Merge pull request #24 from donjuanplatinum/main

feat(candle_demo): 添加candle_demo

candle_demo/Cargo.lock

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candle_demo/Cargo.toml

@@ -0,0 +1,36 @@
+[package]
+name = "codegeex4-candle"
+version = "0.1.0"
+edition = "2021"
+authors = ["Donjuan Platinum <[email protected]>"]
+license = "GPL-2.0-only"
+description = "Codegeex4"
+# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
+
+[dependencies]
+# candle-transformers = {path = "../candle/candle-transformers"}
+# candle-core = {path = "../candle/candle-core"}
+# candle-nn = {path = "../candle/candle-nn"}
+#anyhow = "1.0.86"
+hf-hub = "0.3.2"
+#tokenizer = "0.1.2"
+clap = { version = "4.5.6", features = ["derive"] }
+#tracing-chrome = "0.7.2"
+#candle-examples = {path = "../candle/candle-examples"}
+#tracing-subscriber = "0.3.18"
+tokenizers = "0.19.1"
+serde_json = "1.0.120"
+candle-core = "0.6.0"
+candle-transformers = "0.6.0"
+candle-examples = "0.6.0"
+candle-nn = "0.6.0"
+safetensors = "0.4.3"
+#safetensors = {path ="../safetensors/safetensors"}
+[build-dependencies]
+bindgen_cuda = { version = "0.1.1", optional = true }
+
+
+[features]
+default = []
+cuda = ["candle-core/cuda", "candle-nn/cuda", "candle-transformers/cuda", "dep:bindgen_cuda"]
+

candle_demo/README.md

@@ -0,0 +1,11 @@
+# CPU运行
+```
+cargo run --release -- --prompt your prompt
+```
+
+# Cuda运行
+- 注意 需要cuda为>=12.4以上的版本
+```
+cargo build --release --features cuda
+./target/release/codegeex4-candle --prompt your prompt
+```

candle_demo/src/codegeex4.rs

@@ -0,0 +1,592 @@
+use candle_transformers::models::with_tracing::{linear_b as linear, Linear};
+use candle_core::{DType, Device, IndexOp, Module, Result, Tensor, D};
+use candle_core as candle;
+use candle_nn::VarBuilder;
+
+#[derive(Debug, Clone)]
+pub struct Config {
+    pub num_layers: usize,
+    pub padded_vocab_size: usize,
+    pub hidden_size: usize,
+    pub ffn_hidden_size: usize,
+    pub kv_channels: usize,
+    pub num_attention_heads: usize,
+    pub seq_length: usize,
+    pub layernorm_epsilon: f64,
+    pub rmsnorm: bool,
+    pub apply_residual_connection_post_layernorm: bool,
+    pub post_layer_norm: bool,
+    pub add_bias_linear: bool,
+    pub add_qkv_bias: bool,
+    pub bias_dropout_fusion: bool,
+    pub multi_query_attention: bool,
+    pub multi_query_group_num: usize,
+    pub apply_query_key_layer_scaling: bool,
+    pub attention_softmax_in_fp32: bool,
+    pub fp32_residual_connection: bool,
+}
+
+impl Config {
+    pub fn codegeex4() -> Self {
+        Self {
+            num_layers: 40,
+	    padded_vocab_size: 151552,
+            hidden_size: 4096,
+            ffn_hidden_size: 13696,
+            kv_channels: 128,
+            num_attention_heads: 32,
+            seq_length: 131072,
+            layernorm_epsilon: 1e-5,
+            rmsnorm: true,
+            apply_residual_connection_post_layernorm: false,
+            post_layer_norm: true,
+            add_bias_linear: false,
+            add_qkv_bias: true,
+            bias_dropout_fusion: true,
+            multi_query_attention: true,
+            multi_query_group_num: 2,
+            apply_query_key_layer_scaling: true,
+            attention_softmax_in_fp32: true,
+            fp32_residual_connection: false,
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+struct RotaryEmbedding {
+    cache: Tensor,
+}
+
+impl RotaryEmbedding {
+    fn new(cfg: &Config, dtype: DType, dev: &Device) -> Result<Self> {
+        let rotary_dim = cfg.kv_channels;
+        let n_elem = rotary_dim / 2;
+        let inv_freq: Vec<_> = (0..n_elem)
+            .step_by(2)
+            .map(|i| 1f32 / 10_000f64.powf(i as f64 / n_elem as f64) as f32)
+            .collect();
+        let inv_freq_len = inv_freq.len();
+        let inv_freq = Tensor::from_vec(inv_freq, (1, inv_freq_len), dev)?.to_dtype(dtype)?;
+        let t = Tensor::arange(0u32, cfg.seq_length as u32, dev)?
