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https://github.com/NVIDIA/TensorRT-LLM.git
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d8b408e6dc
* Update TensorRT-LLM --------- Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
233 lines
9.0 KiB
Python
233 lines
9.0 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 numpy as np
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from .._utils import trt_dtype_to_np
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from ..functional import ACT2FN
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from ..module import Module
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from ..quantization import QuantMode
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from ..quantization.layers import FP8Linear, FP8RowLinear
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from .linear import ColumnLinear, RowLinear
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class MLP(Module):
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def __init__(self,
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hidden_size,
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ffn_hidden_size,
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hidden_act,
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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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instance_id: int = 0):
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super().__init__()
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if hidden_act not in ACT2FN:
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raise ValueError(
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'unsupported activation function: {}'.format(hidden_act))
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fc_output_size = 2 * ffn_hidden_size if hidden_act == 'swiglu' else ffn_hidden_size
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self.use_fp8_qdq = quant_mode.has_fp8_qdq()
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if self.use_fp8_qdq:
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self.fc = FP8Linear(hidden_size,
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fc_output_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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gather_output=False)
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self.proj = FP8RowLinear(ffn_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.fc = ColumnLinear(hidden_size,
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fc_output_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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gather_output=False)
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self.proj = RowLinear(ffn_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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self.hidden_act = hidden_act
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self.dtype = dtype
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def forward(self, hidden_states, workspace=None):
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inter = self.fc(hidden_states)
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inter = ACT2FN[self.hidden_act](inter)
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output = self.proj(inter, workspace)
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return output
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class GatedMLP(MLP):
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def __init__(self,
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hidden_size,
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ffn_hidden_size,
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hidden_act,
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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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instance_id: int = 0):
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self.use_fp8_qdq = quant_mode.has_fp8_qdq()
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super().__init__(hidden_size,
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ffn_hidden_size,
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hidden_act,
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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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quant_mode=quant_mode,
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instance_id=instance_id)
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self.hidden_size = hidden_size
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self.ffn_hidden_size = ffn_hidden_size
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self.hidden_act = hidden_act
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self.bias = bias
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self.dtype = dtype
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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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self.instance_id = instance_id
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if self.use_fp8_qdq:
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self.gate = FP8Linear(hidden_size,
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ffn_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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gather_output=False)
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else:
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self.gate = ColumnLinear(hidden_size,
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ffn_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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gather_output=False)
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def forward(self, hidden_states, workspace=None):
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inter = self.fc(hidden_states)
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inter = ACT2FN[self.hidden_act](inter)
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gate = self.gate(hidden_states)
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output = self.proj(inter * gate, workspace)
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return output
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class FusedGatedMLP(GatedMLP):
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def __init__(self,
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hidden_size,
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ffn_hidden_size,
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hidden_act,
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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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instance_id: int = 0):
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super().__init__(hidden_size,
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ffn_hidden_size,
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hidden_act,
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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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quant_mode=quant_mode,
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instance_id=instance_id)
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def forward(self, hidden_states, workspace=None):
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# Combine the following pattern
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#
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# SiLU(FC(x)) + Gate(x)
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#
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# into:
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#
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# SwiGLU(FusedFC(x))
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#
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# Upside is we don't need to modify 4 different weight loading paths just to concat weights
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_np_dtype = trt_dtype_to_np(self.dtype)
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concat_weight = np.concatenate(
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[self.gate.weight.raw_value, self.fc.weight.raw_value],
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axis=0).astype(_np_dtype)
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if self.bias:
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concat_bias = np.concatenate(
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[self.gate.bias.raw_value, self.fc.bias.raw_value],
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axis=0).astype(_np_dtype)
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if self.use_fp8_qdq:
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gate_weights_scaling_factor = self.gate.weights_scaling_factor.raw_value
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fc_weights_scaling_factor = self.fc.weights_scaling_factor.raw_value
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fc_activation_scaling_factor = self.fc.activation_scaling_factor.raw_value
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gate_activation_scaling_factor = self.gate.activation_scaling_factor.raw_value
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assert fc_activation_scaling_factor == gate_activation_scaling_factor, "Activation scales should be identical"
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# Remove dangling TRT-LLM parameter references after the graph rewrite.
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for param, _ in list(self.gate.named_parameters()):
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self.gate._parameters.pop(param)
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self.gate = None
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if self.use_fp8_qdq:
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self.fc = FP8Linear(self.hidden_size,
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self.ffn_hidden_size * 2,
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bias=self.bias,
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dtype=self.dtype,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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gather_output=False)
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else:
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self.fc = ColumnLinear(self.hidden_size,
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self.ffn_hidden_size * 2,
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bias=self.bias,
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dtype=self.dtype,
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tp_group=self.tp_group,
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tp_size=self.tp_size,
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gather_output=False)
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self.fc.weight.value = concat_weight
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if self.use_fp8_qdq:
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self.fc.activation_scaling_factor.value = fc_activation_scaling_factor
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# TODO: need to align with quantization toolkit; preferably put a constraint to equalize
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# fc/gate weight scaling factor to allow horizontal fusion without accuracy loss
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self.fc.weights_scaling_factor.value = max(
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gate_weights_scaling_factor, fc_weights_scaling_factor)
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if self.bias:
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self.fc.bias.value = concat_bias
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inter = self.fc(hidden_states)
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if self.hidden_act == 'silu':
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inter = ACT2FN['swiglu'](inter)
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else:
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raise NotImplementedError(
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f"Activation {self.hidden_act} not yet implemented for FusedGatedMLP"
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)
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output = self.proj(inter, workspace)
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return output
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