chore: import upstream snapshot with attribution

python/mlc_llm/model/mixtral/mixtral_loader.py

@@ -0,0 +1,130 @@
+"""
+This file specifies how MLC's Mixtral 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.quantization import Quantization
+
+from .mixtral_model import MixtralConfig, MixtralForCausalLM
+
+
+def huggingface(model_config: MixtralConfig, 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 : MixtralConfig
+        The configuration of the Mixtral model.
+
+    quantization : Quantization
+        The quantization configuration.
+
+    Returns
+    -------
+    param_map : ExternMapping
+        The parameter mapping from MLC to HuggingFace PyTorch.
+    """
+    model = MixtralForCausalLM(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()
+
+    for i in range(model_config.num_hidden_layers):
+        # Add QKV in self attention
+        attn = f"model.layers.{i}.self_attn"
+        mlc_name = f"{attn}.qkv_proj.weight"
+        mlc_param = named_parameters[mlc_name]
+        mapping.add_mapping(
+            mlc_name,
+            [
+                f"{attn}.q_proj.weight",
+                f"{attn}.k_proj.weight",
+                f"{attn}.v_proj.weight",
+            ],
+            functools.partial(
+                lambda q, k, v, dtype: np.concatenate([q, k, v], axis=0).astype(dtype),
+                dtype=mlc_param.dtype,
+            ),
+        )
+
+        # Add gates in MLP (when MoE is enabled)
+        mlp = f"model.layers.{i}.block_sparse_moe"
+        mlc_mlp = f"model.layers.{i}.moe"
+        mlc_name = f"{mlc_mlp}.e1_e3.weight"
+        mlc_param = named_parameters[mlc_name]
+
+        def combine_expert_gate_up(*hf_params, dtype):
+            stack = []
+            for i in range(0, len(hf_params), 2):
+                stack.append(np.concatenate([hf_params[i], hf_params[i + 1]], axis=0))
+            return np.stack(stack, axis=0).astype(dtype)
+
+        mapping.add_mapping(
+            mlc_name,
+            functools.reduce(
+                lambda a, b: a + b,
+                [
+                    [
+                        f"{mlp}.experts.{expert_id}.w1.weight",
+                        f"{mlp}.experts.{expert_id}.w3.weight",
+                    ]
+                    for expert_id in range(model_config.num_local_experts)
+                ],
+            ),
+            functools.partial(
+                combine_expert_gate_up,
+                dtype=mlc_param.dtype,
+            ),
+        )
+
+        mlc_name = f"{mlc_mlp}.e2.weight"
+        mlc_param = named_parameters[mlc_name]
+        mapping.add_mapping(
+            mlc_name,
+            [
+                f"{mlp}.experts.{expert_id}.w2.weight"
+                for expert_id in range(model_config.num_local_experts)
+            ],
+            functools.partial(
+                lambda *hf_params, dtype: np.stack(hf_params, axis=0).astype(dtype),
+                dtype=mlc_param.dtype,
+            ),
+        )
+
+        mlc_name = f"{mlc_mlp}.gate.weight"
+        mlc_param = named_parameters[mlc_name]
+        mapping.add_mapping(
+            mlc_name,
+            [f"{mlp}.gate.weight"],
+            functools.partial(
+                lambda x, dtype: x.astype(dtype),
+                dtype=mlc_param.dtype,
+            ),
+        )
+
+        # 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/mixtral/mixtral_model.py