+            .to_dtype(dtype).expect("unalbe to dytpe in Rotray Embedding new")
+            .reshape((cfg.seq_length, 1))?;
+        let freqs = t.matmul(&inv_freq)?;
+        let cache = Tensor::stack(&[&freqs.cos()?, &freqs.sin()?], D::Minus1)?;
+        Ok(Self { cache })
+    }
+
+    fn apply(&self, xs: &Tensor, seqlen_offset: usize) -> Result<Tensor> {
+        let (seqlen, _b, np, _hn) = xs.dims4()?;
+        let cache = self.cache.narrow(0, seqlen_offset, seqlen)?;
+        let rot_dim = cache.dim(D::Minus2)? * 2;
+        let (xs, xs_pass) = (
+            xs.narrow(D::Minus1, 0, rot_dim)?,
+            xs.narrow(D::Minus1, rot_dim, rot_dim)?,
+        );
+        let xshaped = xs.reshape((seqlen, (), np, rot_dim / 2, 2))?;
+        let cache = cache.reshape((seqlen, (), 1, rot_dim / 2, 2))?;
+        let (xshaped0, xshaped1) = (
+            xshaped.i((.., .., .., .., 0))?,
+            xshaped.i((.., .., .., .., 1))?,
+        );
+        let (cache0, cache1) = (cache.i((.., .., .., .., 0))?, cache.i((.., .., .., .., 1))?);
+        let xs_out = Tensor::stack(
+            &[
+                (xshaped0.broadcast_mul(&cache0)? - xshaped1.broadcast_mul(&cache1)?)?,
+                (xshaped1.broadcast_mul(&cache0)? + xshaped0.broadcast_mul(&cache1)?)?,
+            ],
+            D::Minus1,
+        )?;
+        let xs_out = xs_out.flatten_from(3)?;
+        Tensor::cat(&[xs_out, xs_pass], D::Minus1)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct CoreAttention {
+    coeff: Option<f64>,
+    norm_factor: f64,
+}
+
+fn masked_fill(on_false: &Tensor, mask: &Tensor, on_true: f32) -> Result<Tensor> {
+    let shape = mask.shape();
+    let on_true = Tensor::new(on_true, on_false.device())?.broadcast_as(shape.dims())?;
+    let m = mask.where_cond(&on_true, on_false)?;
+    Ok(m)
+}
+
+impl CoreAttention {
+    fn new(layer_number: usize, cfg: &Config) -> Result<Self> {
+        let norm_factor = (cfg.kv_channels as f64).sqrt();
+        let (norm_factor, coeff) = if cfg.apply_query_key_layer_scaling {
+            let coeff = f64::max(1.0, layer_number as f64);
+            (norm_factor * coeff, Some(coeff))
+        } else {
+            (norm_factor, None)
+        };
+        Ok(Self { coeff, norm_factor })
+    }
+
+    fn forward(
+        &self,
+        query_layer: &Tensor,
+        key_layer: &Tensor,
+        value_layer: &Tensor,
+        attention_mask: &Option<Tensor>,
+    ) -> Result<Tensor> {
+        let output_size = (
+            query_layer.dim(1)?, // b
+            query_layer.dim(2)?, // np
+            query_layer.dim(0)?, // sq
+            key_layer.dim(0)?,   // sk
+        );
+        let query_layer =
+            query_layer.reshape((output_size.2, output_size.0 * output_size.1, ()))?;
+        let key_layer = key_layer.reshape((output_size.3, output_size.0 * output_size.1, ()))?;
+        let matmul_result = Tensor::matmul(
+            &query_layer.transpose(0, 1)?,
+            &key_layer.transpose(0, 1)?.transpose(1, 2)?,
+        )?;
+        let matmul_result = (matmul_result / self.norm_factor)?.reshape(output_size)?;
+        let matmul_result = match self.coeff {
+            None => matmul_result,
+            Some(coeff) => (matmul_result * coeff)?,
+        };
+        let attention_scores = match attention_mask {
+            Some(mask) => masked_fill(
+                &matmul_result,
+                &mask.broadcast_left((matmul_result.dim(0)?, matmul_result.dim(1)?))?,
+                f32::NEG_INFINITY,
+            )?,
+            None => matmul_result,
+        };
+        let attention_probs = candle_nn::ops::softmax_last_dim(&attention_scores)?;
+
+        let output_size = (
+            value_layer.dim(1)?,
+            value_layer.dim(2)?,
+            query_layer.dim(0)?,
+            value_layer.dim(3)?,
+        );
+        let value_layer =
