Higher priority for user input of max_prefill_tokens & format (#540)
This commit is contained in:
Ying Sheng
+161
-112
@@ -33,13 +33,11 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.utils import print_warning_once
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.managers.controller.model_runner import InputMetadata
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class MixtralMoE(nn.Module):
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"""A tensor-parallel MoE implementation for Mixtral that shards each expert
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across all ranks.
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@@ -76,32 +74,46 @@ class MixtralMoE(nn.Module):
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self.params_dtype = params_dtype
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# Gate always runs at half / full precision for now.
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self.gate = ReplicatedLinear(self.hidden_size,
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self.num_total_experts,
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bias=False,
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params_dtype=self.params_dtype,
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quant_config=None)
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self.gate = ReplicatedLinear(
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self.hidden_size,
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self.num_total_experts,
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bias=False,
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params_dtype=self.params_dtype,
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quant_config=None,
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)
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if self.use_fp8 and self.quant_config.is_checkpoint_fp8_serialized:
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params_dtype = torch.float8_e4m3fn
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self.w13_weight = nn.Parameter(
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torch.empty(self.num_total_experts,
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2 * self.intermediate_size,
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self.hidden_size,
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dtype=params_dtype))
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torch.empty(
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self.num_total_experts,
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2 * self.intermediate_size,
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self.hidden_size,
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dtype=params_dtype,
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)
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)
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self.w2_weight = nn.Parameter(
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torch.empty(self.num_total_experts,
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self.hidden_size,
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self.intermediate_size,
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dtype=params_dtype))
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torch.empty(
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self.num_total_experts,
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self.hidden_size,
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self.intermediate_size,
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dtype=params_dtype,
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)
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)
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set_weight_attrs(self.w13_weight, {
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"weight_loader": self.weight_loader,
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})
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set_weight_attrs(self.w2_weight, {
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"weight_loader": self.weight_loader,
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})
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set_weight_attrs(
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self.w13_weight,
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{
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"weight_loader": self.weight_loader,
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},
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)
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set_weight_attrs(
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self.w2_weight,
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{
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"weight_loader": self.weight_loader,
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},
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)
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# Used for fp8.
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self.w13_scale = None
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@@ -111,46 +123,68 @@ class MixtralMoE(nn.Module):
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if self.use_fp8:
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# WEIGHT_SCALE (for fp8)
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self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts,
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dtype=torch.float32),
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requires_grad=False)
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self.w2_scale = nn.Parameter(torch.ones(self.num_total_experts,
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dtype=torch.float32),
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requires_grad=False)
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self.w13_scale = nn.Parameter(
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torch.ones(self.num_total_experts, dtype=torch.float32),
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requires_grad=False,
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)
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self.w2_scale = nn.Parameter(
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torch.ones(self.num_total_experts, dtype=torch.float32),
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requires_grad=False,
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)
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# If loading fp8 checkpoint, pass the weight loaders.
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# If loading an fp16 checkpoint, do not (we will quantize in
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# process_weights_after_loading()
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if quant_config.is_checkpoint_fp8_serialized:
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set_weight_attrs(self.w13_scale, {
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"weight_loader": self.weight_loader,
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})
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set_weight_attrs(self.w2_scale, {
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"weight_loader": self.weight_loader,
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})
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set_weight_attrs(
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self.w13_scale,
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{
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"weight_loader": self.weight_loader,
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},
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)
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set_weight_attrs(
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self.w2_scale,
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{
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"weight_loader": self.weight_loader,
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},
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)
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# ACT_SCALE (for fp8)
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if quant_config.activation_scheme == "static":
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if not quant_config.is_checkpoint_fp8_serialized:
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raise ValueError(
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"Found static activation scheme for checkpoint that "
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"was not serialized fp8.")
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self.a13_scale = nn.Parameter(torch.zeros(
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self.num_total_experts, dtype=torch.float32),
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requires_grad=False)
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self.a2_scale = nn.Parameter(torch.zeros(
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self.num_total_experts, dtype=torch.float32),
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requires_grad=False)
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"was not serialized fp8."
