[Dev] Refactor the ops script implementation with SE (#78)

* Refactor BatchMatMulEmitter and BatchMatMulSelector for improved readability and maintainability

* Refactor import statements for improved readability and maintainability

* Refactor import statements for improved readability and maintainability

* disable failure email for ci

* remove email notifications.

* move relax pass from testing to mlc_llm

* Refactor scripts with se check_eual_ref_scripts_with_emitter function

* Lint Fix

bitblas/ops/impl/matmul_dequantize_impl.py

@@ -214,6 +214,326 @@ def emit(self):
         )
         return tvm.IRModule.from_expr(func)
 
+# TODO: The following code should be refactored.
+class MatMulNTDequantizeEmitter:
+
+    def __init__(
+        self,
+        M,
+        N,
+        K,
+        in_dtype="float16",
+        out_dtype="float16",
+        accum_dtype="float16",
+        bit=4,
+        storage_dtype="int8",
+        source_format="uint",
+        with_scaling=False,
+        with_zeros=False,
+        group_size=-1,
+        fast_decoding=False,
+        with_bias=False,
+        zeros_mode="original",
+        propagate_a: TransformKind = TransformKind.NonTransform,
+        propagate_b: TransformKind = TransformKind.NonTransform,
+    ):
+        self.M = self._validate_dimension(M, "M")
+        self.N = N
+        self.K = K
+        self.in_dtype = in_dtype
+        self.out_dtype = out_dtype
+        self.accum_dtype = accum_dtype
+        self.bit = bit
+        self.storage_dtype = storage_dtype
+        self.source_format = source_format
+        self.with_scaling = with_scaling
+        self.with_zeros = with_zeros
+        self.group_size = group_size if group_size != -1 else K
+        self.fast_decoding = fast_decoding
+        self.with_bias = with_bias
+        self.zeros_mode = zeros_mode
+        self.propagate_a = self._legalize_transform_kind(propagate_a)
+        self.propagate_b = self._legalize_transform_kind(propagate_b)
+
+        self._validate_bit()
+        self._validate_layout()
+
+    @staticmethod
+    def _validate_dimension(dim, name):
+        if not isinstance(dim, int):
+            return tvm.te.var(name.lower())
+        return dim
+
+    def _validate_bit(self):
+        if self.bit not in [1, 2, 4, 8]:
+            raise ValueError(f"Unsupported bit: {self.bit}")
+
+    def _validate_layout(self):
+        # TODO: extend the dequantize operators into General Layout
+        pass
+
+    def _legalize_group_size(self):
+        if self.group_size == -1:
+            self.group_size = self.K
+
+    def _legalize_transform_kind(self, propagate):
+        if propagate is None:
+            return TransformKind.NonTransform
+        if isinstance(propagate, bool):
+            return (TransformKind.IntraWarpTransform if propagate else TransformKind.NonTransform)
+        elif isinstance(propagate, int):
+            return TransformKind(propagate)
+
+    def _create_placeholders(self):
+        storage_dtype = self.storage_dtype
+        storage_nbit = int("".join(c for c in storage_dtype if c.isdigit()))
+        in_dtype = self.in_dtype
+        bit = self.bit
+        l = r = 16  # noqa: E741
+        if in_dtype in ["int8", "e4m3_float8", "e5m2_float8"]:
+            l, r = 16, 32  # noqa: E741
+
+        A = te.placeholder((self.M, self.K), name="A", dtype=in_dtype)
+        B = te.placeholder((self.N, self.K // storage_nbit * bit), name="B", dtype=storage_dtype)
+        if self.propagate_a:
+            A = te.placeholder((self.M // l, self.K // r, l, r), name="A", dtype=in_dtype)
+        if self.propagate_b:
+            target_dtype = DataType(in_dtype)
+            scaling_factor = 1
+            if bit > 0 and bit < target_dtype.bits:
+                scaling_factor = ((target_dtype.bits // bit) * DataType(storage_dtype).bits //
+                                  target_dtype.bits)
+            qr = r * bit // storage_nbit
