chore: import upstream snapshot with attribution

2026-07-13 13:36:25 +08:00
commit 26446540fa
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+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# ruff: noqa: F401
+
+"""
+.. _opt_llm:
+
+Optimize Large Language Model
+=============================
+As large language models (LLMs) have become a popular research topic in many different fields,
+deploying them on cloud and edge devices has become a challenging task. In this tutorial, we will
+demonstrate how to optimize a large language model using Apache TVM. We will use a pre-trained
+TinyLlama model from Hugging Face and deploy it on various devices.
+"""
+
+######################################################################
+# Review Overall Flow
+# -------------------
+# .. figure:: https://raw.githubusercontent.com/tlc-pack/web-data/main/images/design/tvm_overall_flow.svg
+#    :align: center
+#    :width: 80%
+#
+# The overall flow consists of the following steps:
+#
+# - **Construct or Import a Model**: Construct a neural network model or import a pre-trained
+#   model from other frameworks (e.g. PyTorch, ONNX), and create the TVM IRModule, which contains
+#   all the information needed for compilation, including high-level Relax functions for
+#   computational graph, and low-level TensorIR functions for tensor program.
+# - **Perform Composable Optimizations**: Perform a series of optimization transformations,
+#   such as graph optimizations, tensor program optimizations, and library dispatching.
+# - **Build and Universal Deployment**: Build the optimized model to a deployable module to the
+#   universal runtime, and execute it on different devices, such as CPU, GPU, or other accelerators.
+#
+
+
+######################################################################
+# Construct the model architecture
+# --------------------------------
+# We will use a pre-trained TinyLlama model from Hugging Face. However, usually we only load the
+# pre-trained weight from Hugging Face but not the model architecture. We need to construct the
+# model architecture by ourselves. Apache TVM prepares a PyTorch-liked API to construct the model
+# architecture. We can use the API to construct the model architecture.
+
+import dataclasses
+import enum
+import os
+from pathlib import Path
+from pprint import pprint
+
+from tvm_ffi import Shape
+
+import tvm
+from tvm import relax, te, tirx
+from tvm.relax import register_pipeline
+from tvm.relax.frontend import nn
+from tvm.relax.frontend.nn import Tensor, op
+from tvm.relax.frontend.nn.llm.kv_cache import PagedKVCache, TIRPagedKVCache
+from tvm.s_tir import dlight
+
+######################################################################
+# First, we need to define the model configuration. The configuration includes the key parameters
+# of the model, such as hidden size, intermediate size, etc. Here for convenience, we define a
+# constant config specially for the TinyLlama model.
+
+
+@dataclasses.dataclass
+class LlamaConfig:
+    hidden_size: int = 2048
+    intermediate_size: int = 5632
+    num_attention_heads: int = 32
+    num_hidden_layers: int = 22
+    rms_norm_eps: float = 1e-05
+    vocab_size: int = 32000
+    rope_theta: int = 10000
+    context_window_size: int = 2048
+    prefill_chunk_size: int = 2048
+    num_key_value_heads: int = 4
+    head_dim: int = 64  # hidden_size // num_attention_heads
+
+
+dev = tvm.device("cuda", 0)
+target = tvm.target.Target.from_device(dev)
+
+
+######################################################################
+# Next, we define the RoPE mode of the Paged KV cache. The RoPE mode is used to apply the
+# Relative Positional Encoding (RoPE) to the query and key tensors. The RoPE mode can be set to
+# `NONE`, `NORMAL`, or `INLINE`. If the RoPE mode is `NONE`, the KV cache will not apply RoPE to
+# the query and key tensors. If the RoPE mode is `NORMAL`, RoPE will be applied to the key tensor
+# before adding the key tensor to the cache. If the RoPE mode is `INLINE`, RoPE will be applied to
+# the query and key tensors in the attention kernel on-the-fly.
+
+
+class RopeMode(enum.IntEnum):
+    """The RoPE mode of the Paged KV cache.
+    If it is none, the KV cache will not apply RoPE to q and k.
+    If it is normal, RoPE will be applied to k before adding k to cache.
+    Otherwise, RoPE will be applied to q/k in attention kernel on-the-fly.
+    """
+
+    NONE = 0
+    NORMAL = 1
+    INLINE = 2
+
+
+######################################################################
+# Secondly, we define the model architecture. The model architecture consists of three parts:
+#
+# - Embedding layer: The embedding layer converts the input token IDs to the hidden states.
