mirror of
https://github.com/huggingface/text-generation-inference.git
synced 2025-09-09 03:14:53 +00:00
Remove unnecessary modifications
Signed-off-by: yuanwu <yuan.wu@intel.com>
This commit is contained in:
yuanwu
parent
3aa882337e
commit
4e95db304f
@ -122,5 +122,5 @@ ENV OMPI_MCA_btl_vader_single_copy_mechanism NONE
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COPY backends/gaudi/tgi-entrypoint.sh /tgi-entrypoint.sh
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RUN chmod +x /tgi-entrypoint.sh
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#ENTRYPOINT ["/tgi-entrypoint.sh"]
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#CMD ["--json-output"]
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ENTRYPOINT ["/tgi-entrypoint.sh"]
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CMD ["--json-output"]
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@ -8,7 +8,7 @@ PYTORCH_VERSION := 2.6.0
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.PHONY: image run-local-dev-container install-dependencies install-server install-router install-launcher local-dev-install
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image:
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docker build -t tgi-gaudi -f ${root_dir}/Dockerfile_gaudi ${root_dir} --build-arg HABANA_VERSION=$(HABANA_VERSION) --build-arg PYTORCH_VERSION=$(PYTORCH_VERSION) --build-arg http_proxy=${http_proxy} --build-arg https_proxy=${https_proxy} --build-arg no_proxy=${no_proxy}
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docker build -t tgi-gaudi -f ${root_dir}/Dockerfile_gaudi ${root_dir} --build-arg HABANA_VERSION=$(HABANA_VERSION) --build-arg PYTORCH_VERSION=$(PYTORCH_VERSION)
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run-local-dev-container:
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docker run -it \
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@ -57,7 +57,7 @@ def serve(
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), "MASTER_PORT must be set when sharded is True"
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# Remove default handler
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#logger.remove()
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logger.remove()
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logger.add(
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sys.stdout,
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format="{message}",
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@ -193,7 +193,7 @@ def download_weights(
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merge_lora: bool = False,
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):
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# Remove default handler
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#logger.remove()
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logger.remove()
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logger.add(
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sys.stdout,
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format="{message}",
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@ -37,6 +37,7 @@ class UnquantizedSparseMoELayer(nn.Module):
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self.weight_block_size = weights.weights_loader.weight_block_size
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self.scoring_func = scoring_func
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self.e_score_correction_bias = e_score_correction_bias
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self.gate_up_proj = _load_expert_multi_weights_col(
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prefix=prefix,
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n_experts=n_experts,
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@ -51,6 +52,7 @@ class UnquantizedSparseMoELayer(nn.Module):
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name=down_proj_name,
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weights=weights,
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)
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self.hpu_fused_moe = DynamicFusedMOE(n_experts)
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for i in range(n_experts):
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self.hpu_fused_moe.MoeOp.w13_list[i].set_weight(self.gate_up_proj[i])
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@ -16,9 +16,6 @@ import enum
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from text_generation_server.utils.speculate import get_speculate, set_speculate
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from text_generation_server.models.model import Model
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#from text_generation_server.models.causal_lm import CausalLM
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#from text_generation_server.models.bloom import BLOOM
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#from text_generation_server.models.starcoder import StarCoder
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from text_generation_server.models.custom_modeling.flash_phi_moe_modeling import (
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PhiMoEConfig,
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)
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@ -32,7 +29,6 @@ from text_generation_server.utils.adapter import (
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from text_generation_server.adapters.lora import LoraWeights
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from text_generation_server.utils.log import log_master
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#from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
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__all__ = [
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"Model",
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@ -47,7 +43,7 @@ FLASH_ATT_ERROR_MESSAGE = "{} requires Flash Attention enabled models."