@@ -0,0 +1,186 @@
+"""Implementation for Mistral architecture."""
+
+import dataclasses
+
+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.llama.llama_model import (
+    LlamaAttention,
+    LlamaConfig,
+    LlamaForCausalLM,
+    LlamaModel,
+)
+from mlc_llm.nn import PagedKVCache
+from mlc_llm.nn.expert import MixtralExperts
+from mlc_llm.support import logging
+from mlc_llm.support import tensor_parallel as tp
+
+logger = logging.getLogger(__name__)
+
+
+@dataclasses.dataclass
+class MixtralConfig(LlamaConfig):
+    """Configuration of the Mixtral model."""
+
+    num_local_experts: int = 0
+    num_experts_per_tok: int = 0
+
+
+class MixtralMoE(nn.Module):
+    """Mixture of experts"""
+
+    def __init__(self, config: MixtralConfig):
+        super().__init__()
+        self.num_experts_per_tok = config.num_experts_per_tok
+        self.num_local_experts = config.num_local_experts
+        if config.intermediate_size % config.tensor_parallel_shards != 0:
+            raise ValueError(
+                f"Cannot split MoE 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 = nn.Linear(
+            in_features=config.hidden_size,
+            out_features=config.num_local_experts,
+            bias=False,
+        )
+        self.e1_e3 = MixtralExperts(
+            self.num_local_experts,
+            in_features=config.hidden_size,
+            out_features=2 * self.intermediate_size,
+            tensor_parallel_shards=config.tensor_parallel_shards,
+        )
+        self.e2 = MixtralExperts(
+            self.num_local_experts,
+            in_features=self.intermediate_size,
+            out_features=config.hidden_size,
+            tensor_parallel_shards=config.tensor_parallel_shards,
+        )
+        self.dtype = "float32"
+
+    def forward(self, x: Tensor):
+        def _expert_forward(x: Tensor, indptr: Tensor):
+            x1_x3 = self.e1_e3(x, indptr)
+            x1, x3 = op.split(x1_x3, indices_or_sections=2, axis=-1)
+            x = self.e2(op.silu(x1) * x3, indptr)
+            return x
+
+        experts_per_tok = self.num_experts_per_tok  # activated experts per token
+        local_experts = self.num_local_experts  # total number of experts
+        batch_size, seq_len, hidden_size = x.shape
+        num_tokens = batch_size * seq_len
+        x = x.reshape(num_tokens, hidden_size)
+        # gate: [num_tokens, local_experts]
+        gate: Tensor = self.gate(x)
+        # expert_weights: [num_tokens, experts_per_tok]
+        # expert_indices: [num_tokens, experts_per_tok]
+        expert_weights, expert_indices = op_ext.moe_misc.gating_softmax_topk(gate, experts_per_tok)
+        use_ft = (
+            op_ext.get_store().cutlass_group_gemm or op_ext.get_store().faster_transformer
+        ) and self.dtype == "float16"
+        if num_tokens == 1:
+            # x: [num_tokens * experts_per_tok, hidden_size]
+            x = _expert_forward(x, expert_indices)
+        else:
+            # cumsum: [num_tokens * local_experts]
+            cumsum = op_ext.moe_misc.moe_cumsum(expert_indices, local_experts)
+            # indices: [num_tokens * experts_per_tok]
+            reverse_indices, token_indices = op_ext.moe_misc.get_indices(cumsum, expert_indices)
+            if use_ft:
+                # indptr: [num_local_experts]
+                indptr = op_ext.moe_misc.get_indptr(
+                    cumsum, local_experts, num_tokens, inclusive=True, out_dtype="int64"
+                )
+            else:
+                # indptr: [num_local_experts + 1]
+                indptr = op_ext.moe_misc.get_indptr(
+                    cumsum,
+                    local_experts,
+                    num_tokens,
+                    inclusive=False,
+                    out_dtype="int32",
+                )
+            # x: [num_tokens * experts_per_tok, hidden_size]
+            x = op.take(x, token_indices, axis=0)
+            x = _expert_forward(x, indptr)
+            x = op_ext.moe_misc.scatter_output(x, reverse_indices)
+        # x: [num_tokens, experts_per_tok, hidden_size]
+        x = x.reshape(num_tokens, experts_per_tok, hidden_size) * expert_weights.reshape(
+            num_tokens, experts_per_tok, 1
+        )
+        # x: [num_tokens, hidden_size]
+        x = op_ext.moe_misc.moe_sum(x, dim=1)
+        x = x.reshape(batch_size, seq_len, hidden_size)
+        return x
+
+
+class MixtralDecoderLayer(nn.Module):
+    """Mixtral decoder layer"""
+
+    def __init__(self, config: MixtralConfig):
+        eps = config.rms_norm_eps
+        self.self_attn = LlamaAttention(config)
+        self.moe = MixtralMoE(config)
+        self.input_layernorm = nn.RMSNorm(config.hidden_size, -1, eps, bias=False)
+        self.post_attention_layernorm = nn.RMSNorm(config.hidden_size, -1, eps, bias=False)
+
+        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_kv_heads * hd
+            v = self.self_attn.num_kv_heads * hd
+            i = self.moe.intermediate_size
+            _set(
+                self.self_attn.qkv_proj,
+                tp.ShardSingleDim("_shard_qkv", segs=[q, k, v], dim=0),
+            )
+            _set(self.self_attn.o_proj, tp.ShardSingleDim("_shard_o", dim=1))
+            _set(self.moe.e1_e3, tp.ShardSingleDim("_shard_mlp_up", segs=[i, i], dim=1))
+            _set(self.moe.e2, tp.ShardSingleDim("_shard_mlp_down", dim=2))
+
+        self.tensor_parallel_shards = config.tensor_parallel_shards
+        _set_tp()
+
+    def forward(self, hidden_states: Tensor, attention_mask: Tensor, total_seq_len: tirx.Var):
+        """Forward pass of a decoder layer; calculate attention, and add an residual connection."""
+        out = self.self_attn(self.input_layernorm(hidden_states), attention_mask, total_seq_len)
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        out = self.moe(self.post_attention_layernorm(hidden_states))
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        return hidden_states
+
+    def batch_forward(self, hidden_states: Tensor, paged_kv_cache: PagedKVCache, layer_id: int):
+        out = self.self_attn(self.input_layernorm(hidden_states), paged_kv_cache, layer_id)
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        out = self.moe(self.post_attention_layernorm(hidden_states))
+        hidden_states = self._apply_residual(out, residual=hidden_states)
+        return hidden_states
+
+    def _apply_residual(self, out, residual):
+        if self.tensor_parallel_shards > 1:
+            return op.ccl_allreduce(out, "sum") + residual
+        return out + residual
+
+
+class MixtralModel(LlamaModel):
+    """Exact same as LlamaModel."""
+
+    def __init__(self, config: MixtralConfig):
+        super().__init__(config)
+        self.layers = nn.ModuleList(
+            [MixtralDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+
+
+class MixtralForCausalLM(LlamaForCausalLM):
+    """Same as LlamaForCausalLM."""
+
+    def __init__(self, config: MixtralConfig):
+        super().__init__(config)
+        self.model = MixtralModel(config)