+# - Decoder layers: The decoder layers are the core of the model. Each decoder layer consists of
+#   a self-attention layer and a feed-forward network (FFN) layer.
+# - Output layer: The output layer converts the hidden states to the logits.
+#
+# First we define the FFN layer. Note that the following FFN layer is optimized implementation
+# where we fuse the gate and up projection into one kernel.
+# The naive implementation of FFN layer is: ``FFN(x) = down_proj(silu(gate(x)) * up(x))``
+# We could combine the ``gate`` and ``up`` projection into one kernel for better performance.
+# The optimized implementation is:
+#
+# .. code-block:: python
+#
+#   concat_x = gate_up(x)
+#   gate_x, up_x = split(concat_x, 2, axis=-1)
+#   FFN(x) = down_proj(silu(gate_x) * up_x)
+#
+
+
+class LlamaFFN(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.gate_up_proj = nn.Linear(
+            in_features=config.hidden_size,
+            out_features=2 * config.intermediate_size,
+            bias=False,
+        )
+        self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
+
+    def forward(self, x: Tensor):
+        concat_x1_x2 = self.gate_up_proj(x)
+        x1, x2 = op.split(concat_x1_x2, 2, axis=-1)
+        return self.down_proj(op.silu(x1) * x2)
+
+
+######################################################################
+# Then we define the self-attention layer. The self-attention layer consists of three parts:
+#
+# - QKV projection: The QKV projection converts the input hidden states to the query, key, and
+#   value tensors.
+# - Attention: The attention layer computes the attention scores and applies the softmax
+#   operation.
+# - Output projection: The output projection converts the attention output to the hidden states.
+#
+# We perform optimizations on the different parts of the self-attention layer:
+#
+# - QKV projection: We leverage the horizontal fusion on QKV projection and fuse them into one
+#   kernel.
+# - Attention: We leverage the horizontal fusion on attention and fuse the QKV projection and
+
+
+class LlamaAttention(nn.Module):  # pylint: disable=too-many-instance-attributes
+    def __init__(self, config: LlamaConfig):
+        self.head_dim = config.head_dim
+        self.num_q_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        # horizontal fusion on QKV projection
+        self.qkv_proj = nn.Linear(
+            in_features=config.hidden_size,
+            out_features=(self.num_q_heads + 2 * self.num_kv_heads) * self.head_dim,
+            bias=False,
+        )
+        self.o_proj = nn.Linear(self.num_q_heads * self.head_dim, config.hidden_size, bias=False)
+
+    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+        d, h_q, h_kv = self.head_dim, self.num_q_heads, self.num_kv_heads
+        b, s, _ = hidden_states.shape
+        # QKV Projection
+        qkv = self.qkv_proj(hidden_states)
+        qkv = op.reshape(qkv, (b, s, h_q + h_kv + h_kv, d))
+        # Attention
+        output = op.reshape(
+            paged_kv_cache.attention_with_fused_qkv(
+                layer_id, qkv, self.num_q_heads, sm_scale=self.head_dim**-0.5
+            ),
+            (b, s, h_q * d),
+        )
+        # Output Projection
+        return self.o_proj(output)
+
+
+######################################################################
+# Finally, we define the model architecture with FFN and self-attention layers.
