[Backend] Initial support of CPU backend

cpp/serve/sampler/cpu_sampler.cc

@@ -554,6 +554,10 @@ class CPUSampler : public SamplerObj {
   /*! \brief Copy prob distributions from device to CPU. */
   NDArray CopyProbsToCPU(NDArray probs_on_device) {
     // probs_on_device: (n, v)
+    if (probs_on_device->device.device_type == kDLCPU) {
+      return probs_on_device;
+    }
+
     ICHECK(probs_on_device->device.device_type != kDLCPU);
     if (probs_host_.defined()) {
       ICHECK_EQ(probs_host_->shape[1], probs_on_device->shape[1]);

python/mlc_llm/compiler_pass/attach_logit_processor.py

@@ -27,12 +27,48 @@ def __init__(self, target: tvm.target.Target):
     def transform_module(self, mod: IRModule, _ctx: tvm.transform.PassContext) -> IRModule:
         """Entrypoint"""
         mod = mod.clone()
-        mod["apply_logit_bias_inplace"] = _get_apply_logit_bias_inplace(self.target)
-        mod["apply_penalty_inplace"] = _get_apply_penalty_inplace(self.target)
-        mod["apply_bitmask_inplace"] = _get_apply_bitmask_inplace(self.target)
+        if str(self.target.kind) == "llvm":
+            mod["apply_logit_bias_inplace"] = _get_apply_logit_bias_inplace_cpu()
+            mod["apply_penalty_inplace"] = _get_apply_penalty_inplace_cpu()
+            mod["apply_bitmask_inplace"] = _get_apply_bitmask_inplace_cpu()
+        else:
+            mod["apply_logit_bias_inplace"] = _get_apply_logit_bias_inplace(self.target)
+            mod["apply_penalty_inplace"] = _get_apply_penalty_inplace(self.target)
+            mod["apply_bitmask_inplace"] = _get_apply_bitmask_inplace(self.target)
         return mod
 
 
+def _get_apply_logit_bias_inplace_cpu():
+    @T.prim_func
+    def _apply_logit_bias_inplace(
+        var_logits: T.handle,
+        var_pos2seq_id: T.handle,
+        var_token_ids: T.handle,
+        var_logit_bias: T.handle,
+    ) -> None:
+        """Function that applies logit bias in place."""
+        T.func_attr(
+            {
+                "global_symbol": "apply_logit_bias_inplace",
+                "tir.noalias": True,
+                "tir.is_scheduled": True,
+            }
+        )
+        batch_size = T.int32(is_size_var=True)
+        vocab_size = T.int32(is_size_var=True)
+        num_token = T.int32(is_size_var=True)
+        logits = T.match_buffer(var_logits, (batch_size, vocab_size), "float32")
+        # seq_ids
+        pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32")
+        token_ids = T.match_buffer(var_token_ids, (num_token,), "int32")
+        logit_bias = T.match_buffer(var_logit_bias, (num_token,), "float32")
+
+        for i in range(num_token):
+            logits[pos2seq_id[i], token_ids[i]] += logit_bias[i]
+
+    return _apply_logit_bias_inplace
+
+
 def _get_apply_logit_bias_inplace(target: tvm.target.Target):
     tx = 1024  # default
     max_num_threads_per_block = get_max_num_threads_per_block(target)
@@ -74,6 +110,50 @@ def _apply_logit_bias_inplace(
     return _apply_logit_bias_inplace
 
