Source code for tensorrt_llm.layers.mlp

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from typing import Optional

import tensorrt as trt

from .._common import default_net
from ..functional import (ACT2FN, AllReduceParams, cast, chunk, concat,
                          gemm_swiglu, is_gated_activation,
                          low_latency_gemm_swiglu)
from ..mapping import Mapping
from ..module import Module
from ..quantization import QuantMode
from ..quantization.functional import quantize
from ..quantization.layers import FP8Linear, FP8RowLinear
from .linear import ColumnLinear, RowLinear
from .lora import LoraRuntimeParams
from .normalization import LayerNorm


[docs] def fc_gate_lora(hidden_states, lora, fused_gate_up_lora, lora_layer_params): if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") mlp_gate_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate") mlp_gate_up_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate_up") if mlp_gate_up_lora_params is not None: assert fused_gate_up_lora is not None mlp_gate_up_lora = fused_gate_up_lora(hidden_states, mlp_gate_up_lora_params) return mlp_gate_up_lora elif mlp_fc_lora_params is not None and mlp_gate_lora_params is not None: mlp_in_lora_params = LoraRuntimeParams( lora_ranks=[ mlp_fc_lora_params.lora_ranks[0], mlp_gate_lora_params.lora_ranks[0] ], lora_weights_pointers=[ mlp_fc_lora_params.lora_weights_pointers[0], mlp_gate_lora_params.lora_weights_pointers[0] ], host_request_types=mlp_fc_lora_params.host_request_types, host_context_lengths=mlp_fc_lora_params.host_context_lengths) mlp_fc_lora, mlp_gate_lora = lora(hidden_states, mlp_in_lora_params) mlp_in_result = concat([mlp_gate_lora, mlp_fc_lora], dim=mlp_fc_lora.rank() - 1) return mlp_in_result return None
[docs] def fc_gate_dora(hidden_states, dora, fused_gate_up_dora, lora_layer_params): if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") mlp_gate_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate") mlp_gate_up_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate_up") if mlp_gate_up_lora_params is not None: assert fused_gate_up_dora is not None return fused_gate_up_dora(hidden_states, mlp_gate_up_lora_params) if mlp_fc_lora_params is not None and mlp_gate_lora_params is not None: mlp_in_lora_params = LoraRuntimeParams( lora_ranks=[ mlp_fc_lora_params.lora_ranks[0], mlp_gate_lora_params.lora_ranks[0] ], lora_weights_pointers=[ mlp_fc_lora_params.lora_weights_pointers[0], mlp_gate_lora_params.lora_weights_pointers[0] ], host_request_types=mlp_fc_lora_params.host_request_types, host_context_lengths=mlp_fc_lora_params.host_context_lengths) return dora(hidden_states, mlp_in_lora_params) return None
[docs] class MLP(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), inner_layernorm=False, eps=1e-05, is_expert=False, ): super().__init__() if hidden_act not in ACT2FN: raise ValueError( 'unsupported activation function: {}'.format(hidden_act)) fc_output_size = 2 * ffn_hidden_size if hidden_act in [ 'swiglu', 'gegelu' ] else ffn_hidden_size self.inner_layernorm = LayerNorm(ffn_hidden_size, dtype=dtype, eps=eps) if inner_layernorm else None self.fc = ColumnLinear(hidden_size, fc_output_size, bias=bias, dtype=dtype, tp_group=tp_group, tp_size=tp_size, gather_output=False) self.proj = RowLinear(ffn_hidden_size, hidden_size, bias=bias, dtype=dtype, tp_group=tp_group, tp_size=tp_size, is_expert=is_expert) self.hidden_size = hidden_size self.ffn_hidden_size = ffn_hidden_size self.hidden_act = hidden_act self.dtype = dtype self.bias = bias self.tp_group = tp_group self.tp_size = tp_size self.quant_mode = quant_mode self.eps = eps self.is_expert = is_expert # see optimize_model's add_lora for LoRA initialization self.lora = None self.dora = None
[docs] def forward(self, hidden_states, lora_layer_params=None, gegelu_limit=None): if lora_layer_params is not None: assert lora_layer_params.get_runtime_params( 0, "mlp_gate_up" ) is None, f"LoRA module 'mlp_gate_up' is not supported in {self}" if is_gated_activation(self.hidden_act): inter = self.fc(hidden_states) lora_result = fc_gate_lora(hidden_states, self.lora, None, lora_layer_params) if lora_result is not None: inter = inter + lora_result if self.dora is not None: inter = fc_gate_dora(inter, self.dora, self.fused_gate_up_dora, lora_layer_params) else: mlp_fc_lora_params = None if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") inter = self.fc(hidden_states, mlp_fc_lora_params) mlp_proj_lora_params = None if lora_layer_params is not None: mlp_proj_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_4h_to_h") if self.hidden_act == 'gegelu': inter = ACT2FN[self.hidden_act](inter, gegelu_limit) else: inter = ACT2FN[self.hidden_act](inter) if self.inner_layernorm is not None: inter = self.inner_layernorm(inter) output = self.proj(inter, lora_runtime_params=mlp_proj_lora_params) return output