+
+
+class LlamaDecoderLayer(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        rms_norm_eps = config.rms_norm_eps
+        self.self_attn = LlamaAttention(config)
+        self.mlp = LlamaFFN(config)
+        self.input_layernorm = nn.RMSNorm(config.hidden_size, -1, rms_norm_eps, bias=False)
+        self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, -1, rms_norm_eps, bias=False)
+
+    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+        hidden_states += self.self_attn(
+            self.input_layernorm(hidden_states), paged_kv_cache, layer_id
+        )
+        hidden_states += self.mlp(self.post_attention_layernorm(hidden_states))
+        return hidden_states
+
+
+class LlamaModel(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        assert config.hidden_size % config.num_attention_heads == 0
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList(
+            [LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.norm = nn.RMSNorm(config.hidden_size, -1, config.rms_norm_eps, bias=False)
+
+    def forward(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        hidden_states = input_embed
+        for layer_id, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class LlamaForCausalLM(nn.Module):
+    def __init__(self, config: LlamaConfig):
+        self.model = LlamaModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.num_hidden_layers = config.num_hidden_layers
+        self.num_attention_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.hidden_size = config.hidden_size
+        self.vocab_size = config.vocab_size
+        self.rope_theta = config.rope_theta
+        self.dtype = "float32"
+
+    def to(self, dtype: str | None = None):
+        super().to(dtype=dtype)
+        if dtype is not None:
+            self.dtype = dtype
+
+    def embed(self, input_ids: Tensor):
+        return self.model.embed_tokens(input_ids)
+
+    def get_logits(self, hidden_states: Tensor):
+        logits = self.lm_head(hidden_states)
+        if logits.dtype != "float32":
+            logits = logits.astype("float32")
+        return logits
+
+    def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        def _index(x: te.Tensor):  # x[:-1,:]
+            b, s, d = x.shape
+            return te.compute((b, 1, d), lambda i, _, k: x[i, s - 1, k], name="index")
+
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        hidden_states = op.tensor_expr_op(_index, name_hint="index", args=[hidden_states])
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        logits = self.get_logits(hidden_states)
+        return logits, paged_kv_cache
+
+    def create_tir_paged_kv_cache(
+        self,
+        max_batch_size: tirx.Var,
+        max_total_seq_len: tirx.Var,
+        prefill_chunk_size: tirx.Var,
+        page_size: tirx.Var,
+    ) -> PagedKVCache:
+        return TIRPagedKVCache(
+            attn_kind="mha",
+            max_batch_size=max_batch_size,
+            max_total_seq_len=max_total_seq_len,
+            prefill_chunk_size=prefill_chunk_size,
+            page_size=page_size,
+            support_sliding_window=0,
+            layer_partition=relax.ShapeExpr([0, self.num_hidden_layers]),
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            qk_head_dim=self.head_dim,
+            v_head_dim=self.head_dim,
+            mla_original_qk_head_dim=0,
+            mla_original_v_head_dim=0,
+            rope_mode=RopeMode.NORMAL,
+            rope_scale=1,
+            rope_theta=self.rope_theta,
+            rope_scaling={},
+            rope_ext_factors=tirx.IntImm("int64", 0),
+            rotary_dim=self.head_dim,
+            dtype=self.dtype,
+            target=target,
+            enable_disaggregation=False,
+        )
+
+    def get_default_spec(self):
+        mod_spec = {
+            "embed": {
+                "input_ids": nn.spec.Tensor(["seq_len"], "int32"),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill": {
+                "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "decode": {
+                "input_embed": nn.spec.Tensor([1, 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "create_tir_paged_kv_cache": {
+                "max_batch_size": int,
+                "max_total_seq_len": int,
+                "prefill_chunk_size": int,
+                "page_size": int,
+                "$": {
+                    "param_mode": "none",
+                    "effect_mode": "none",
+                },
+            },
+        }
+        return nn.spec.ModuleSpec.from_raw(mod_spec, self)
+
+
+######################################################################
+# Export the model to Relax IRModule
+# ----------------------------------
+# After defining the model architecture, we can export the model to the Relax IRModule.
+# For demonstration, we only show the part of the model architecture. and parameters.
+
+model_config = LlamaConfig()
+model = LlamaForCausalLM(model_config)
+model.to("float16")
+mod, named_params = model.export_tvm(spec=model.get_default_spec())
+prefill_str = mod["prefill"].script()
+print(*prefill_str.split("\n")[3:20], sep="\n")  # Only show the first 10 lines for demonstration
+print("        ...")
+
+print("\nParameters:")
+pprint(named_params[:5])  # Only show the first 5 parameters for demonstration
+
+######################################################################
+# Define Optimization Pipeline
+# ----------------------------
+# We define a series of optimization passes to optimize the model. The optimization pipeline
+# is designed specifically for the LLMs.