+            value_layer.reshape((value_layer.dim(0)?, output_size.0 * output_size.1, ()))?;
+        let attention_probs =
+            attention_probs.reshape((output_size.0 * output_size.1, output_size.2, ()))?;
+        let context_layer = Tensor::matmul(&attention_probs, &value_layer.transpose(0, 1)?)?;
+        let context_layer = context_layer.reshape(output_size)?;
+        let context_layer = context_layer.permute((2, 0, 1, 3))?.contiguous()?;
+        context_layer.flatten_from(D::Minus2)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct SelfAttention {
+    query_key_value: Linear,
+    core_attention: CoreAttention,
+    dense: Linear,
+    multi_query_attention: bool,
+    num_attention_heads_per_partition: usize,
+    num_multi_query_groups_per_partition: usize,
+    hidden_size_per_attention_head: usize,
+    kv_cache: Option<(Tensor, Tensor)>,
+}
+
+impl SelfAttention {
+    fn new(layer_number: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let projection_size = cfg.kv_channels * cfg.num_attention_heads;
+        let hidden_size_per_attention_head = projection_size / cfg.num_attention_heads;
+        let qkv_hidden_size = if cfg.multi_query_attention {
+            projection_size + 2 * hidden_size_per_attention_head * cfg.multi_query_group_num
+        } else {
+            3 * projection_size
+        };
+        let query_key_value = linear(
+            cfg.hidden_size,
+            qkv_hidden_size,
+            cfg.add_bias_linear || cfg.add_qkv_bias,
+            vb.pp("query_key_value"),
+        )?;
+        let core_attention = CoreAttention::new(layer_number, cfg)?;
+        let dense = linear(
+            cfg.hidden_size,
+            cfg.hidden_size,
+            cfg.add_bias_linear,
+            vb.pp("dense"),
+        )?;
+        Ok(Self {
+            query_key_value,
+            core_attention,
+            dense,
+            multi_query_attention: cfg.multi_query_attention,
+            num_attention_heads_per_partition: cfg.num_attention_heads,
+            num_multi_query_groups_per_partition: cfg.multi_query_group_num,
+            hidden_size_per_attention_head: cfg.kv_channels,
+            kv_cache: None,
+        })
+    }
+
+    fn reset_kv_cache(&mut self) {
+        self.kv_cache = None
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: &Option<Tensor>,
+        rotary_emb: &RotaryEmbedding,
+    ) -> Result<Tensor> {
+        let mixed_x_layer = xs.apply(&self.query_key_value)?;
+        if !self.multi_query_attention {
+            candle::bail!("only multi_query_attention=true is supported")
+        }
+        let hpa = self.hidden_size_per_attention_head;
+        let query_layer =
+            mixed_x_layer.narrow(D::Minus1, 0, self.num_attention_heads_per_partition * hpa)?;
+        let key_layer = mixed_x_layer.narrow(
+            D::Minus1,
+            self.num_attention_heads_per_partition * hpa,
+            self.num_multi_query_groups_per_partition * hpa,
+        )?;
+        let value_layer = mixed_x_layer.narrow(
+            D::Minus1,
+            self.num_attention_heads_per_partition * hpa
+                + self.num_multi_query_groups_per_partition * hpa,
+            self.num_multi_query_groups_per_partition * hpa,
+        )?;
+        let query_layer = query_layer.reshape((
+            query_layer.dim(0)?,
+            query_layer.dim(1)?,
+            self.num_attention_heads_per_partition,
+            hpa,
+        ))?;
+        let key_layer = key_layer.reshape((
+            key_layer.dim(0)?,
+            key_layer.dim(1)?,
+            self.num_multi_query_groups_per_partition,
+            hpa,
+        ))?;
+        let value_layer = value_layer.reshape((
+            value_layer.dim(0)?,
+            value_layer.dim(1)?,
+            self.num_multi_query_groups_per_partition,
+            hpa,
+        ))?;
+
+        // Rotary embeddings.