 
+def _get_apply_penalty_inplace_cpu():
+    @T.prim_func
+    def _apply_penalty_inplace(  # pylint: disable=too-many-arguments,too-many-locals
+        var_logits: T.handle,
+        var_seq_ids: T.handle,
+        var_pos2seq_id: T.handle,
+        var_token_ids: T.handle,
+        var_token_cnt: T.handle,
+        var_penalties: T.handle,
+    ) -> None:
+        """Function that applies penalties in place."""
+        T.func_attr(
+            {
+                "global_symbol": "apply_penalty_inplace",
+                "tir.noalias": True,
+                "tir.is_scheduled": True,
+            }
+        )
+        batch_size = T.int32(is_size_var=True)
+        vocab_size = T.int32(is_size_var=True)
+        num_token = T.int32(is_size_var=True)
+        num_seq = T.int32(is_size_var=True)
+        logits = T.match_buffer(var_logits, (batch_size, vocab_size), "float32")
+        seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32")
+        pos2seq_id = T.match_buffer(var_pos2seq_id, (num_token,), "int32")
+        token_ids = T.match_buffer(var_token_ids, (num_token,), "int32")
+        token_cnt = T.match_buffer(var_token_cnt, (num_token,), "int32")
+        penalties = T.match_buffer(var_penalties, (num_seq, 3), "float32")
+
+        for token in T.serial(num_token):
+            with T.block("block"):
+                vp = T.axis.spatial(num_token, token)
+                logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] -= (
+                    penalties[pos2seq_id[vp], 0] + token_cnt[vp] * penalties[pos2seq_id[vp], 1]
+                )
+                logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] = T.if_then_else(
+                    logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] < 0,
+                    logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] * penalties[pos2seq_id[vp], 2],
+                    logits[seq_ids[pos2seq_id[vp]], token_ids[vp]] / penalties[pos2seq_id[vp], 2],
+                )
+
+    return _apply_penalty_inplace
+
+
 def _get_apply_penalty_inplace(target: tvm.target.Target):
     tx = 1024  # default
     max_num_threads_per_block = get_max_num_threads_per_block(target)
@@ -129,6 +209,42 @@ def _apply_penalty_inplace(  # pylint: disable=too-many-arguments,too-many-local
     return _apply_penalty_inplace
 
 
+def _get_apply_bitmask_inplace_cpu():
+    @T.prim_func
+    def _apply_bitmask_inplace(
+        var_logits: T.handle,
+        var_seq_ids: T.handle,
+        var_bitmask: T.handle,
+    ) -> None:
+        """Function that applies vocabulary masking in place."""
+        T.func_attr(
+            {
+                "global_symbol": "apply_bitmask_inplace",
+                "tir.noalias": True,
+                "tir.is_scheduled": True,
+            }
+        )
+        batch_size = T.int32(is_size_var=True)
+        vocab_size = T.int32(is_size_var=True)
+        num_seq = T.int32(is_size_var=True)
+        logits = T.match_buffer(var_logits, (batch_size, vocab_size), "float32")
+        seq_ids = T.match_buffer(var_seq_ids, (num_seq,), "int32")
+        bitmask = T.match_buffer(var_bitmask, (batch_size, (vocab_size + 31) // 32), "int32")
+
+        for token in T.serial(num_seq * vocab_size):
+            with T.block("block"):
+                vs = T.axis.spatial(num_seq, (token) // vocab_size)
+                vv = T.axis.spatial(vocab_size, (token) % vocab_size)
+
+                logits[seq_ids[vs], vv] = T.if_then_else(
+                    (bitmask[seq_ids[vs], vv // 32] >> (vv % 32)) & 1 == 1,
+                    logits[seq_ids[vs], vv],
+                    T.min_value("float32"),
+                )
+
+    return _apply_bitmask_inplace
+
+
 def _get_apply_bitmask_inplace(target: tvm.target.Target):
     tx = 1024  # default
     max_num_threads_per_block = get_max_num_threads_per_block(target)

python/mlc_llm/compiler_pass/attach_softmax_with_temperature.py

@@ -213,31 +213,37 @@ def softmax_with_chunked_sum(
                     )
 
     sch = tvm.tir.Schedule(IRModule({"softmax_with_chunked_sum": softmax_with_chunked_sum}))
-    max_threads = get_max_num_threads_per_block(target)
-    TX = 32
-    TY = max_threads // TX
-    unroll_depth = 64
-    # pylint: enable=invalid-name
-
-    sch.work_on("softmax_with_chunked_sum")
-    l0, l1, l2 = sch.get_loops("log_pad")
-    bx = sch.fuse(l0, l1)
-    sch.bind(bx, "blockIdx.x")
-    unroll, ty, tx = sch.split(l2, [None, TY, TX])
-    sch.bind(ty, "threadIdx.y")
-    sch.bind(tx, "threadIdx.x")
-    sch.annotate(unroll, ann_key="pragma_auto_unroll_max_step", ann_val=unroll_depth)
-    sch.annotate(unroll, ann_key="pragma_unroll_explicit", ann_val=1)
-
-    for block_name in ["sum_exp", "max"]:
-        block = sch.get_block(block_name)
-        sch.set_scope(block, buffer_index=0, storage_scope="shared")
-        sch.compute_at(block, bx)
-        r_loop = sch.get_loops(block)[-1]
-        r_loop, tx = sch.split(r_loop, [None, TX])
-        sch.reorder(tx, r_loop)
-        sch.bind(tx, "threadIdx.x")
-        sch.annotate(r_loop, ann_key="pragma_auto_unroll_max_step", ann_val=unroll_depth)
-        sch.annotate(r_loop, ann_key="pragma_unroll_explicit", ann_val=1)
 
