chore: import upstream snapshot with attribution

python/mlc_llm/model/gpt2/gpt2_loader.py

@@ -0,0 +1,85 @@
+"""
+This file specifies how MLC's GPT-2 parameter maps from other formats, for example HuggingFace
+PyTorch, HuggingFace safetensors.
+"""
+
+import functools
+
+from mlc_llm.loader import ExternMapping
+from mlc_llm.quantization import Quantization
+
+from .gpt2_model import GPT2Config, GPT2LMHeadModel
+
+
+def huggingface(model_config: GPT2Config, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of HuggingFace PyTorch parameters.
+
+    Parameters
+    ----------
+    model_config : GPT2Config
+        The configuration of the GPT-2 model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to HuggingFace PyTorch.
+    """
+    model = GPT2LMHeadModel(model_config)
+    if quantization is not None:
+        model.to(quantization.model_dtype)
+    _, _named_params, _ = model.export_tvm(
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    mapping.add_mapping(
+        "lm_head.weight",
+        ["wte.weight"],
+        functools.partial(
+            lambda x, dtype: x.astype(dtype),
+            dtype=named_parameters["transformer.wte.weight"].dtype,
+        ),
+    )
+
+    for i in range(model_config.n_layer):
+        mapping.add_unused(f"h.{i}.attn.bias")
+
+        # Transpose c_attn, c_proj and c_fc weights since GPT-2 uses Conv1D
+        for conv1d_weight_name in [
+            "attn.c_attn",
+            "attn.c_proj",
+            "mlp.c_proj",
+            "mlp.c_fc",
+        ]:
+            src_name = f"h.{i}.{conv1d_weight_name}.weight"
+            mlc_name = f"transformer.{src_name}"
+            mapping.add_mapping(
+                mlc_name,
+                [src_name],
+                functools.partial(
+                    lambda x, dtype: x.transpose().astype(dtype),
+                    dtype=named_parameters[mlc_name].dtype,
+                ),
+            )
+
+    for mlc_name, mlc_param in named_parameters.items():
+        if mlc_name not in mapping.param_map:
+            # transformer.h.0.attn.c_attn.weight --> h.0.attn.c_attn.weight
+            source_name = mlc_name.split(".", 1)[1]
+            mapping.add_mapping(
+                mlc_name,
+                [source_name],
+                functools.partial(
+                    lambda x, dtype: x.astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+
+    return mapping

python/mlc_llm/model/gpt2/gpt2_model.py

@@ -0,0 +1,382 @@
+"""
+Implementation for GPT-2 architecture.
+"""
+
+import dataclasses
+from typing import Any, Dict, Optional  # noqa: UP035
+
+from tvm import tirx
+from tvm.relax.frontend import nn
+from tvm.relax.frontend.nn import Tensor, op
+
+from mlc_llm import op as op_ext
+from mlc_llm.model.model_utils import index_last_token
+from mlc_llm.nn import PagedKVCache, RopeMode
+from mlc_llm.support import logging
+from mlc_llm.support import tensor_parallel as tp
+from mlc_llm.support.config import ConfigBase
+from mlc_llm.support.style import bold
+
+logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class GPT2Config(ConfigBase):
+    """Configuration of the GPT-2 model."""
+
+    vocab_size: int
+    n_embd: int
+    n_layer: int
+    n_head: int
+    layer_norm_epsilon: float
+    n_inner: int = -1
+    context_window_size: int = 0
+    prefill_chunk_size: int = 0
+    scale_attn_by_inverse_layer_idx: bool = False
+    tensor_parallel_shards: int = 1
+    head_dim: int = 0
+    max_batch_size: int = 1
+    kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)  # noqa: UP006
+
+    def __post_init__(self):
+        if self.n_inner is None or self.n_inner == -1:
+            self.n_inner = 4 * self.n_embd
+        if self.context_window_size == 0:
+            for name in ["n_positions", "max_sequence_length"]:
+                if name in self.kwargs:
+                    self.context_window_size = self.kwargs.pop(name)
+                    logger.info(
+                        "%s not found in config.json. Falling back to %s (%d)",
+                        bold("context_window_size"),
+                        bold(name),
+                        self.context_window_size,
+                    )
+                    break
+            else:
+                raise ValueError(
+                    "Unable to determine the maximum sequence length, because none of "
+                    "`context_window_size`, `n_positions` or `max_sequence_length` is "
+                    "provided in `config.json`."