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FLASH_ATTENTION = False
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if ATTENTION == "paged":
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FLASH_ATTENTION = True
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print(f"Flash Attention enabled models: {FLASH_ATTENTION}")
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try:
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from text_generation_server.models.flash_causal_lm import FlashCausalLM
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from text_generation_server.models.flash_vlm_causal_lm import FlashVlmCausalLM
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@ -459,9 +455,7 @@ def get_model(
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kv_cache_dtype = dtype
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print(f"Model type: {model_type}")
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if FLASH_ATTENTION:
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print(f"Flash Attention enabled models: {model_type}")
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if model_type == DEEPSEEK_V2:
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head_size = max(
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config_dict.get("qk_nope_dim", 128)
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@ -31,7 +31,6 @@ from text_generation_server.layers.attention import (
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KVCache,
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get_kv_scales,
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)
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from text_generation_server.utils.log import log_master
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from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
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from text_generation_server.layers.attention import (
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paged_attention,
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@ -47,7 +46,6 @@ from text_generation_server.layers import (
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TensorParallelMultiAdapterLinear,
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TensorParallelAdapterRowLinear,
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)
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from loguru import logger
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from text_generation_server.layers.rotary import PositionRotaryEmbedding
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from text_generation_server.layers.layernorm import (
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FastRMSNorm,
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@ -61,11 +59,6 @@ from text_generation_server.utils.weights import (
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)
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from text_generation_server.layers.fp8 import HybridFP8UnquantLoader
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def torch_save(tensor, name):
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# Only save on the main process (rank 0) when using distributed training
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if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0:
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torch.save(tensor, name)
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def load_attention(config, prefix: str, weights, layer_id):
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# Only defined in granite.
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@ -382,7 +375,7 @@ class LlamaMLP(nn.Module):
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class FlashLlamaLayer(nn.Module):
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def __init__(self, index, prefix, config, weights):
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super().__init__()
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self.index = index
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with no_fp8(weights):
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self.self_attn = FlashLlamaAttention(
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index=index,
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@ -443,7 +436,6 @@ class FlashLlamaLayer(nn.Module):
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seqlen,
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adapter_data,
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cross_attention_states,
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run_index,
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hpu_attention_meta: Optional[HPUPagedAttentionMetadata],
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):
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normed_hidden_states, res = self.input_layernorm(hidden_states, residual)
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@ -460,10 +452,6 @@ class FlashLlamaLayer(nn.Module):
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adapter_data,
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hpu_attention_meta=hpu_attention_meta,
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)
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if run_index != -1:
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torch_save(attn_output, f"trans.{run_index}.Llama4TextDecoderLayer.{self.index}.attention.attention_states.pt")
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if self.residual_multiplier is not None:
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attn_output *= self.residual_multiplier
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@ -472,10 +460,6 @@ class FlashLlamaLayer(nn.Module):
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)
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mlp_output = self.mlp(normed_attn_res_output, adapter_data)
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if run_index != -1:
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torch_save(mlp_output, f"trans.{run_index}.Llama4TextDecoderLayer.{self.index}.mlp.pt")
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if self.residual_multiplier is not None:
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mlp_output *= self.residual_multiplier
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@ -485,7 +469,6 @@ class FlashLlamaLayer(nn.Module):
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class FlashLlamaModel(torch.nn.Module):
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def __init__(self, prefix, config, weights):
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super().__init__()
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self.run_index = -1
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process_group = weights.process_group
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self.tp_rank = process_group.rank()
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self.tp_world_size = process_group.size()
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@ -582,12 +565,11 @@ class FlashLlamaModel(torch.nn.Module):
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seqlen,
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adapter_data,
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cross_attention_states,
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self.run_index,
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hpu_attention_meta=hpu_attention_meta,
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)
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hidden_states, _ = self.norm(hidden_states, residual)
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self.run_index += 1
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return hidden_states
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@ -650,14 +632,7 @@ class FlashLlamaForCausalLM(torch.nn.Module):