+        let seqlen_offset = match &self.kv_cache {
+            None => 0,
+            Some((prev_k, _)) => prev_k.dim(0)?,
+        };
+        let query_layer = rotary_emb.apply(&query_layer, seqlen_offset)?;
+        let key_layer = rotary_emb.apply(&key_layer, seqlen_offset)?;
+
+        // KV cache.
+        let (key_layer, value_layer) = match &self.kv_cache {
+            None => (key_layer, value_layer),
+            Some((prev_k, prev_v)) => {
+                let k = Tensor::cat(&[prev_k, &key_layer], 0)?;
+                let v = Tensor::cat(&[prev_v, &value_layer], 0)?;
+                (k, v)
+            }
+        };
+        self.kv_cache = Some((key_layer.clone(), value_layer.clone()));
+
+        // Repeat KV.
+        let ratio =
+            self.num_attention_heads_per_partition / self.num_multi_query_groups_per_partition;
+        let key_layer = {
+            let (d0, d1, d2, d3) = key_layer.dims4()?;
+            key_layer
+                .unsqueeze(D::Minus2)?
+                .expand((d0, d1, d2, ratio, d3))?
+                .reshape((
+                    d0,
+                    d1,
+                    self.num_attention_heads_per_partition,
+                    self.hidden_size_per_attention_head,
+                ))?
+        };
+        let value_layer = {
+            let (d0, d1, d2, d3) = value_layer.dims4()?;
+            value_layer
+                .unsqueeze(D::Minus2)?
+                .expand((d0, d1, d2, ratio, d3))?
+                .reshape((
+                    d0,
+                    d1,
+                    self.num_attention_heads_per_partition,
+                    self.hidden_size_per_attention_head,
+                ))?
+        };
+
+        let context_layer =
+            self.core_attention
+                .forward(&query_layer, &key_layer, &value_layer, attention_mask)?;
+        let output = context_layer.apply(&self.dense)?;
+        Ok(output)
+    }
+}
+
+#[allow(clippy::upper_case_acronyms)]
+#[derive(Debug, Clone)]
+struct MLP {
+    dense_h_to_4h: Linear,
+    dense_4h_to_h: Linear,
+}
+
+impl MLP {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let dense_h_to_4h = linear(
+            cfg.hidden_size,
+            cfg.ffn_hidden_size * 2,
+            cfg.add_bias_linear,
+            vb.pp("dense_h_to_4h"),
+        )?;
+        let dense_4h_to_h = linear(
+            cfg.ffn_hidden_size,
+            cfg.hidden_size,
+            cfg.add_bias_linear,
+            vb.pp("dense_4h_to_h"),
+        )?;
+        Ok(Self {
+            dense_4h_to_h,
+            dense_h_to_4h,
+        })
+    }
+}
+
+impl Module for MLP {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        xs.apply(&self.dense_h_to_4h)?
+            .apply(&candle_nn::Activation::Swiglu)?