+                )
+        if self.head_dim == 0:
+            self.head_dim = self.n_embd // self.n_head
+        assert self.head_dim * self.n_head == self.n_embd
+        if self.prefill_chunk_size == 0:
+            logger.info(
+                "%s defaults to %d",
+                bold("prefill_chunk_size"),
+                min(self.context_window_size, 8192),
+            )
+            self.prefill_chunk_size = min(self.context_window_size, 8192)
+        elif self.prefill_chunk_size > self.context_window_size:
+            logger.info(
+                "Overriding %s from %d to %d",
+                bold("prefill_chunk_size"),
+                self.prefill_chunk_size,
+                min(self.context_window_size, 8192),
+            )
+            self.prefill_chunk_size = min(self.context_window_size, 8192)
+
+
+class GPT2Attention(nn.Module):
+    def __init__(self, config: GPT2Config):
+        self.embed_dim = config.n_embd
+        if config.n_head % config.tensor_parallel_shards != 0:
+            raise ValueError(
+                f"Cannot split {config.n_head} attention heads "
+                f"evenly to {config.tensor_parallel_shards} GPUs."
+            )
+        self.num_heads = config.n_head // config.tensor_parallel_shards
+        self.head_dim = config.head_dim
+        self.scale_attn_by_inverse_layer_idx = config.scale_attn_by_inverse_layer_idx
+
+        self.c_attn = nn.Linear(
+            in_features=self.embed_dim,
+            out_features=3 * self.num_heads * self.head_dim,
+            bias=True,
+        )
+        self.c_proj = nn.Linear(self.num_heads * self.head_dim, self.embed_dim, bias=True)
+
+    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+        d, h = self.head_dim, self.num_heads
+        b, s, _ = hidden_states.shape
+
+        qkv = self.c_attn(hidden_states)
+        qkv = op.reshape(qkv, (b, s, 3 * h, d))
+
+        if self.scale_attn_by_inverse_layer_idx:
+            attn_score_scaling_factor = 1.0 / float(layer_id + 1)
+        else:
+            attn_score_scaling_factor = 1.0
+
+        # Attention
+        output = op.reshape(
+            paged_kv_cache.attention_with_fused_qkv(
+                layer_id,
+                qkv,
+                self.num_heads,
+                sm_scale=attn_score_scaling_factor * (self.head_dim**-0.5),
+            ),
+            (b, s, h * d),
+        )
+        return self.c_proj(output)
+
+
+class GPT2MLP(nn.Module):
+    def __init__(self, config: GPT2Config):
+        embed_dim = config.n_embd
+        if config.n_inner % config.tensor_parallel_shards != 0:
+            raise ValueError(
+                f"Cannot split MLP intermediate size {config.n_inner} "
+                f"evenly to {config.tensor_parallel_shards} GPUs."
+            )
+        intermediate_size = config.n_inner // config.tensor_parallel_shards
+        self.c_fc = nn.Linear(embed_dim, intermediate_size)
+        self.c_proj = nn.Linear(intermediate_size, embed_dim)
+
+    def forward(self, hidden_states: Tensor):
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = op.gelu(hidden_states, approximate="tanh")
+        hidden_states = self.c_proj(hidden_states)
+        return hidden_states
+
+
+class GPT2Block(nn.Module):
+    def __init__(self, config: GPT2Config):
+        hidden_size = config.n_embd
+        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.attn = GPT2Attention(config)
+        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        self.mlp = GPT2MLP(config)
+
+        def _set_tp():
+            def _set(param, hint):
+                param.attrs["shard_strategy"] = hint
+
+            hd = config.head_dim
+            q = k = v = self.attn.num_heads * hd
+            _set(
+                self.attn.c_attn.weight,
+                tp.ShardSingleDim("_shard_qkv_weight", dim=0, segs=[q, k, v]),
+            )
+            _set(
+                self.attn.c_attn.bias,
+                tp.ShardSingleDim("_shard_qkv_bias", dim=0, segs=[q, k, v]),
+            )
+            _set(self.attn.c_proj.weight, tp.ShardSingleDim("_shard_attn_c_proj", dim=1))
+            _set(
+                self.mlp.c_fc.weight,
+                tp.ShardSingleDim("_shard_c_fc_weight", dim=0),
+            )
+            _set(self.mlp.c_fc.bias, tp.ShardSingleDim("_shard_c_fc_bias", dim=0))
+            _set(self.mlp.c_proj.weight, tp.ShardSingleDim("_shard_mlp_c_proj", dim=1))
+
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        _set_tp()
+
+    def forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+        with (
+            tp.shard_bias(self.attn.c_proj, self.tensor_parallel_shards),
+            tp.shard_bias(self.mlp.c_proj, self.tensor_parallel_shards),
+        ):
+            hidden_states = self._apply_residual(
+                self.attn(self.ln_1(hidden_states), paged_kv_cache, layer_id),
+                hidden_states,
+            )
+            hidden_states = self._apply_residual(self.mlp(self.ln_2(hidden_states)), hidden_states)
+
+        return hidden_states
+
+    def _apply_residual(self, out, residual):
+        if self.tensor_parallel_shards > 1:
+            return op.ccl_allreduce(out + residual / self.tensor_parallel_shards, "sum")
+        return out + residual
+
+
+class GPT2Model(nn.Module):
+    def __init__(self, config: GPT2Config):
+        assert config.n_embd % config.n_head == 0
+        self.wte = nn.Embedding("vocab_size", config.n_embd)