-    return chunk_lse, sch.mod["softmax_with_chunked_sum"]
+    def apply_gpu_schedule(target, sch):
+        max_threads = get_max_num_threads_per_block(target)
+        TX = 32
+        TY = max_threads // TX
+        unroll_depth = 64
+        # pylint: enable=invalid-name
+
+        sch.work_on("softmax_with_chunked_sum")
+        l0, l1, l2 = sch.get_loops("log_pad")
+        bx = sch.fuse(l0, l1)
+        sch.bind(bx, "blockIdx.x")
+        unroll, ty, tx = sch.split(l2, [None, TY, TX])
+        sch.bind(ty, "threadIdx.y")
+        sch.bind(tx, "threadIdx.x")
+        sch.annotate(unroll, ann_key="pragma_auto_unroll_max_step", ann_val=unroll_depth)
+        sch.annotate(unroll, ann_key="pragma_unroll_explicit", ann_val=1)
+
+        for block_name in ["sum_exp", "max"]:
+            block = sch.get_block(block_name)
+            sch.set_scope(block, buffer_index=0, storage_scope="shared")
+            sch.compute_at(block, bx)
+            r_loop = sch.get_loops(block)[-1]
+            r_loop, tx = sch.split(r_loop, [None, TX])
+            sch.reorder(tx, r_loop)
+            sch.bind(tx, "threadIdx.x")
+            sch.annotate(r_loop, ann_key="pragma_auto_unroll_max_step", ann_val=unroll_depth)
+            sch.annotate(r_loop, ann_key="pragma_unroll_explicit", ann_val=1)
+
+        return chunk_lse, sch.mod["softmax_with_chunked_sum"]
+
+    if target.kind.name == "llvm":
+        return chunk_lse, sch.mod["softmax_with_chunked_sum"]
+    return apply_gpu_schedule(target, sch)

python/mlc_llm/compiler_pass/pipeline.py

@@ -120,7 +120,11 @@ def _pipeline(mod: tvm.ir.IRModule, _ctx: tvm.transform.PassContext) -> tvm.ir.I
                 FuseFTDequantizeEpilogue(),
                 FuseDequantizeTranspose(),
                 BLASDispatch(target) if cublas_gemm else tvm.transform.Sequential([]),
-                FuseAddRMSNorm(target=target),
+                (
+                    FuseAddRMSNorm(target=target)
+                    if target.kind.name != "llvm"
+                    else tvm.transform.Sequential([])
+                ),
                 FuseTransposeMatmul(),
                 _DebugDump("debug-phase1.py", debug_dump, show_meta=False),
                 # Phase 2. Lowering to TIR, inherited TVM Relax's official "zero" pipeline
@@ -152,7 +156,11 @@ def _pipeline(mod: tvm.ir.IRModule, _ctx: tvm.transform.PassContext) -> tvm.ir.I
                 ),
                 _DebugDump("debug-phase4.py", debug_dump, show_meta=False),
                 _LogProgress("Lowering to VM bytecode"),
-                LiftTIRGlobalBufferAlloc(),
+                (
+                    LiftTIRGlobalBufferAlloc()
+                    if target.kind.name != "llvm"
+                    else tvm.transform.Sequential([])
+                ),
                 (
                     tvm.tir.transform.ForceNarrowIndexToInt32()
                     if target.kind.name != "cuda"

python/mlc_llm/support/auto_device.py

@@ -13,7 +13,7 @@
 
 FOUND = green("Found")
 NOT_FOUND = red("Not found")
-AUTO_DETECT_DEVICES = ["cuda", "rocm", "metal", "vulkan", "opencl"]
+AUTO_DETECT_DEVICES = ["cpu", "cuda", "rocm", "metal", "vulkan", "opencl"]
 _RESULT_CACHE: Dict[str, bool] = {}