant_scale.value if self.quant_mode.has_int8_kv_cache(
|
|
) else None
|
|
kv_dequant_scale = self.kv_quant_orig_scale.value if self.quant_mode.has_int8_kv_cache(
|
|
) else None
|
|
context, past_key_value = gpt_attention(
|
|
qkv,
|
|
past_key_value,
|
|
sequence_length,
|
|
host_past_key_value_lengths,
|
|
context_lengths,
|
|
cache_indirection,
|
|
host_request_types,
|
|
self.num_attention_heads,
|
|
self.num_kv_heads,
|
|
self.q_scaling,
|
|
self.rotary_embedding_dim,
|
|
self.position_embedding_type,
|
|
self.multi_block_mode,
|
|
kv_quant_scale,
|
|
kv_dequant_scale,
|
|
self.quant_mode,
|
|
max_context_length,
|
|
kv_cache_block_pointers=kv_cache_block_pointers,
|
|
host_context_lengths=host_context_lengths)
|
|
else:
|
|
assert self.paged_kv_cache == False
|
|
|
|
def transpose_for_scores(x):
|
|
new_x_shape = concat([
|
|
shape(x, 0),
|
|
shape(x, 1), self.num_attention_heads,
|
|
self.attention_head_size
|
|
])
|
|
return x.view(new_x_shape).permute([0, 2, 1, 3])
|
|
|
|
query, key, value = split(qkv, self.hidden_size, dim=2)
|
|
query = transpose_for_scores(query)
|
|
key = transpose_for_scores(key)
|
|
value = transpose_for_scores(value)
|
|
|
|
if past_key_value is not None:
|
|
|
|
def dequantize_tensor(x, scale):
|
|
# Cast from int8 to dtype
|
|
casted_x = cast(x, self.dtype)
|
|
return casted_x * scale
|
|
|
|
if self.quant_mode.has_int8_kv_cache():
|
|
past_key_value = dequantize_tensor(
|
|
past_key_value, self.kv_dequantization_scale.value)
|
|
|
|
# past_key_value [bs, 2, num_heads, max_seq_len, head_dim]
|
|
past_key, past_value = split(past_key_value, 1, dim=1)
|
|
|
|
key_shape = concat([
|
|
shape(past_key, 0),
|
|
shape(past_key, 2),
|
|
shape(past_key, 3),
|
|
shape(past_key, 4)
|
|
])
|
|
past_key = past_key.view(key_shape, zero_is_placeholder=False)
|
|
past_value = past_value.view(key_shape,
|
|
zero_is_placeholder=False)
|
|
key = concat([past_key, key], dim=2)
|
|
value = concat([past_value, value], dim=2)
|
|
|
|
def merge_caches():
|
|
key_inflated_shape = concat([
|
|
shape(key, 0), 1,
|
|
shape(key, 1),
|
|
shape(key, 2),
|
|
shape(key, 3)
|
|
])
|
|
inflated_key = key.view(key_inflated_shape,
|
|
zero_is_placeholder=False)
|
|
inflated_value = value.view(key_inflated_shape,
|
|
zero_is_placeholder=False)
|
|
past_key_value = concat([inflated_key, inflated_value], dim=1)
|
|
return past_key_value
|
|
|
|
if self.attention_mask_type == AttentionMaskType.causal:
|
|
query_length = shape(query, 2)
|
|
key_length = shape(key, 2)
|
|
starts = concat([0, 0, key_length - query_length, 0])
|
|
sizes = concat([1, 1, query_length, key_length])
|
|
buffer = constant(
|
|
np.expand_dims(
|
|
np.tril(
|
|
np.ones(
|
|
(self.max_position_embeddings,
|
|
self.max_position_embeddings))).astype(bool),
|
|
(0, 1)))
|
|
causal_mask = slice(buffer, starts, sizes)
|
|
|
|
key = key.permute([0, 1, 3, 2])
|
|
with precision('float32'):
|
|
attention_scores = matmul(cast(query, 'float32'),
|
|
cast(key, 'float32'))
|
|
|
|
if self.attention_mask_type == AttentionMaskType.causal:
|
|
attention_scores = where(causal_mask, attention_scores,
|
|
-10000.0)
|
|
|
|
attention_scores = attention_scores / self.norm_factor
|
|
attention_probs = softmax(attention_scores, dim=-1)
|
|
|
|
context = matmul(attention_probs, value).permute([0, 2, 1, 3])
|
|
context = context.view(
|
|
concat([shape(context, 0),
|
|
shape(context, 1), self.hidden_size]))
|
|
|
|
past_key_value = merge_caches()
|
|
|
|
if use_cache and self.quant_mode.has_int8_kv_cache():
|
|
past_key_value = quantize_tensor(
|
|
past_key_value, self.kv_quantization_scale.value)
|
|
|
|
context = context / self.dense.smoother.value
|
|
if self.quant_mode.has_act_and_weight_quant():
|
|
if self.quant_mode.has_act_static_scaling():
|
|
# Avoid quantiztion layers as it breaks int8 plugins
|
|
context = quantize_tensor(
|
|
context, self.quantization_scaling_factor.value)
|
|
else:
|
|
# Quantize per token outputs tuple:
|
|
# quantized tensor and scaling factors per token
|
|
context = quantize_per_token(context)
|
|
|
|
context = self.dense(context)
|
|
|
|
if use_cache:
|
|
return (context, past_key_value)
|
|
|
|
return context
|