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)
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self.a13_scale = nn.Parameter(
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torch.zeros(self.num_total_experts, dtype=torch.float32),
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requires_grad=False,
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)
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self.a2_scale = nn.Parameter(
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torch.zeros(self.num_total_experts, dtype=torch.float32),
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requires_grad=False,
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)
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set_weight_attrs(self.a13_scale, {
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"weight_loader": self.weight_loader,
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})
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set_weight_attrs(self.a2_scale, {
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"weight_loader": self.weight_loader,
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})
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set_weight_attrs(
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self.a13_scale,
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{
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"weight_loader": self.weight_loader,
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},
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)
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set_weight_attrs(
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self.a2_scale,
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{
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"weight_loader": self.weight_loader,
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},
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)
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def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor,
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weight_name: str, expert_id: int):
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def weight_loader(
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self,
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param: nn.Parameter,
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loaded_weight: torch.Tensor,
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weight_name: str,
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expert_id: int,
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):
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tp_rank = get_tensor_model_parallel_rank()
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param_data = param.data
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shard_size = self.intermediate_size
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@@ -158,8 +192,9 @@ class MixtralMoE(nn.Module):
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if weight_name.endswith("w1.weight"):
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param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :]
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if weight_name.endswith("w3.weight"):
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param_data[expert_id,
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shard_size:2 * shard_size, :] = loaded_weight[shard, :]
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param_data[expert_id, shard_size : 2 * shard_size, :] = loaded_weight[
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shard, :
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]
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if weight_name.endswith("w2.weight"):
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param_data[expert_id, :, :] = loaded_weight[:, shard]
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if "act_scale" in weight_name or "weight_scale" in weight_name:
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@@ -172,17 +207,17 @@ class MixtralMoE(nn.Module):
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# If checkpoint is fp16, quantize here.
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if not self.quant_config.is_checkpoint_fp8_serialized:
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w13_weight = torch.empty_like(self.w13_weight.data,
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dtype=torch.float8_e4m3fn)
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w2_weight = torch.empty_like(self.w2_weight.data,
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dtype=torch.float8_e4m3fn)
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w13_weight = torch.empty_like(
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self.w13_weight.data, dtype=torch.float8_e4m3fn
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)
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w2_weight = torch.empty_like(self.w2_weight.data, dtype=torch.float8_e4m3fn)
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for expert in range(self.num_total_experts):
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w13_weight[expert, :, :], self.w13_scale[
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expert] = ops.scaled_fp8_quant(
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self.w13_weight.data[expert, :, :])
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w2_weight[expert, :, :], self.w2_scale[
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expert] = ops.scaled_fp8_quant(
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self.w2_weight.data[expert, :, :])
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w13_weight[expert, :, :], self.w13_scale[expert] = ops.scaled_fp8_quant(
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self.w13_weight.data[expert, :, :]
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)
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w2_weight[expert, :, :], self.w2_scale[expert] = ops.scaled_fp8_quant(
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self.w2_weight.data[expert, :, :]
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)
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self.w13_weight = nn.Parameter(w13_weight, requires_grad=False)
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self.w2_weight = nn.Parameter(w2_weight, requires_grad=False)
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@@ -193,40 +228,40 @@ class MixtralMoE(nn.Module):
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if self.a13_scale is None or self.a2_scale is None:
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raise ValueError(
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"QuantConfig has static quantization, but found "
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"activation scales are None.")
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"activation scales are None."