[docs] class GatedMLP(MLP): def __init__( self, hidden_size, ffn_hidden_size, hidden_act, bias=True, dtype=None, tp_group=None, tp_size=1, quant_mode=QuantMode(0), inner_layernorm=False, eps=1e-05, is_expert=False, ): 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, inner_layernorm=inner_layernorm, eps=eps, is_expert=is_expert) self.hidden_size = hidden_size self.ffn_hidden_size = ffn_hidden_size self.tp_group = tp_group self.tp_size = tp_size self.gate = ColumnLinear(hidden_size, ffn_hidden_size, bias=bias, dtype=dtype, tp_group=tp_group, tp_size=tp_size, gather_output=False)
[docs] def forward(self, hidden_states, lora_layer_params=None, all_reduce_params: Optional[AllReduceParams] = None): if lora_layer_params is not None: assert lora_layer_params.get_runtime_params( 0, "mlp_gate_up" ) is None, f"LoRA module 'mlp_gate_up' is not supported in {self}" mlp_fc_lora_params = None if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") mlp_gate_lora_params = None if lora_layer_params is not None: mlp_gate_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate") mlp_proj_lora_params = None if lora_layer_params is not None: mlp_proj_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_4h_to_h") inter = self.fc(hidden_states, mlp_fc_lora_params) inter = ACT2FN[self.hidden_act](inter) gate = self.gate(hidden_states, mlp_gate_lora_params) intermediate = inter * gate if self.inner_layernorm is not None: intermediate = self.inner_layernorm(intermediate) output = self.proj(intermediate, lora_runtime_params=mlp_proj_lora_params, all_reduce_params=all_reduce_params) return output
[docs] class FusedGatedMLP(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), inner_layernorm=False, eps=1e-05, is_expert=False, ): super().__init__() self.hidden_size = hidden_size self.ffn_hidden_size = ffn_hidden_size self.hidden_act = hidden_act self.bias = bias self.dtype = dtype self.tp_group = tp_group self.tp_size = tp_size self.quant_mode = quant_mode self.fused_fc = ColumnLinear( self.hidden_size, self.ffn_hidden_size * 2, bias=self.bias, dtype=self.dtype, tp_group=self.tp_group, tp_size=self.tp_size, gather_output=False, ) self.inner_layernorm = LayerNorm(ffn_hidden_size, dtype=dtype, eps=eps) if inner_layernorm else None self.proj = RowLinear(ffn_hidden_size, hidden_size, bias=bias, dtype=dtype, tp_group=tp_group, tp_size=tp_size, is_expert=is_expert) # see optimize_model's add_lora for LoRA initialization self.lora = None # used for split up and gate proj self.fused_gate_up_lora = None # used for merged up_gate proj self.dora = None self.fused_gate_up_dora = None
[docs] def fc_gate_plugin(self, hidden_states, lora_layer_params=None): # Combine the following pattern # # SiLU(FC(x)) * Gate(x) # # into: # # SwiGLU(FusedFC(x)) if default_net( ).plugin_config.low_latency_gemm_swiglu_plugin is not None: p_dtype = default_net().plugin_config.low_latency_gemm_swiglu_plugin else: p_dtype = default_net().plugin_config.gemm_swiglu_plugin use_fp8 = p_dtype == 'fp8' assert use_fp8, "gemm_swiglu_plugin and low_latency_gemm_swiglu_plugin only supports fp8 now" if lora_layer_params is not None: mlp_fc_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_h_to_4h") mlp_gate_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_gate") if mlp_fc_lora_params is not None or mlp_gate_lora_params is not None: raise NotImplementedError( f"LoRA of splitting fc and gate is not yet implemented for gemm_swiglu_plugin" ) if self.hidden_act != 'silu': raise NotImplementedError( f"Activation {self.hidden_act} not yet implemented for gemm_swiglu_plugin" ) if self.bias: raise NotImplementedError( f"bias not yet implemented for gemm_swiglu_plugin fp8") assert isinstance( self.fused_fc, FP8Linear), "fp8 gemm_swiglu only supports fp8 weights" assert isinstance( self.proj, FP8RowLinear), "fp8 gemm_swiglu only supports fp8 weights" assert self.fused_fc.weight.shape == ( self.hidden_size, self.ffn_hidden_size * 2 // self.tp_size), "fp8 gemm_swiglu only supports (k, n) weights" scale_d0 = (self.fused_fc.weights_scaling_factor.raw_value.item() * self.fused_fc.activation_scaling_factor.raw_value.item()) scale_d1 = scale_d0 scale_output = 1.0 / self.proj.activation_scaling_factor.raw_value.item( ) activation_scaling_factor = cast( self.fused_fc.activation_scaling_factor.value, self.dtype) if hidden_states.dtype != trt.fp8: hidden_states = quantize(hidden_states, activation_scaling_factor, 'fp8') if default_net( ).plugin_config.low_latency_gemm_swiglu_plugin is not None: inter = low_latency_gemm_swiglu(hidden_states, self.fused_fc.weight.value, scale_d0, scale_d1, scale_output) else: inter = gemm_swiglu(hidden_states, self.fused_fc.weight.value, None, scale_d0, scale_d1, scale_output) lora_result = fc_gate_lora(hidden_states, self.lora, self.fused_gate_up_lora, lora_layer_params) if lora_result is not None: inter = inter + lora_result return inter
