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# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional, Tuple, Union
import numpy as np
import tensorrt as trt
from .._common import default_net, default_trtnet
from .._utils import str_dtype_to_np, str_dtype_to_trt
from ..functional import (Tensor, _add_plugin_info, _create_tensor, cast, clip,
constant, matmul, repeat_interleave, round)
from ..plugin import TRT_LLM_PLUGIN_NAMESPACE
from .mode import QuantMode
def smooth_quant_gemm(input: Tensor, weights: Tensor, scales_a: Tensor,
scales_b: Tensor, per_token_scaling: bool,
per_channel_scaling: bool) -> Tensor:
if not default_net().plugin_config.smooth_quant_gemm_plugin:
raise TypeError("Smooth Quant GEMM is only supported with plugin")
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'SmoothQuantGemm', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
per_channel_scaling = 1 if per_channel_scaling else 0
per_channel_scaling = trt.PluginField(
"has_per_channel_scaling",
np.array(per_channel_scaling, dtype=np.int32),
trt.PluginFieldType.INT32)
per_token_scaling = 1 if per_token_scaling else 0
per_token_scaling = trt.PluginField(
"has_per_token_scaling", np.array(per_token_scaling,
dtype=np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net().plugin_config.smooth_quant_gemm_plugin
pf_type = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection(
[per_channel_scaling, per_token_scaling, pf_type])
gemm_plug = plg_creator.create_plugin("sq_gemm", pfc)
plug_inputs = [
input.trt_tensor, weights.trt_tensor, scales_a.trt_tensor,
scales_b.trt_tensor
]
layer = default_trtnet().add_plugin_v2(plug_inputs, gemm_plug)
_add_plugin_info(layer, plg_creator, "sq_gemm", pfc)
if not default_net().strongly_typed:
layer.get_input(0).set_dynamic_range(-127, 127)
layer.get_input(1).set_dynamic_range(-127, 127)
return _create_tensor(layer.get_output(0), layer)
def fp8_rowwise_gemm(input: Tensor, weights: Tensor, scales_a: Tensor,
scales_b: Tensor, per_token_scaling: bool,
per_channel_scaling: bool) -> Tensor:
if not default_net().plugin_config.fp8_rowwise_gemm_plugin:
raise TypeError("Fp8 Rowwise GEMM is only supported with plugin")
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'Fp8RowwiseGemm', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
per_channel_scaling = 1 if per_channel_scaling else 0
per_channel_scaling = trt.PluginField(
"has_per_channel_scaling",
np.array(per_channel_scaling, dtype=np.int32),
trt.PluginFieldType.INT32)
per_token_scaling = 1 if per_token_scaling else 0
per_token_scaling = trt.PluginField(
"has_per_token_scaling", np.array(per_token_scaling,
dtype=np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net().plugin_config.fp8_rowwise_gemm_plugin
pf_type = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection(
[per_channel_scaling, per_token_scaling, pf_type])
gemm_plug = plg_creator.create_plugin("fp8_rowwise_gemm", pfc)
plug_inputs = [
input.trt_tensor, weights.trt_tensor, scales_a.trt_tensor,
scales_b.trt_tensor
]
layer = default_trtnet().add_plugin_v2(plug_inputs, gemm_plug)
_add_plugin_info(layer, plg_creator, "fp8_rowwise_gemm", pfc)
if not default_net().strongly_typed:
layer.get_input(0).set_dynamic_range(-448, 448)
layer.get_input(1).set_dynamic_range(-448, 448)
return _create_tensor(layer.get_output(0), layer)
def weight_only_quant_matmul(input: Tensor,
weights: Tensor,
scales: Tensor,
weightTypeId: int,
dtype: str = 'float16',
transa: bool = False,
transb: bool = False) -> Tensor:
if not default_net(
).plugin_config.weight_only_quant_matmul_plugin or transa or transb:
scale_axis = 0 if transb else 1
if weights.dtype != trt.int8:
# Q->DQ
weights = quantize(weights, scales, dtype='int8', axis=1)
weights = dequantize(weights, scales, scale_axis, input.dtype)
else:
weights = dequantize(weights, scales, scale_axis, input.dtype)
res = matmul(input, weights, transa=transa, transb=transb)
return cast(res, dtype)
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'WeightOnlyQuantMatmul', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
weight_type_id = trt.PluginField("weight_type_id",
np.array(weightTypeId, dtype=np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net().plugin_config.weight_only_quant_matmul_plugin
pf_type = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection([pf_type, weight_type_id])
matmul_plug = plg_creator.create_plugin("woq_matmul", pfc)
plug_inputs = [input.trt_tensor, weights.trt_tensor, scales.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, matmul_plug)
_add_plugin_info(layer, plg_creator, "woq_matmul", pfc)
if not default_net().strongly_typed:
layer.get_input(1).set_dynamic_range(-127, 127)
return _create_tensor(layer.get_output(0), layer)
def weight_only_groupwise_quant_matmul(input: Tensor,
pre_quant_scale: Tensor,
weights: Tensor,
scales: Tensor,
zeros: Tensor,
biases: Tensor,
alpha: Tensor,
quant_algo: int,
group_size: int,
dtype: str = 'float16') -> Tensor:
if not default_net(
).plugin_config.weight_only_groupwise_quant_matmul_plugin:
scales = repeat_interleave(scales, group_size, 0)
weights = quantize(weights, scales, dtype='int8', axis=1)
weights = dequantize(weights, scales, 1, input.dtype)
if quant_algo & 8:
# fp8_alpha
input = input * alpha
if quant_algo & 4:
# pre quant
input = input * pre_quant_scale
elif quant_algo & 2:
# zero
zeros = repeat_interleave(zeros, group_size, 0)
weights += zeros
res = matmul(input, weights)
if quant_algo & 1:
# bias
res += biases
return cast(res, dtype)
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'WeightOnlyGroupwiseQuantMatmul', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
quant_algo_ = trt.PluginField("quant_algo",
np.array(quant_algo, dtype=np.int32),
trt.PluginFieldType.INT32)
group_size_ = trt.PluginField("group_size",
np.array(group_size, dtype=np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net(
).plugin_config.weight_only_groupwise_quant_matmul_plugin
pf_type_ = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection([pf_type_, quant_algo_, group_size_])
matmul_plug = plg_creator.create_plugin("woq_groupwise_matmul", pfc)
# quant_algo = fp8_alpha * 8 + pre_quant_scale * 4 + zero * 2 + bias
plug_inputs = [input.trt_tensor]
# Flags for indicating whether the corresponding inputs are applied in quant_algo
# quant_algo = fp8_alpha * FP8_ALPHA + pre_quant_scale * PRE_QUANT_SCALE + zero * ZERO + bias * BIAS
# Here pre_quant_scale, zero and bias are boolean type
BIAS = 1
ZERO = 2
PRE_QUANT_SCALE = 4
FP8_ALPHA = 8
if quant_algo & PRE_QUANT_SCALE:
plug_inputs += [pre_quant_scale.trt_tensor]
plug_inputs += [weights.trt_tensor, scales.trt_tensor]
if quant_algo & ZERO:
plug_inputs += [zeros.trt_tensor]
if quant_algo & BIAS:
plug_inputs += [biases.trt_tensor]
if quant_algo & FP8_ALPHA:
plug_inputs += [alpha.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, matmul_plug)
_add_plugin_info(layer, plg_creator, "woq_groupwise_matmul", pfc)
return _create_tensor(layer.get_output(0), layer)
def smooth_quant_layer_norm(input: Tensor,
normalized_shape: Union[int, Tuple[int]],
weight: Optional[Tensor] = None,
bias: Optional[Tensor] = None,
scale: Optional[Tensor] = None,
eps: float = 1e-05,
use_diff_of_squares: bool = True,
dynamic_act_scaling: bool = False) -> Tensor:
if not default_net().plugin_config.layernorm_quantization_plugin:
raise TypeError("Smooth Quant Layer Norm is only supported with plugin")
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'LayernormQuantization', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
eps = trt.PluginField("eps", np.array(eps, dtype=np.float32),
trt.PluginFieldType.FLOAT32)
use_diff_of_squares = trt.PluginField(
"use_diff_of_squares",
np.array([int(use_diff_of_squares)], dtype=np.int32),
trt.PluginFieldType.INT32)
dyn_act_scaling = trt.PluginField(
"dyn_act_scaling", np.array([int(dynamic_act_scaling)], np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net().plugin_config.layernorm_quantization_plugin
pf_type = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection(
[eps, use_diff_of_squares, dyn_act_scaling, pf_type])
layernorm_plug = plg_creator.create_plugin("layernorm_quantized", pfc)
normalized_shape = [normalized_shape] if isinstance(
normalized_shape, int) else normalized_shape
if weight is None:
weight = constant(
np.ones(normalized_shape, dtype=str_dtype_to_np(p_dtype)))
if bias is None:
bias = constant(
np.zeros(normalized_shape, dtype=str_dtype_to_np(p_dtype)))
# LayerNorm plugin only supports float32 scale
scale = cast(scale, "float32")
plug_inputs = [
input.trt_tensor, weight.trt_tensor, bias.trt_tensor,
scale.trt_tensor
]
layer = default_trtnet().add_plugin_v2(plug_inputs, layernorm_plug)
if not default_net().strongly_typed:
layer.get_output(0).set_dynamic_range(-127, 127)
_add_plugin_info(layer, plg_creator, "layernorm_quantized", pfc)
if not dynamic_act_scaling:
return _create_tensor(layer.get_output(0), layer)
return _create_tensor(layer.get_output(0),
layer), _create_tensor(layer.get_output(1), layer)
def smooth_quant_rms_norm(input: Tensor,
normalized_shape: Union[int, Tuple[int]],
weight: Optional[Tensor] = None,
bias: Optional[Tensor] = None,
scale: Optional[Tensor] = None,
clamp_val: Optional[Tensor] = None,
eps: float = 1e-05,
dynamic_act_scaling: bool = False) -> Tensor:
if not default_net().plugin_config.rmsnorm_quantization_plugin:
raise TypeError("Smooth Quant Rms Norm is only supported with plugin")
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'RmsnormQuantization', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
output_type = trt.PluginField("out_type_id",
np.array([int(trt.int8)], np.int32),
trt.PluginFieldType.INT32)
quant_mode = trt.PluginField(
"quant_mode",
np.array([int(QuantMode.use_smooth_quant(per_token=True))],
np.int32), trt.PluginFieldType.INT32)
clamp_enabled = trt.PluginField(
"clamp_enabled", np.array([clamp_val is not None], np.int32),
trt.PluginFieldType.INT32)
eps = trt.PluginField("eps", np.array(eps, dtype=np.float32),
trt.PluginFieldType.FLOAT32)
dyn_act_scaling = trt.PluginField(
"dyn_act_scaling", np.array([int(dynamic_act_scaling)], np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net().plugin_config.rmsnorm_quantization_plugin
pf_type = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection([
eps, dyn_act_scaling, clamp_enabled, quant_mode, pf_type,
output_type
])
rmsnorm_plug = plg_creator.create_plugin("rmsnorm_quantized", pfc)
normalized_shape = [normalized_shape] if isinstance(
normalized_shape, int) else normalized_shape
if weight is None:
weight = constant(
np.ones(normalized_shape, dtype=str_dtype_to_np(p_dtype)))
if bias is None:
bias = constant(
np.zeros(normalized_shape, dtype=str_dtype_to_np(p_dtype)))
# RMS Norm Plugin only supports float32 scale
scale = cast(scale, "float32")
plug_inputs = [
input.trt_tensor, weight.trt_tensor, bias.trt_tensor,
scale.trt_tensor
]
if clamp_val:
plug_inputs += [clamp_val.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, rmsnorm_plug)