+            .apply(&self.dense_4h_to_h)
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Block {
+    input_layernorm: candle_nn::LayerNorm,
+    self_attention: SelfAttention,
+    post_attention_layernorm: candle_nn::LayerNorm,
+    mlp: MLP,
+    apply_residual_connection_post_layernorm: bool,
+}
+
+impl Block {
+    fn new(layer_number: usize, cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let input_layernorm = if cfg.rmsnorm {
+            candle_nn::rms_norm(
+                cfg.hidden_size,
+                cfg.layernorm_epsilon,
+                vb.pp("input_layernorm"),
+            )?
+            .into_inner()
+        } else {
+            candle_nn::layer_norm(
+                cfg.hidden_size,
+                cfg.layernorm_epsilon,
+                vb.pp("input_layernorm"),
+            )?
+        };
+        let post_attention_layernorm = if cfg.rmsnorm {
+            candle_nn::rms_norm(
+                cfg.hidden_size,
+                cfg.layernorm_epsilon,
+                vb.pp("post_attention_layernorm"),
+            )?
+            .into_inner()
+        } else {
+            candle_nn::layer_norm(
+                cfg.hidden_size,
+                cfg.layernorm_epsilon,
+                vb.pp("post_attention_layernorm"),
+            )?
+        };
+        let self_attention = SelfAttention::new(layer_number, cfg, vb.pp("self_attention"))?;
+        let mlp = MLP::new(cfg, vb.pp("mlp"))?;
+        Ok(Self {
+            input_layernorm,
+            self_attention,
+            post_attention_layernorm,
+            mlp,
+            apply_residual_connection_post_layernorm: cfg.apply_residual_connection_post_layernorm,
+        })
+    }
+
+    fn reset_kv_cache(&mut self) {
+        self.self_attention.reset_kv_cache()
+    }
+
+    fn forward(
+        &mut self,
+        xs: &Tensor,
+        attention_mask: &Option<Tensor>,
+        rotary_emb: &RotaryEmbedding,
+    ) -> Result<Tensor> {
+        let layernorm_output = xs.apply(&self.input_layernorm)?;
+        let attention_output =
+            self.self_attention
+                .forward(&layernorm_output, attention_mask, rotary_emb)?;
+        let residual = if self.apply_residual_connection_post_layernorm {
+            &layernorm_output
+        } else {
+            xs
+        };
+        let layernorm_input = (residual + attention_output)?;
+        let layernorm_output = layernorm_input.apply(&self.post_attention_layernorm)?;
+        let mlp_output = layernorm_output.apply(&self.mlp)?;
+        let residual = if self.apply_residual_connection_post_layernorm {
+            &layernorm_output
+        } else {
+            &layernorm_input
+        };
+        mlp_output + residual
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Transformer {
+    layers: Vec<Block>,
+    final_layernorm: Option<candle_nn::LayerNorm>,
+    rotary_emb: RotaryEmbedding,
+}
+
+impl Transformer {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb_l = vb.pp("layers");
+        let mut layers = Vec::with_capacity(cfg.num_layers);
+	println!("transofrmer layers create");
+	let mut count = 0;
+        for layer_index in 0..cfg.num_layers {
+	    count += 1;
+	    println!("for layer index in {} total is {} ",count, cfg.num_layers);
+            let block = Block::new(layer_index + 1, cfg, vb_l.pp(layer_index))?;
+            layers.push(block)
+        }
+        let final_layernorm = if cfg.post_layer_norm {
+            let ln = if cfg.rmsnorm {
+                candle_nn::rms_norm(
+                    cfg.hidden_size,
+                    cfg.layernorm_epsilon,
+                    vb.pp("final_layernorm"),
+                )?
+                .into_inner()
+            } else {
+                candle_nn::layer_norm(
+                    cfg.hidden_size,
+                    cfg.layernorm_epsilon,
+                    vb.pp("final_layernorm"),
+                )?