+        self.wpe = nn.Embedding(config.context_window_size, config.n_embd)
+        self.h = nn.ModuleList([GPT2Block(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+
+    def forward(self, inputs: Tensor, paged_kv_cache: PagedKVCache):
+        # Position Embeddings
+        # Generate np.arange(offset, offset+seq_len)
+        # shape[1] indicates the total query length in the batch
+        input_positions = paged_kv_cache.get_query_positions(inputs.shape[1])
+        pos_embd = self.wpe(input_positions)
+
+        # Pass through GPT2Block
+        hidden_states = inputs + pos_embd
+        for layer_id, layer in enumerate(self.h):
+            hidden_states = layer(hidden_states, paged_kv_cache, layer_id)
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPT2LMHeadModel(nn.Module):
+    def __init__(self, config: GPT2Config):
+        self.transformer = GPT2Model(config)
+        self.lm_head = nn.Linear(config.n_embd, "vocab_size", bias=False)
+        self.n_layer = config.n_layer
+        self.n_embed = config.n_embd
+        self.n_head = config.n_head
+        self.head_dim = config.head_dim
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        self.dtype = "float32"
+
+    def to(self, dtype: Optional[str] = None):
+        super().to(dtype=dtype)
+        if dtype is not None:
+            self.dtype = dtype
+
+    def batch_forward(
+        self,
+        input_embeds: Tensor,
+        paged_kv_cache: PagedKVCache,
+        logit_positions: Optional[Tensor] = None,
+    ):
+        op_ext.configure()
+
+        hidden_states = self.transformer(input_embeds, paged_kv_cache)
+        if logit_positions is not None:
+            hidden_states = op.take(hidden_states, logit_positions, axis=1)
+        logits = self.lm_head(hidden_states)
+        if logits.dtype != "float32":
+            logits = logits.astype("float32")
+        return logits
+
+    def embed(self, input_ids: Tensor):
+        if self.tensor_parallel_shards > 1:
+            input_ids = op.ccl_broadcast_from_worker0(input_ids)
+        return self.transformer.wte(input_ids)
+
+    def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.transformer(input_embed, paged_kv_cache)
+        hidden_states = index_last_token(hidden_states)
+        logits = self.lm_head(hidden_states)
+        if logits.dtype != "float32":
+            logits = logits.astype("float32")
+        return logits, paged_kv_cache
+
+    def decode(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.transformer(input_embed, paged_kv_cache)
+        logits = self.lm_head(hidden_states)
+        if logits.dtype != "float32":
+            logits = logits.astype("float32")
+        return logits, paged_kv_cache
+
+    def batch_prefill(
+        self,
+        input_embeds: Tensor,
+        logit_positions: Tensor,
+        paged_kv_cache: PagedKVCache,
+    ):
+        if self.tensor_parallel_shards > 1:
+            logit_positions = op.ccl_broadcast_from_worker0(logit_positions)
+        logits = self.batch_forward(input_embeds, paged_kv_cache, logit_positions)
+        return logits, paged_kv_cache
+
+    def batch_decode(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def batch_verify(self, input_embeds: Tensor, paged_kv_cache: PagedKVCache):
+        logits = self.batch_forward(input_embeds, paged_kv_cache)
+        return logits, paged_kv_cache
+
+    def create_paged_kv_cache(
+        self,
+        max_batch_size: tirx.Var,
+        max_total_seq_len: tirx.Var,
+        prefill_chunk_size: tirx.Var,
+        page_size: tirx.Var,
+        support_sliding_window: tirx.Var,
+    ) -> PagedKVCache:
+        return PagedKVCache.create_generic(
+            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=support_sliding_window,
+            num_hidden_layers=self.n_layer,
+            num_attention_heads=self.n_head // self.tensor_parallel_shards,
+            num_key_value_heads=self.n_head // self.tensor_parallel_shards,
+            qk_head_dim=self.head_dim,
+            v_head_dim=self.head_dim,
+            rope_mode=RopeMode.NONE,
+            rope_scale=-1,
+            rope_theta=-1,
+            dtype=self.dtype,
+        )
+
+    def get_default_spec(self):
+        mod_spec = {
+            "embed": {
+                "input_ids": nn.spec.Tensor(["seq_len"], "int32"),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill": {
+                "input_embed": nn.spec.Tensor([1, "seq_len", self.n_embed], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "decode": {
+                "input_embed": nn.spec.Tensor([1, 1, self.n_embed], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_prefill": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.n_embed], self.dtype),
+                "logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_decode": {
+                "input_embeds": nn.spec.Tensor(["batch_size", 1, self.n_embed], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_verify": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.n_embed], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "create_paged_kv_cache": {
+                "max_batch_size": int,
+                "max_total_seq_len": int,
+                "prefill_chunk_size": int,
+                "page_size": int,
+                "support_sliding_window": int,
+                "$": {
+                    "param_mode": "none",
+                    "effect_mode": "none",
+                },
+            },
+        }
+        return nn.spec.ModuleSpec.from_raw(mod_spec, self)