[docs] def fc_gate(self, hidden_states, lora_layer_params=None): # Combine the following pattern # # SiLU(FC(x)) * Gate(x) # # into: # # SwiGLU(FusedFC(x)) # # Upside is we don't need to modify 4 different weight loading paths just to concat weights inter = self.fused_fc(hidden_states) lora_result = fc_gate_lora(hidden_states, self.lora, self.fused_gate_up_lora, lora_layer_params) if lora_result is not None: inter = inter + lora_result if self.dora is not None: inter = fc_gate_dora(inter, self.dora, self.fused_gate_up_lora, lora_layer_params) if self.hidden_act == 'silu': inter = ACT2FN['swiglu'](inter) elif self.hidden_act == 'gelu': inter = ACT2FN['geglu'](inter) else: raise NotImplementedError( f"Activation {self.hidden_act} not yet implemented for {self.__class__.__name__}." ) return inter
[docs] def forward(self, hidden_states, lora_layer_params=None, all_reduce_params: Optional[AllReduceParams] = None): if default_net().plugin_config.gemm_swiglu_plugin or default_net( ).plugin_config.low_latency_gemm_swiglu_plugin: inter = self.fc_gate_plugin(hidden_states, lora_layer_params) else: inter = self.fc_gate(hidden_states, lora_layer_params) if self.inner_layernorm is not None: inter = self.inner_layernorm(inter) mlp_proj_lora_params = None if lora_layer_params is not None: mlp_proj_lora_params = lora_layer_params.get_runtime_params( 0, "mlp_4h_to_h") output = self.proj(inter, lora_runtime_params=mlp_proj_lora_params, all_reduce_params=all_reduce_params) return output
[docs] class LinearGELU(Module): def __init__(self, dim_in: int, dim_out: int, approximate: str = 'tanh', bias: bool = True, mapping=Mapping(), dtype=None): super().__init__() self.proj = ColumnLinear(dim_in, dim_out, bias=bias, dtype=dtype, tp_group=mapping.tp_group, tp_size=mapping.tp_size) if approximate != 'tanh': raise NotImplementedError('GELU only support tanh now.')
[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states = ACT2FN['gelu_pytorch_tanh'](hidden_states) return hidden_states
[docs] class LinearGEGLU(Module): def __init__(self, dim_in: int, dim_out: int, approximate: str = 'tanh', bias: bool = True, mapping=Mapping(), dtype=None): super().__init__() self.proj = ColumnLinear(dim_in, dim_out * 2, bias=bias, dtype=dtype, tp_group=mapping.tp_group, tp_size=mapping.tp_size) if approximate != 'tanh': raise NotImplementedError('GELU only support tanh now.')
[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states, gate = chunk(hidden_states, 2, dim=(hidden_states.ndim() - 1)) return hidden_states * ACT2FN['gelu_pytorch_tanh'](gate)
[docs] class LinearApproximateGELU(Module): def __init__(self, dim_in: int, dim_out: int, bias: bool = True, mapping=Mapping(), dtype=None): super().__init__() self.proj = ColumnLinear(dim_in, dim_out, bias=bias, dtype=dtype, tp_group=mapping.tp_group, tp_size=mapping.tp_size)
[docs] def forward(self, x): x = self.proj(x) return x * ACT2FN['sigmoid'](1.702 * x)
[docs] class LinearSwiGLU(Module): def __init__(self, dim_in: int, dim_out: int, bias: bool = True, mapping=Mapping(), dtype=None): super().__init__() self.proj = ColumnLinear(dim_in, dim_out * 2, bias=bias, dtype=dtype, tp_group=mapping.tp_group, tp_size=mapping.tp_size) self.hidden_act = 'silu'
[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) hidden_states, gate = chunk(hidden_states, 2, dim=(hidden_states.ndim() - 1)) return hidden_states * ACT2FN[self.hidden_act](gate)
[docs] class LinearActivation(Module): def __init__(self, dim_in: int, dim_out: int, bias: bool = True, activation: str = "silu", mapping=Mapping(), dtype=None): super().__init__() self.proj = ColumnLinear(dim_in, dim_out, bias=bias, dtype=dtype, tp_group=mapping.tp_group, tp_size=mapping.tp_size) self.hidden_act = activation
[docs] def forward(self, hidden_states): hidden_states = self.proj(hidden_states) return ACT2FN[self.activation](hidden_states)