+
+
+@register_pipeline("opt_llm")
+def _pipeline(  # pylint: disable=too-many-arguments
+    ext_mods: list[nn.ExternModule] | None = None,
+):
+    ext_mods = ext_mods or []
+
+    @tvm.transform.module_pass(opt_level=0)
+    def _pipeline(mod: tvm.ir.IRModule, _ctx: tvm.transform.PassContext) -> tvm.ir.IRModule:
+        seq = tvm.transform.Sequential(
+            [
+                # Phase 1. Passes on high-level operator graph
+                # We can enable cublas for further optimization
+                relax.transform.FuseTransposeMatmul(),
+                # Phase 2. Lowering to TIR, inherited TVM Relax's official "zero" pipeline
+                relax.transform.LegalizeOps(),
+                relax.transform.AnnotateTIROpPattern(),
+                relax.transform.FoldConstant(),
+                relax.transform.FuseOps(),
+                relax.transform.FuseTIR(),
+                # Phase 3. Passes on TIR
+                relax.transform.DeadCodeElimination(),
+                # Phase 4. Low-level Optimizations
+                dlight.ApplyDefaultSchedule(
+                    dlight.gpu.Matmul(),
+                    dlight.gpu.GEMV(),
+                    dlight.gpu.Reduction(),
+                    dlight.gpu.GeneralReduction(),
+                    dlight.gpu.Fallback(),
+                ),
+                # Phase 5. Lowering to VM bytecode
+                relax.transform.RewriteDataflowReshape(),
+                relax.transform.ToNonDataflow(),
+                relax.transform.RemovePurityChecking(),
+                relax.transform.CallTIRRewrite(),
+                relax.transform.StaticPlanBlockMemory(),
+                relax.transform.RewriteCUDAGraph(),
+                relax.transform.LowerAllocTensor(),
+                relax.transform.KillAfterLastUse(),
+                relax.transform.LowerRuntimeBuiltin(),
+                relax.transform.VMShapeLower(),
+                relax.transform.AttachGlobalSymbol(),
+                relax.transform.AttachExternModules(ext_mods),
+            ]
+        )
+        mod = seq(mod)
+        return mod
+
+    return _pipeline
+
+
+with target:
+    ex = tvm.compile(mod, target, relax_pipeline=relax.get_pipeline("opt_llm"))
+    vm = relax.VirtualMachine(ex, dev)
+
+
+######################################################################
+# Prepare the model weights
+# -------------------------
+# We load the pre-trained weights from Hugging Face and prepare the model weights.
+# The pre-trained weights are stored in the Hugging Face format. We need to load the weights
+# and prepare the model parameters.
+#
+# .. note::
+#
+#   Note that we won't execute the following code in this tutorial because the pre-trained weights
+#   are not available in the CI environment.
+#
+
+
+IS_IN_CI = os.getenv("CI", "") == "true"
+
+HF_WEIGHT_PATH = None
+# HF_WEIGHT_PATH = Path("/path/to/TinyLlama-1.1B-Chat-v1.0/")
+
+if not IS_IN_CI:
+    import numpy as np
+    import safetensors.torch
+    import torch
+
+    if HF_WEIGHT_PATH is None or not HF_WEIGHT_PATH.exists():
+        raise ValueError("Please set the HF_WEIGHT_PATH to the path of the pre-trained weights.")
+
+    # Torch format weights
+    param_dict = safetensors.torch.load_file(HF_WEIGHT_PATH / "model.safetensors", device="cpu")
+    # Numpy format weights
+    param_dict = {
+        k: v.half().numpy() if v.dtype == torch.bfloat16 else v.numpy()
+        for k, v in param_dict.items()
+    }
+
+    named_params = dict(named_params)
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"model.layers.{i}.self_attn"
+        param_dict[f"{attn}.qkv_proj.weight"] = np.concatenate(
+            [
+                param_dict.pop(f"{attn}.q_proj.weight"),  # Pop the old parameters to save memory
+                param_dict.pop(f"{attn}.k_proj.weight"),
+                param_dict.pop(f"{attn}.v_proj.weight"),
+            ],
+            axis=0,
+        )
+        # Add gates in MLP
+        mlp = f"model.layers.{i}.mlp"
+        param_dict[f"{mlp}.gate_up_proj.weight"] = np.concatenate(
+            [
+                param_dict.pop(f"{mlp}.gate_proj.weight"),
+                param_dict.pop(f"{mlp}.up_proj.weight"),
+            ],
+            axis=0,
+        )
+
+    # Convert params into ndarray
+    params = [
+        tvm.runtime.tensor(param_dict[k].astype("float16"), device=dev) for k in named_params.keys()
+    ]
+
+
+######################################################################
+# Deploy the compiled model
+# -------------------------
+# After the model and weights are ready, we can deploy the compiled model on the target device.
+# The language models inference includes two steps: prefill and decode. The prefill step is
+# used to process the input tokens and store the KVCache. The decode step is used to generate
+# the token until the end token is generated.
+
+
+######################################################################
+# Tokenization
+# ~~~~~~~~~~~~
+# The first step is to tokenize the input prompt and embed the tokens into the hidden states.
+# The tokenization and embedding are the same as the original model. We use the HF tokenizer
+# to tokenize the input prompt and embed the tokens into the hidden states.