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)
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if (not all_close_1d(self.a13_scale)
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or not all_close_1d(self.a2_scale)):
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if not all_close_1d(self.a13_scale) or not all_close_1d(self.a2_scale):
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print_warning_once(
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"Found act_scales that are not equal for fp8 MoE layer. "
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"Using the maximum across experts for each layer. ")
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"Using the maximum across experts for each layer. "
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)
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self.a13_scale = nn.Parameter(self.a13_scale.max(),
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requires_grad=False)
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self.a2_scale = nn.Parameter(self.a2_scale.max(),
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requires_grad=False)
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self.a13_scale = nn.Parameter(self.a13_scale.max(), requires_grad=False)
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self.a2_scale = nn.Parameter(self.a2_scale.max(), requires_grad=False)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_size = hidden_states.shape
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hidden_states = hidden_states.view(-1, self.hidden_size)
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# router_logits: (num_tokens, n_experts)
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router_logits, _ = self.gate(hidden_states)
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final_hidden_states = fused_moe(hidden_states,
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self.w13_weight,
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self.w2_weight,
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router_logits,
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self.top_k,
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renormalize=True,
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inplace=True,
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use_fp8=self.use_fp8,
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w1_scale=self.w13_scale,
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w2_scale=self.w2_scale,
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a1_scale=self.a13_scale,
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a2_scale=self.a2_scale)
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final_hidden_states = fused_moe(
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hidden_states,
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self.w13_weight,
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self.w2_weight,
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router_logits,
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self.top_k,
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renormalize=True,
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inplace=True,
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use_fp8=self.use_fp8,
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w1_scale=self.w13_scale,
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w2_scale=self.w2_scale,
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a1_scale=self.a13_scale,
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a2_scale=self.a2_scale,
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)
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(
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final_hidden_states)
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_size)
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@@ -335,7 +370,8 @@ class MixtralDecoderLayer(nn.Module):
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size,
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quant_config=quant_config)
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quant_config=quant_config,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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@@ -444,35 +480,48 @@ class MixtralForCausalLM(nn.Module):
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("qkv_proj", "v_proj", "v"),
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]
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expert_params_mapping = [
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# These are the weight scales for the experts
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# (param_name, weight_name, expert_id)
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("w13_scale" if weight_name in ["w1", "w3"] else "w2_scale",
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f"experts.{expert_id}.{weight_name}.weight_scale", expert_id)
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for expert_id in range(self.config.num_local_experts)
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for weight_name in ["w1", "w2", "w3"]
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] + [
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# These are the weights for the experts
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# (param_name, weight_name, expert_id)
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("w13_weight" if weight_name in ["w1", "w3"] else "w2_weight",
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f"experts.{expert_id}.{weight_name}.weight", expert_id)
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for expert_id in range(self.config.num_local_experts)
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for weight_name in ["w1", "w2", "w3"]
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] + [
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# These are the activation scales for the experts
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# (param_name, weight_name, expert_id)
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("a13_scale" if weight_name in ["w1", "w3"] else "a2_scale",
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f"experts.{expert_id}.{weight_name}.act_scale", expert_id)
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for expert_id in range(self.config.num_local_experts)
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for weight_name in ["w1", "w2", "w3"]
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]
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expert_params_mapping = (
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[
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# These are the weight scales for the experts
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# (param_name, weight_name, expert_id)
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(
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"w13_scale" if weight_name in ["w1", "w3"] else "w2_scale",
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f"experts.{expert_id}.{weight_name}.weight_scale",
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expert_id,
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)
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for expert_id in range(self.config.num_local_experts)
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for weight_name in ["w1", "w2", "w3"]
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]
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+ [
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# These are the weights for the experts
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# (param_name, weight_name, expert_id)
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(
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"w13_weight" if weight_name in ["w1", "w3"] else "w2_weight",
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f"experts.{expert_id}.{weight_name}.weight",
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expert_id,
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)
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for expert_id in range(self.config.num_local_experts)
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for weight_name in ["w1", "w2", "w3"]
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]
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+ [
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# These are the activation scales for the experts
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# (param_name, weight_name, expert_id)
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(
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"a13_scale" if weight_name in ["w1", "w3"] else "a2_scale",
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f"experts.{expert_id}.{weight_name}.act_scale",
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expert_id,
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)
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for expert_id in range(self.config.num_local_experts)
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for weight_name in ["w1", "w2", "w3"]
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]
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)
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name:
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continue
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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@@ -490,18 +539,18 @@ class MixtralForCausalLM(nn.Module):
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name = name.replace(weight_name, param_name)
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param,
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loaded_weight,
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weight_name,
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expert_id=expert_id)
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weight_loader(
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param, loaded_weight, weight_name, expert_id=expert_id
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)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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