+            };
+            Some(ln)
+        } else {
+            None
+        };
+        let rotary_emb = RotaryEmbedding::new(cfg, vb.dtype(), vb.device())?;
+        Ok(Self {
+            layers,
+            final_layernorm,
+            rotary_emb,
+        })
+    }
+
+    fn reset_kv_cache(&mut self) {
+        for block in self.layers.iter_mut() {
+            block.reset_kv_cache()
+        }
+    }
+
+    fn forward(&mut self, xs: &Tensor, attention_mask: &Option<Tensor>) -> Result<Tensor> {
+        let mut xs = xs.clone();
+        for block in self.layers.iter_mut() {
+            xs = block.forward(&xs, attention_mask, &self.rotary_emb)?
+        }
+        match self.final_layernorm.as_ref() {
+            None => Ok(xs),
+            Some(ln) => xs.apply(ln),
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+struct Embedding {
+    word_embeddings: candle_nn::Embedding,
+    fp32_residual_connection: bool,
+}
+
+impl Embedding {
+    fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let word_embeddings = candle_nn::embedding(
+            cfg.padded_vocab_size,
+            cfg.hidden_size,
+            vb.pp("word_embeddings"),
+        )?;
+        Ok(Self {
+            word_embeddings,
+            fp32_residual_connection: cfg.fp32_residual_connection,
+        })
+    }
+}
+
+impl Module for Embedding {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let xs = self.word_embeddings.forward(xs)?.transpose(0, 1)?; // b,s,h -> s,b,h
+        if self.fp32_residual_connection {
+            xs.to_dtype(candle::DType::F32)
+        } else {
+            xs.contiguous()
+        }
+    }
+}
+
+#[derive(Debug, Clone)]
+pub struct Model {
+    embedding: Embedding,
+    encoder: Transformer,
+    output_layer: Linear,
+}
+
+fn get_mask(size: usize, device: &Device) -> Result<Tensor> {
+    let mask: Vec<_> = (0..size)
+        .flat_map(|i| (0..size).map(move |j| u8::from(j > i)))
+        .collect();
+    Tensor::from_slice(&mask, (size, size), device)
+}
+
+impl Model {
+    pub fn new(cfg: &Config, vb: VarBuilder) -> Result<Self> {
+        let vb = vb.pp("transformer");
+        let embedding = Embedding::new(cfg, vb.pp("embedding"))?;
+        let encoder = Transformer::new(cfg, vb.pp("encoder"))?;
+        let output_layer = linear(
+            cfg.hidden_size,
+            cfg.padded_vocab_size,
+            false,
+            vb.pp("output_layer"),
+        )?;
+	
+	
+        Ok(Self {
+            embedding,
+            encoder,
+            output_layer,
+        })
+    }
+
+    pub fn reset_kv_cache(&mut self) {
+        self.encoder.reset_kv_cache()
+    }
+
+    pub fn forward(&mut self, xs: &Tensor) -> Result<Tensor> {
+        let (_b_size, seq_len) = xs.dims2()?;
+        let input_embeds = xs.apply(&self.embedding)?;
+        let attention_mask = if seq_len <= 1 {
+            None
+        } else {
+            Some(get_mask(seq_len, xs.device())?)