+# Note that different models require different tokenization and prompt format, please refer to
+# the model documentation for the correct tokenization and prompt format.
+
+
+if not IS_IN_CI:
+    from transformers import AutoTokenizer
+
+    tokenizer = AutoTokenizer.from_pretrained(HF_WEIGHT_PATH)
+    messages = [
+        {"role": "user", "content": "What's your name?"},
+    ]
+    prompt = tokenizer.apply_chat_template(messages)
+    input_len = len(prompt)
+
+    # Load prompt tokens into TVM ndarray on the target device
+    tokens = tvm.runtime.tensor(np.array(prompt).astype("int32"), device=dev)
+
+######################################################################
+# Create the KVCache
+# ~~~~~~~~~~~~~~~~~~
+# Before starting the inference, we need to create the KVCache. The KVCache is used to store the
+# key and value tensors for the attention layer. Apache TVM provides a PagedKVCache to store the
+# key and value tensors. We create the PagedKVCache with the specified parameters.
+
+if not IS_IN_CI:
+    kv_cache = vm["create_tir_paged_kv_cache"](
+        Shape([1]),  # max_batch_size=1
+        Shape([2048]),  # max_total_seq_len=2048
+        Shape([2048]),  # prefill_chunk_size=2048
+        Shape([16]),  # page_size=16
+    )
+
+
+######################################################################
+# Embedding
+# ~~~~~~~~~
+# The next step is to embed the tokens into the hidden states. We use the `embed` function
+# compiled in the Relax IRModule to embed the tokens into the hidden states.
+
+nd_view_func = tvm.get_global_func("vm.builtin.reshape")
+
+
+def embed(tokens, params):
+    _embed = vm["embed"](tokens, params)
+    # Reshape hidden from [seq_len, hidden_size] to [1, seq_len, hidden_size]
+    _embed = nd_view_func(_embed, Shape([1, _embed.shape[0], _embed.shape[1]]))
+    return _embed
+
+
+######################################################################
+# Prefill
+# ~~~~~~~
+# Before running the forward pass, we first get some help functions for preparation.
+
+add_sequence_func = tvm.get_global_func("vm.builtin.kv_state_add_sequence")
+begin_forward_func = tvm.get_global_func("vm.builtin.kv_state_begin_forward")
+end_forward_func = tvm.get_global_func("vm.builtin.kv_state_end_forward")
+
+######################################################################
+# As we are creating a new sequence, we need to call `add_sequence_func` to initialize
+# the request. Additionally, we need to call `begin_forward_func` to start the forward pass,
+# and `end_forward_func` to end the forward pass.
+
+if not IS_IN_CI:
+    seq_id = 0
+    add_sequence_func(kv_cache, seq_id)
+    hidden_states = embed(tokens, params)
+    begin_forward_func(kv_cache, Shape([seq_id]), Shape([input_len]))
+    logits, kv_cache = vm["prefill"](hidden_states, kv_cache, params)
+    end_forward_func(kv_cache)
+
+######################################################################
+# Now we have the output logits from the prefill step. The logits are used to generate the token
+# via sampling. Let's sample the token from the logits.
+#
+# In this tutorial, we simplify the sampling process and pick the token with the highest
+# probability. In practice, we should sample the token based on the probability distribution.
+# Also, to make the tutorial concise, we execute the sample process on CPU.
+
+
+def sample_token(logits):
+    logits_np = logits.numpy()
+    return np.argmax(logits_np)
+
+
+if not IS_IN_CI:
+    last_token = sample_token(logits)
+    output_tokens = [last_token]
+
+
+######################################################################
+# Decode
+# ~~~~~~
+# After the prefill step, we can start the decode step. The decode step is used to generate the
+# token until the end token is generated. We use the `decode` function compiled in the Relax
+# IRModule to generate the token.
+
+if not IS_IN_CI:
+    print("The generated token:")
+
+    while last_token != tokenizer.eos_token_id:
+        tokens = tvm.runtime.tensor(np.array([last_token]).astype("int32"), device=dev)
+        hidden_states = embed(tokens, params)
+        begin_forward_func(kv_cache, Shape([seq_id]), Shape([1]))
+        logits, kv_cache = vm["decode"](hidden_states, kv_cache, params)
+
+        end_forward_func(kv_cache)
+        last_token = sample_token(logits)
+        output_tokens.append(last_token)
+
+    print(tokenizer.decode(output_tokens))