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adapter_data: Optional[torch.Tensor] = None,
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cross_attention_states=None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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log_master(
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logger.debug,
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f"input_ids: {input_ids}, input_ids.shape={input_ids.shape}, input_ids={input_ids[:-20]}"
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)
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inputs_embeds = self.embed_tokens(input_ids)
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print(f"111111111 inputs_embeds: {inputs_embeds}")
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hidden_states = self.model(
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inputs_embeds,
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position_ids,
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@ -1520,10 +1520,6 @@ class FlashCausalLM(Model):
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if os.getenv("VLLM_SKIP_WARMUP", "false").lower() == "true":
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logger.info("skip warmup hpu graph, not recommmended")
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del _batch, batch
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print(f"max_input_tokens: {max_input_tokens}")
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print(f"max_total_tokens: {max_total_tokens}")
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print(f"num_blocks: {num_blocks}")
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print(f"BLOCK_SIZE: {BLOCK_SIZE}")
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return int(num_blocks * BLOCK_SIZE), max_input_tokens, max_total_tokens
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self.warmup_hpu_graph(batch)
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@ -1796,7 +1792,7 @@ class FlashCausalLM(Model):
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kwargs = {}
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if htorch.utils.internal.is_lazy():
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kwargs["bypass_hpu_graphs"] = batch.prefilling
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print(f"11111111111111111111input_ids: {input_ids.shape}")
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logits, speculative_logits = self.model.forward(
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input_ids=input_ids,
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position_ids=position_ids,
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@ -24,25 +24,25 @@ from text_generation_server.utils.adapter import AdapterInfo
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from text_generation_server.utils.tokens import make_tokenizer_optional
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from text_generation_server.utils.prefill_chunking import set_max_prefill_tokens
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#try:
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from text_generation_server.models.pali_gemma import PaliGemmaBatch
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from text_generation_server.models.mllama_causal_lm import FlashMllamaCausalLMBatch
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# from text_generation_server.models.vlm_causal_lm import (
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# VlmCausalLMBatch,
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# )
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from text_generation_server.models.flash_vlm_causal_lm import (
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try:
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from text_generation_server.models.pali_gemma import PaliGemmaBatch
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from text_generation_server.models.mllama_causal_lm import FlashMllamaCausalLMBatch
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from text_generation_server.models.vlm_causal_lm import (
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VlmCausalLMBatch,
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)
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from text_generation_server.models.flash_vlm_causal_lm import (
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FlashVlmCausalLMBatch,
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)
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)
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VLM_BATCH_TYPES = {
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VLM_BATCH_TYPES = {
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PaliGemmaBatch,
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FlashVlmCausalLMBatch,
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FlashMllamaCausalLMBatch,
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}
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#except (ImportError, NotImplementedError):
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}
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except (ImportError, NotImplementedError):
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# These imports can fail on CPU/Non flash.
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# print(f"importError: {ImportError}")
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# VLM_BATCH_TYPES = set()
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VLM_BATCH_TYPES = set()
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from text_generation_server.utils.version import (
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is_driver_compatible,
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MIN_TGI_GAUDI_SYNAPSE_VERSION,
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@ -110,7 +110,6 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
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async def Warmup(self, request, context):
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if ATTENTION == "paged":
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set_max_prefill_tokens(request.max_prefill_tokens)
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print(f"VLM_BATCH_TYPES: {VLM_BATCH_TYPES}")
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if (
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self.model.batch_type in VLM_BATCH_TYPES
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): # Hack, i would rather use kwargs in the `from_pb` call
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@ -1,6 +1,5 @@
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import torch
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from loguru import logger
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from text_generation_server.utils.log import log_master
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def get_hpu_free_memory(device, memory_fraction):
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@ -8,7 +7,7 @@ def get_hpu_free_memory(device, memory_fraction):
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device_id = device.index
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mem_stats = memory_stats(device_id)
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log_master(logger.debug, f"mem_stats: {mem_stats}")
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logger.info(f"mem_stats: {mem_stats}")
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total_free_memory = mem_stats["Limit"] - mem_stats["MaxInUse"]
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free_memory = max(
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0, int(total_free_memory - (1 - memory_fraction) * mem_stats["Limit"])
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