chore: import upstream snapshot with attribution

python/mlc_llm/model/cohere/cohere_loader.py

@@ -0,0 +1,115 @@
+"""
+This file specifies how MLC's Cohere parameter maps from other formats, for example HuggingFace
+PyTorch, HuggingFace safetensors.
+"""
+
+import functools
+
+import numpy as np
+
+from mlc_llm.loader import ExternMapping
+from mlc_llm.loader.standard_loader import make_standard_hf_loader
+from mlc_llm.quantization import Quantization, make_awq_quant
+
+from .cohere_model import CohereConfig, CohereForCausalLM
+
+awq_quant = make_awq_quant(CohereForCausalLM)
+
+
+def _cohere_name_transform(name: str) -> str:
+    if "out_proj." in name:
+        return name.replace("out_proj.", "o_proj.")
+    return name
+
+
+huggingface = make_standard_hf_loader(
+    model_cls=CohereForCausalLM,
+    include_gate_up=False,
+    name_transform=_cohere_name_transform,
+)
+
+
+# https://huggingface.co/alijawad07/aya-23-8B-AWQ-GEMM/tree/main
+def awq(model_config: CohereConfig, quantization: Quantization) -> ExternMapping:
+    """Returns a parameter mapping that maps from the names of MLC LLM parameters to
+    the names of AWQ parameters.
+    Parameters
+    ----------
+    model_config : CohereConfig
+        The configuration of the Cohere model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to AWQ.
+    """
+    model, _ = awq_quant(model_config, quantization)
+    _, _named_params, _ = model.export_tvm(
+        spec=model.get_default_spec(),
+        allow_extern=True,
+    )
+    named_parameters = dict(_named_params)
+
+    mapping = ExternMapping()
+
+    def _add(mlc_name, hf_name):
+        mapping.add_mapping(
+            mlc_name,
+            [hf_name],
+            functools.partial(
+                lambda x, dtype: x.astype(dtype),
+                dtype=named_parameters[mlc_name].dtype,
+            ),
+        )
+
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"model.layers.{i}.self_attn"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            mlc_name = f"{attn}.qkv_proj.{quantize_suffix}"
+            assert mlc_name in named_parameters
+            mlc_param = named_parameters[mlc_name]
+            mapping.add_mapping(
+                mlc_name,
+                [
+                    f"{attn}.q_proj.{quantize_suffix}",
+                    f"{attn}.k_proj.{quantize_suffix}",
+                    f"{attn}.v_proj.{quantize_suffix}",
+                ],
+                functools.partial(
+                    lambda q, k, v, dtype: np.concatenate(
+                        [q, k, v],
+                        axis=1,  # AWQ GEMM would transpose the weight
+                    ).astype(dtype),
+                    dtype=mlc_param.dtype,
+                ),
+            )
+            _add(f"{attn}.out_proj.{quantize_suffix}", f"{attn}.o_proj.{quantize_suffix}")
+
+        # Concat gate and up in MLP
+        mlp = f"model.layers.{i}.mlp"
+        for quantize_suffix in ["qweight", "qzeros", "scales"]:
+            _add(f"{mlp}.up_proj.{quantize_suffix}", f"{mlp}.up_proj.{quantize_suffix}")
+            _add(
+                f"{mlp}.gate_proj.{quantize_suffix}",
+                f"{mlp}.gate_proj.{quantize_suffix}",
+            )
+            _add(
+                f"{mlp}.down_proj.{quantize_suffix}",
+                f"{mlp}.down_proj.{quantize_suffix}",
+            )
+
+        # inv_freq is not used in the model
+        # mapping.add_unused(f"{attn}.rotary_emb.inv_freq")
+
+    for mlc_name, mlc_param in named_parameters.items():
+        if mlc_name not in mapping.param_map:
+            mapping.add_mapping(
+                mlc_name,
+                [mlc_name],
+                functools.partial(lambda x, dtype: x.astype(dtype), dtype=mlc_param.dtype),
+            )
+    return mapping

python/mlc_llm/model/cohere/cohere_model.py

@@ -0,0 +1,406 @@
+"""
+Implementation for Aya23 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 CohereConfig(ConfigBase):
+    """Configuration of the Cohere Aya-23 model"""
+
+    model_type: str  # cohere
+    hidden_size: int
+    vocab_size: int
+    num_hidden_layers: int
+    num_attention_heads: int
+    num_key_value_heads: int
+    intermediate_size: int
+    layer_norm_eps: float
+    position_embedding_base: int = 0
+    context_window_size: int = 0
+    prefill_chunk_size: int = 0
+    head_dim: int = 0
+    tensor_parallel_shards: int = 1
+    max_batch_size: int = 1
+    kwargs: Dict[str, Any] = dataclasses.field(default_factory=dict)  # noqa: UP006
+
+    def __post_init__(self):
+        if self.position_embedding_base == 0:
+            if "rope_theta" in self.kwargs:
+                self.position_embedding_base = self.kwargs["rope_theta"]
+            else:
+                self.position_embedding_base = 10000
+
+            if self.context_window_size == 0:
+                for name in ["max_position_embeddings", "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
+
+            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)
+
+            if self.num_key_value_heads == 0 or self.num_key_value_heads is None:
+                self.num_key_value_heads = self.num_attention_heads
+            if self.head_dim == 0:
+                self.head_dim = self.hidden_size // self.num_attention_heads
+            assert self.head_dim * self.num_attention_heads == self.hidden_size, (
+                "head_dim * num_attention_heads != hidden_size"
+            )
+            assert self.num_attention_heads % self.num_key_value_heads == 0, (
+                "num_attention_heads % num_key_value_heads != 0"
+            )
+
+
+class CohereMLP(nn.Module):
+    def __init__(self, config: CohereConfig):
+        super().__init__()
+        if config.intermediate_size % config.tensor_parallel_shards != 0:
+            raise ValueError(
+                f"Cannot split MLP intermediate size {config.intermediate_size} "
+                f"evenly to {config.tensor_parallel_shards} GPUs."
+            )
+
+        self.intermediate_size = config.intermediate_size // config.tensor_parallel_shards
+        self.gate_proj = nn.Linear(config.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(config.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, config.hidden_size, bias=False)
+
+    def forward(self, x):
+        down_proj = self.down_proj(op.silu(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class CohereAttention(nn.Module):
+    def __init__(self, config: CohereConfig):
+        self.num_q_heads = config.num_attention_heads // config.tensor_parallel_shards
+        assert config.num_attention_heads % config.tensor_parallel_shards == 0, (
+            f"num_attention_heads({config.num_attention_heads}) "
+            "must be divisible by tensor_parallel_shards"
+        )
+        self.num_key_value_heads = config.num_key_value_heads // config.tensor_parallel_shards
+        assert config.num_key_value_heads % config.tensor_parallel_shards == 0, (
+            f"num_attention_heads({config.num_key_value_heads}) "
+            "must be divisible by tensor_parallel_shards"
+        )
+        self.head_dim = config.head_dim
+
+        self.qkv_proj = nn.Linear(
+            in_features=config.hidden_size,
+            out_features=(self.num_q_heads + 2 * self.num_key_value_heads) * self.head_dim,
+            bias=False,
+        )
+        self.out_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_key_value_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),
+        )
+        return self.out_proj(output)
+
+
+class CohereDecoderLayer(nn.Module):
+    def __init__(self, config: CohereConfig):
+        super().__init__()
+        self.self_attn = CohereAttention(config)
+        self.mlp = CohereMLP(config)
+        self.input_layernorm = CohereNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        def _set_tp():
+            def _set(layer, hint):
+                layer.weight.attrs["shard_strategy"] = hint
+
+            hd = config.head_dim
+            q = self.self_attn.num_q_heads * hd
+            k = self.self_attn.num_key_value_heads * hd
+            v = self.self_attn.num_key_value_heads * hd
+            i = self.mlp.intermediate_size
+            _set(
+                self.self_attn.qkv_proj,
+                tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0),
+            )
+            _set(self.self_attn.out_proj, tp.ShardSingleDim("_shard_o", dim=1))
+            _set(
+                self.mlp.gate_proj,
+                tp.ShardSingleDim("_shard_mlp_gate", segs=[i, i], dim=0),
+            )
+            _set(self.mlp.up_proj, tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=0))
+            _set(self.mlp.down_proj, tp.ShardSingleDim("_shard_mlp_down", 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):
+        hidden_ln = self.input_layernorm(hidden_states)
+        attn = self.self_attn(hidden_ln, paged_kv_cache, layer_id)
+        mlp = self.mlp(hidden_ln)
+        hidden_states = self._apply_parallel_residual(attn, residual=hidden_states)
+        hidden_states = self._apply_parallel_residual(mlp, residual=hidden_states)
+        return hidden_states
+