+            B = te.placeholder((self.N // l, (self.K // scaling_factor) // qr, l, qr),
+                               name="B",
+                               dtype=storage_dtype)
+
+        LUT = te.placeholder((1 << bit,), name="LUT", dtype=in_dtype)
+        Scale = te.placeholder((self.N, self.K // self.group_size), name="Scale", dtype=in_dtype)
+        Zeros = te.placeholder((self.N, self.K // self.group_size), name="Zeros", dtype=in_dtype)
+        QZeros = te.placeholder(((self.K // self.group_size), self.N // storage_nbit * bit),
+                                name="QZeros",
+                                dtype=self.storage_dtype)
+        Bias = te.placeholder((self.N,), name="Bias", dtype=in_dtype)
+        return A, B, LUT, Scale, Zeros, QZeros, Bias
+
+    def _propagate_input(self, tensor, transform_kind=TransformKind.NonTransform, matrix_name="A"):
+        if transform_kind == TransformKind.NonTransform:
+            return tensor
+        in_dtype = self.in_dtype
+        l = r = 16  # noqa: E741
+        if in_dtype in ["int8", "e4m3_float8", "e5m2_float8"]:
+            l, r = 16, 32  # noqa: E741
+        _, inversed_index_map = get_propagate_map(
+            trans=False, dtype=in_dtype, matrix_name=matrix_name)
+
+        def fcompute(i, j):
+            warp_i, warp_j = i % l, j % r
+            spatial_args = i // l, j // r
+            if transform_kind >= TransformKind.IntraWarpTransform:
+                warp_i, warp_j = inversed_index_map.map_indices([warp_i, warp_j])
+            new_index = (*spatial_args, warp_i, warp_j)
+            return tensor[new_index]
+
+        return te.compute(
+            (self.M, self.K),
+            fcompute,
+            name=f"{matrix_name}_reindex",
+        )
+
+    def _propagage_weight(self, tensor, transform_kind=TransformKind.NonTransform, matrix_name="B"):
+        if transform_kind == TransformKind.NonTransform:
+            return tensor
+        in_dtype = self.in_dtype
+        bit = self.bit
+        storage_dtype = self.storage_dtype
+        storage_nbit = int("".join(c for c in self.storage_dtype if c.isdigit()))
+
+        l = r = 16  # noqa: E741
+        if in_dtype in ["int8", "e4m3_float8", "e5m2_float8"]:
+            l, r = 16, 32  # noqa: E741
+        _, inversed_index_map = get_propagate_map(
+            trans=True, dtype=in_dtype, matrix_name=matrix_name)
+        target_dtype = DataType(in_dtype)
+        scaling_factor = 1
+        if bit > 0 and bit < target_dtype.bits:
+            scaling_factor = ((target_dtype.bits // bit) * DataType(storage_dtype).bits //
+                              target_dtype.bits)
+            initial_indices = inversed_index_map.initial_indices
+            scaling_final_indices = inversed_index_map.map_indices(
+                initial_indices[:-1] + [initial_indices[-1] * scaling_factor])
+            scaling_final_indices = scaling_final_indices[:-1] + [
+                scaling_final_indices[-1] // scaling_factor
+            ]
+            inversed_index_map = IndexMap(
+                initial_indices,
+                scaling_final_indices,
+                None,
+            )
+
+        qr = r * bit // storage_nbit
+
+        def fcompute(i, j):
+            warp_i, warp_j = i % l, j % qr
+            spatial_args = i // l, j // qr
+            if transform_kind >= TransformKind.IntraWarpTransform:
+                warp_i, warp_j = inversed_index_map.map_indices([warp_i, warp_j])
+            new_index = (*spatial_args, warp_i, warp_j)
+            return tensor[new_index]
+
+        return te.compute(
+            (self.N, self.K // storage_nbit * bit),
+            fcompute,
+            name=f"{matrix_name}_reindex",
+        )
+
+    def _decode_func(self, B, LUT, Scale, Zeros, QZeros):
+        bit = self.bit
+        in_dtype = self.in_dtype
+        storage_dtype = self.storage_dtype
+        storage_nbit = int("".join(c for c in storage_dtype if c.isdigit()))