+        };
+        let xs = self.encoder.forward(&input_embeds, &attention_mask)?;
+        let lm_logits = xs.i(seq_len - 1)?.apply(&self.output_layer)?;
+        Ok(lm_logits)
+    }
+}

candle_demo/src/lib.rs

@@ -0,0 +1 @@
+pub mod codegeex4;

candle_demo/src/main.rs

@@ -0,0 +1,247 @@
+use clap::Parser;
+use codegeex4_candle::codegeex4::*;
+
+
+use candle_core::{DType, Device, Tensor};
+use candle_core as candle;
+use candle_nn::VarBuilder;
+use candle_transformers::generation::LogitsProcessor;
+use hf_hub::{api::sync::Api, Repo, RepoType};
+use tokenizers::Tokenizer;
+
+struct TextGeneration {
+    model: Model,
+    device: Device,
+    tokenizer: Tokenizer,
+    logits_processor: LogitsProcessor,
+    repeat_penalty: f32,
+    repeat_last_n: usize,
+    verbose_prompt: bool,
+    dtype: DType,
+}
+
+impl TextGeneration {
+    #[allow(clippy::too_many_arguments)]
+    fn new(
+        model: Model,
+        tokenizer: Tokenizer,
+        seed: u64,
+        temp: Option<f64>,
+        top_p: Option<f64>,
+        repeat_penalty: f32,
+        repeat_last_n: usize,
+        verbose_prompt: bool,
+        device: &Device,
+	dtype: DType,
+    ) -> Self {
+        let logits_processor = LogitsProcessor::new(seed, temp, top_p);
+        Self {
+            model,
+            tokenizer,
+            logits_processor,
+            repeat_penalty,
+            repeat_last_n,
+            verbose_prompt,
+            device: device.clone(),
+	    dtype,
+        }
+    }
+
+    fn run(&mut self, prompt: &str, sample_len: usize) -> Result<(),()> {
+        use std::io::Write;
+        println!("starting the inference loop");
+        let tokens = self.tokenizer.encode(prompt, true).expect("tokens error");
+        if tokens.is_empty() {
+            panic!("Empty prompts are not supported in the chatglm model.")
+        }
+        if self.verbose_prompt {
+            for (token, id) in tokens.get_tokens().iter().zip(tokens.get_ids().iter()) {
+                let token = token.replace('▁', " ").replace("<0x0A>", "\n");
+                println!("{id:7} -> '{token}'");
+            }
+        }
+        let eos_token = match self.tokenizer.get_vocab(true).get("<|endoftext|>") {
+            Some(token) => *token,
+            None => panic!("cannot find the endoftext token"),
+        };
+        let mut tokens = tokens.get_ids().to_vec();
+        let mut generated_tokens = 0usize;
+        
+        
+        print!("{prompt}");
+        std::io::stdout().flush().expect("output flush error");
+        let start_gen = std::time::Instant::now();
+	
+	println!("\n start_gen");
+	println!("samplelen {}",sample_len);
+	let mut count = 0;
+	let mut result = vec!();
+        for index in 0..sample_len {
+	    count += 1;
+	    println!("sample count {}",count);
+            let context_size = if index > 0 { 1 } else { tokens.len() };
+            let ctxt = &tokens[tokens.len().saturating_sub(context_size)..];
+            let input = Tensor::new(ctxt, &self.device).unwrap().unsqueeze(0).expect("create tensor input error");
+            let logits = self.model.forward(&input).unwrap();
+            let logits = logits.squeeze(0).unwrap().to_dtype(self.dtype).unwrap();
+            let logits = if self.repeat_penalty == 1. {
+                logits
+            } else {
+                let start_at = tokens.len().saturating_sub(self.repeat_last_n);
+                candle_transformers::utils::apply_repeat_penalty(
+                    &logits,
+                    self.repeat_penalty,
+                    &tokens[start_at..],
+                ).unwrap()
+            };
+
+            let next_token = self.logits_processor.sample(&logits).unwrap();
+            tokens.push(next_token);
+            generated_tokens += 1;
+            if next_token == eos_token {
+                break;
+            }
+	    println!("raw generate token {}",next_token);
+            let token = self.tokenizer.decode(&[next_token], true).expect("Token error");
+            println!("[token:{token}]");
+	    result.push(token);
+            std::io::stdout().flush().unwrap();
+        }
+        let dt = start_gen.elapsed();
+        println!(
+            "\n{generated_tokens} tokens generated ({:.2} token/s)",
+            generated_tokens as f64 / dt.as_secs_f64(),
+        );
+	println!("Result:");
+	for tokens in result {
+	    print!("{tokens}");
+	}
+        Ok(())
+    }
+}
+
+#[derive(Parser, Debug)]
+#[command(author, version, about, long_about = None)]
+struct Args {
+    /// Run on CPU rather than on GPU.
+    #[arg(name="cache",short,long, default_value=".")]