+    def _apply_parallel_residual(self, mlp_out, residual):
+        if self.tensor_parallel_shards > 1:
+            return op.ccl_allreduce(mlp_out + residual / self.tensor_parallel_shards, "sum")
+        return mlp_out + residual
+
+
+class CohereNorm(nn.Module):
+    def __init__(
+        self, normalized_shape: int, eps: float = 1e-5, dtype: Optional[str] = None
+    ) -> None:
+        super().__init__()
+        self.normalized_shape = normalized_shape
+        self.eps = eps
+        self.weight = nn.Parameter((normalized_shape,), dtype=dtype)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return op.layer_norm(
+            x,
+            normalized_shape=self.normalized_shape,
+            weight=self.weight,
+            bias=None,
+            eps=self.eps,
+        )
+
+
+class CohereEmbedding(nn.Embedding):
+    def lm_head_forward(self, x: nn.Tensor):
+        """The lm_head forwarding, which transposes the weight and multiplies
+        with the input tensor.
+        """
+        weight = nn.op.permute_dims(self.weight)
+        return nn.op.matmul(x, weight, out_dtype="float32")
+
+
+class CohereModel(nn.Module):
+    def __init__(self, config: CohereConfig):
+        assert config.hidden_size % config.num_attention_heads == 0
+        self.embed_tokens = CohereEmbedding("vocab_size", config.hidden_size)
+        self.layers = nn.ModuleList(
+            [CohereDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.norm = CohereNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    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 CohereForCausalLM(nn.Module):
+    def __init__(self, config: CohereConfig) -> None:
+        super().__init__()
+        self.model = CohereModel(config)
+        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.position_embedding_base
+        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.model(input_embeds, paged_kv_cache)
+        if logit_positions is not None:
+            hidden_states = op.take(hidden_states, logit_positions, axis=1)
+        lm_logits = self.model.embed_tokens.lm_head_forward(hidden_states)
+        if lm_logits.dtype != "float32":
+            lm_logits = lm_logits.astype("float32")
+        return lm_logits
+
+    def prefill(self, input_embed: Tensor, paged_kv_cache: PagedKVCache):
+        op_ext.configure()
+
+        hidden_states = self.model(input_embed, paged_kv_cache)
+        hidden_states = index_last_token(hidden_states)
+        # logits = self.lm_head(hidden_states)
+        logits = self.model.embed_tokens.lm_head_forward(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.model(input_embed, paged_kv_cache)
+        logits = self.model.embed_tokens.lm_head_forward(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 embed(self, input_ids: Tensor):
+        if self.tensor_parallel_shards > 1:
+            input_ids = op.ccl_broadcast_from_worker0(input_ids)
+        embeds = self.model.embed_tokens(input_ids)
+        return embeds
+
+    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.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads // self.tensor_parallel_shards,
+            num_key_value_heads=self.num_key_value_heads // self.tensor_parallel_shards,
+            qk_head_dim=self.head_dim,
+            v_head_dim=self.head_dim,
+            rope_mode=RopeMode.NORMAL,
+            rope_scale=1,
+            rope_theta=self.rope_theta,
+            dtype=self.dtype,
+        )
+
+    def get_default_spec(self):
+        mod_spec = {
+            "embed": {
+                "input_ids": nn.spec.Tensor(["seq_len"], "int32"),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "prefill": {
+                "input_embed": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "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),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_prefill": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "logit_positions": nn.spec.Tensor(["batch_size"], "int32"),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_decode": {
+                "input_embeds": nn.spec.Tensor(["batch_size", 1, self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "param_mode": "packed",
+                    "effect_mode": "none",
+                },
+            },
+            "batch_verify": {
+                "input_embeds": nn.spec.Tensor([1, "seq_len", self.hidden_size], self.dtype),
+                "paged_kv_cache": nn.spec.Object(object_type=PagedKVCache),
+                "$": {
+                    "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)