if not default_net().strongly_typed:
layer.get_output(0).set_dynamic_range(-127, 127)
_add_plugin_info(layer, plg_creator, "rmsnorm_quantized", pfc)
if not dynamic_act_scaling:
return _create_tensor(layer.get_output(0), layer)
return _create_tensor(layer.get_output(0),
layer), _create_tensor(layer.get_output(1), layer)
def fp8_rowwise_rms_norm(input: Tensor,
normalized_shape: Union[int, Tuple[int]],
weight: Optional[Tensor] = None,
bias: Optional[Tensor] = None,
scale: Optional[Tensor] = None,
clamp_val: Optional[Tensor] = None,
eps: float = 1e-05,
dynamic_act_scaling: bool = True) -> Tensor:
if not default_net().plugin_config.rmsnorm_quantization_plugin:
raise TypeError("Fp8 Rowwise Rms Norm is only supported with plugin")
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'RmsnormQuantization', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
output_type = trt.PluginField("out_type_id",
np.array([int(trt.fp8)], np.int32),
trt.PluginFieldType.INT32)
quant_mode = trt.PluginField(
"quant_mode",
np.array([int(QuantMode.from_description(use_fp8_rowwise=True))],
np.int32), trt.PluginFieldType.INT32)
clamp_enabled = trt.PluginField(
"clamp_enabled", np.array([clamp_val is not None], np.int32),
trt.PluginFieldType.INT32)
eps = trt.PluginField("eps", np.array(eps, dtype=np.float32),
trt.PluginFieldType.FLOAT32)
dyn_act_scaling = trt.PluginField(
"dyn_act_scaling", np.array([int(dynamic_act_scaling)], np.int32),
trt.PluginFieldType.INT32)
p_dtype = default_net().plugin_config.rmsnorm_quantization_plugin
pf_type = trt.PluginField(
"type_id", np.array([int(str_dtype_to_trt(p_dtype))], np.int32),
trt.PluginFieldType.INT32)
pfc = trt.PluginFieldCollection([
eps, dyn_act_scaling, clamp_enabled, quant_mode, pf_type,
output_type
])
rmsnorm_plug = plg_creator.create_plugin("rmsnorm_quantized", pfc)
normalized_shape = [normalized_shape] if isinstance(
normalized_shape, int) else normalized_shape
if weight is None:
weight = constant(
np.ones(normalized_shape, dtype=str_dtype_to_np(p_dtype)))
if bias is None:
bias = constant(
np.zeros(normalized_shape, dtype=str_dtype_to_np(p_dtype)))
if scale is None:
scale = constant(np.ones((1, ), dtype=str_dtype_to_np(p_dtype)))
# RMS Norm Plugin only supports float32 scale
scale = cast(scale, "float32")
plug_inputs = [
input.trt_tensor, weight.trt_tensor, bias.trt_tensor,
scale.trt_tensor
]
if clamp_val:
plug_inputs += [clamp_val.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, rmsnorm_plug)
if not default_net().strongly_typed:
layer.get_output(0).set_dynamic_range(-448, 448)
_add_plugin_info(layer, plg_creator, "rmsnorm_quantized", pfc)
if not dynamic_act_scaling:
return _create_tensor(layer.get_output(0), layer)
return _create_tensor(layer.get_output(0),
layer), _create_tensor(layer.get_output(1), layer)
def quantize(input: Tensor,
scale_factor: Tensor,
dtype: str,
axis: int = -1) -> Tensor:
layer = default_trtnet().add_quantize(input.trt_tensor,
scale_factor.trt_tensor,
str_dtype_to_trt(dtype))
layer.axis = axis
output = _create_tensor(layer.get_output(0), layer)
return output
def dequantize(input: Tensor,
scale_factor: Tensor,
axis: int = -1,
output_type: Union[str, trt.DataType] = 'float16') -> Tensor:
if isinstance(output_type, str):
output_type = str_dtype_to_trt(output_type)
layer = default_trtnet().add_dequantize(input.trt_tensor,
scale_factor.trt_tensor,
output_type)
layer.axis = axis
if not default_net().strongly_typed:
layer.precision = input.dtype
output = _create_tensor(layer.get_output(0), layer)
return output
def quantize_per_token(x: Tensor,
clamp_val: Optional[Tensor] = None) -> Tuple[Tensor]:
if not default_net().plugin_config.quantize_per_token_plugin:
x = cast(x, 'float32')
xmax = x.abs().max(-1, keepdim=True)
scale = xmax / 127.0
out = x * 127.0 / xmax
out = round(out)
out = clip(out, -128, 127)
quantized_out = cast(out, 'int8')
return quantized_out, scale
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'QuantizePerToken', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
output_type = trt.PluginField("type_id",
np.array([int(trt.int8)], np.int32),
trt.PluginFieldType.INT32)
quant_mode = trt.PluginField(
"quant_mode",
np.array([int(QuantMode.use_smooth_quant(per_token=True))],
np.int32), trt.PluginFieldType.INT32)
clamp_enabled = trt.PluginField(
"clamp_enabled", np.array([clamp_val is not None], np.int8),
trt.PluginFieldType.INT8)
pfc = trt.PluginFieldCollection(
[output_type, quant_mode, clamp_enabled])
quantize_plug = plg_creator.create_plugin("quantize_per_token_plugin",
pfc)
plug_inputs = [x.trt_tensor]
if clamp_val:
plug_inputs += [clamp_val.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, quantize_plug)
if not default_net().strongly_typed:
layer.get_output(0).set_dynamic_range(-127, 127)
_add_plugin_info(layer, plg_creator, "quantize_per_token_plugin", pfc)
quantized = _create_tensor(layer.get_output(0), layer)
scales = _create_tensor(layer.get_output(1), layer)
return quantized, scales
def quantize_fp8_per_token(x: Tensor,
clamp_val: Optional[Tensor] = None) -> Tuple[Tensor]:
if not default_net().plugin_config.quantize_per_token_plugin:
x = cast(x, 'float32')
xmax = x.abs().max(-1, keepdim=True)
scale = xmax / 448.0
out = x * 448.0 / xmax
out = round(out)
out = clip(out, -448, 448)
quantized_out = cast(out, 'fp8')
return quantized_out, scale
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'QuantizePerToken', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
output_type = trt.PluginField("type_id",
np.array([int(trt.fp8)], np.int32),
trt.PluginFieldType.INT32)
quant_mode = trt.PluginField(
"quant_mode",
np.array([int(QuantMode.from_description(use_fp8_rowwise=True))],
np.int32), trt.PluginFieldType.INT32)
clamp_enabled = trt.PluginField(
"clamp_enabled", np.array([clamp_val is not None], np.int8),
trt.PluginFieldType.INT8)
pfc = trt.PluginFieldCollection(
[output_type, quant_mode, clamp_enabled])
quantize_plug = plg_creator.create_plugin("quantize_per_token_plugin",
pfc)
plug_inputs = [x.trt_tensor]
if clamp_val:
plug_inputs += [clamp_val.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, quantize_plug)
if not default_net().strongly_typed:
layer.get_output(0).set_dynamic_range(-448, 448)
_add_plugin_info(layer, plg_creator, "quantize_per_token_plugin", pfc)
quantized = _create_tensor(layer.get_output(0), layer)
scales = _create_tensor(layer.get_output(1), layer)
return quantized, scales
def quantize_tensor(x, scale):
if not default_net().plugin_config.quantize_tensor_plugin:
scaled = x * scale
rounded = round(scaled)
clipped = clip(rounded, -128, 127)
quantized = cast(clipped, 'int8')
else:
plg_creator = trt.get_plugin_registry().get_plugin_creator(
'QuantizeTensor', '1', TRT_LLM_PLUGIN_NAMESPACE)
assert plg_creator is not None
pfc = trt.PluginFieldCollection([])
quantize_plug = plg_creator.create_plugin("quantize_tensor_plugin", pfc)
plug_inputs = [x.trt_tensor, scale.trt_tensor]
layer = default_trtnet().add_plugin_v2(plug_inputs, quantize_plug)
if not default_net().strongly_typed:
layer.get_output(0).set_dynamic_range(-127, 127)
_add_plugin_info(layer, plg_creator, "quantize_tensor_plugin", pfc)
quantized = _create_tensor(layer.get_output(0), layer)
return quantized
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