+    cache_path: String,
+    
+    #[arg(long)]
+    cpu: bool,
+
+    /// Display the token for the specified prompt.
+    #[arg(long)]
+    verbose_prompt: bool,
+
+    #[arg(long)]
+    prompt: String,
+
+    /// The temperature used to generate samples.
+    #[arg(long)]
+    temperature: Option<f64>,
+
+    /// Nucleus sampling probability cutoff.
+    #[arg(long)]
+    top_p: Option<f64>,
+
+    /// The seed to use when generating random samples.
+    #[arg(long, default_value_t = 299792458)]
+    seed: u64,
+
+    /// The length of the sample to generate (in tokens).
+    #[arg(long, short = 'n', default_value_t = 5000)]
+    sample_len: usize,
+
+    #[arg(long)]
+    model_id: Option<String>,
+
+    #[arg(long)]
+    revision: Option<String>,
+
+    #[arg(long)]
+    weight_file: Option<String>,
+
+    #[arg(long)]
+    tokenizer: Option<String>,
+
+    /// Penalty to be applied for repeating tokens, 1. means no penalty.
+    #[arg(long, default_value_t = 1.1)]
+    repeat_penalty: f32,
+
+    /// The context size to consider for the repeat penalty.
+    #[arg(long, default_value_t = 64)]
+    repeat_last_n: usize,
+}
+
+fn main() -> Result<(),()> {
+    
+    let args = Args::parse();
+    println!(
+        "avx: {}, neon: {}, simd128: {}, f16c: {}",
+        candle::utils::with_avx(),
+        candle::utils::with_neon(),
+        candle::utils::with_simd128(),
+        candle::utils::with_f16c()
+    );
+    println!(
+        "temp: {:.2} repeat-penalty: {:.2} repeat-last-n: {}",
+        args.temperature.unwrap_or(0.95),
+        args.repeat_penalty,
+        args.repeat_last_n
+    );
+
+    let start = std::time::Instant::now();
+    println!("cache path {}",args.cache_path);
+    let api = hf_hub::api::sync::ApiBuilder::from_cache(hf_hub::Cache::new(args.cache_path.into())).build().unwrap();
+    
+    let model_id = match args.model_id {
+        Some(model_id) => model_id.to_string(),
+        None => "THUDM/codegeex4-all-9b".to_string(),
+    };
+    let revision = match args.revision {
+        Some(rev) => rev.to_string(),
+        None => "main".to_string(),
+    };
+    let repo = api.repo(Repo::with_revision(model_id, RepoType::Model, revision));
+    let tokenizer_filename = match args.tokenizer {
+        Some(file) => std::path::PathBuf::from(file),
+        None => api
+            .model("THUDM/codegeex4-all-9b".to_string())
+            .get("tokenizer.json").unwrap(),
+    };
+    let filenames = match args.weight_file {
+        Some(weight_file) => vec![std::path::PathBuf::from(weight_file)],
+        None => candle_examples::hub_load_safetensors(&repo, "model.safetensors.index.json").unwrap(),
+    };
+    println!("retrieved the files in {:?}", start.elapsed());
+    let tokenizer = Tokenizer::from_file(tokenizer_filename).expect("Tokenizer Error");
+
+    let start = std::time::Instant::now();
+    let config = Config::codegeex4();
+    let device = candle_examples::device(args.cpu).unwrap();
+    let dtype = if device.is_cuda() {
+	DType::BF16
+    } else {
+	DType::F32
+    };
+    let vb = unsafe { VarBuilder::from_mmaped_safetensors(&filenames, dtype, &device).unwrap() };
+    let model = Model::new(&config, vb).unwrap();
+
+    println!("loaded the model in {:?}", start.elapsed());
+
+    let mut pipeline = TextGeneration::new(
+        model,
+        tokenizer,
+        args.seed,
+        args.temperature,
+        args.top_p,
+        args.repeat_penalty,
+        args.repeat_last_n,
+        args.verbose_prompt,
+        &device,
+	dtype,
+    );
+    pipeline.run(&args.prompt, args.sample_len)?;
+    Ok(())
+}