+        storage_type = str("".join(c for c in storage_dtype if not c.isdigit()))
+        n_float_per_elem = storage_nbit // bit
+
+        # TODO: Move the decode function into a more general place
+        def decode(n, k):
+            w = None
+            if self.with_zeros and self.zeros_mode == "quantized":
+                qzeros_dequantize = _tir_packed_to_unsigned_convert(storage_type, storage_nbit)(
+                    bit,
+                    QZeros[k, n // n_float_per_elem],
+                    n % n_float_per_elem,
+                    dtype=self.storage_dtype,
+                )
+                w = _tir_packed_to_unsigned_convert_with_zeros(storage_type, storage_nbit)(
+                    bit,
+                    B[n, k // n_float_per_elem],
+                    k % n_float_per_elem,
+                    qzeros_dequantize,
+                    dtype=in_dtype,
+                )
+            elif self.source_format == "uint":
+                if bit == 8:
+                    w = B[n, k].astype(in_dtype)
+                w = _tir_packed_to_unsigned_convert(storage_type, storage_nbit)(
+                    bit, B[n, k // n_float_per_elem], k % n_float_per_elem, dtype=in_dtype)
+            elif self.source_format == "int":
+                if bit == 1:
+                    w = _tir_packed_int_to_int_convert(storage_type, storage_nbit)(
+                        bit, B[n, k // n_float_per_elem], k % n_float_per_elem, dtype=in_dtype)
+                if bit == 8:
+                    w = B[n, k].astype(in_dtype)
+                w = _tir_packed_to_signed_convert(storage_type, storage_nbit)(
+                    bit, B[n, k // n_float_per_elem], k % n_float_per_elem, dtype=in_dtype)
+            elif self.source_format == "fp":
+                w = _tir_u32_to_f4_to_f16(
+                    bit, B[n, k // n_float_per_elem], k % n_float_per_elem, dtype=in_dtype)
+            elif self.source_format == "fp_e4m3":
+                w = _tir_u8_to_f8_e4m3_to_f16(bit, B[n, k], dtype=in_dtype)
+            elif self.source_format == "nf":
+                index = _tir_packed_to_unsigned_convert(storage_type, storage_nbit)(
+                    bit,
+                    B[n, k // n_float_per_elem],
+                    k % n_float_per_elem,
+                    dtype="int32",
+                )
+                w = LUT[index]
+            else:
+                raise ValueError(f"Unsupported source_format: {self.source_format}")
+
+            assert w is not None, "w is None"
+
+            group_size = self.group_size
+            zeros_mode = self.zeros_mode
+
+            if not self.with_scaling:
+                return w
+
+            if not self.with_zeros:
+                return w * Scale[n, k // group_size]
+
+            if zeros_mode == "original":
+                w = (w - Zeros[n, k // group_size]) * Scale[n, k // group_size]
+            elif zeros_mode == "rescale":
+                w = w * Scale[n, k // group_size] - Zeros[n, k // group_size]
+            elif zeros_mode == "quantized":
+                w = w * Scale[n, k // group_size]
+            else:
+                raise ValueError("Unsupported zeros_mode: {}".format(zeros_mode))
+
+            return w
+
+        return te.compute((self.N, self.K), decode, name="B_decode")
+
+    def _compute_matmul(self, A, B_decode):
+        k = te.reduce_axis((0, self.K), name="k")
+        C = te.compute(
+            (self.M, self.N),
+            lambda i, j: te.sum(
+                A[i, k].astype(self.accum_dtype) * B_decode[j, k].astype(self.accum_dtype), axis=k),
+            name="C",
+        )
+        return C
+
+    def _convert_dtype(self, tensor):
+        if self.accum_dtype != self.out_dtype:
+            return te.compute((self.M, self.N),
+                              lambda i, j: tensor[i, j].astype(self.out_dtype),
+                              name="D")
+        return tensor
+
+    def _apply_bias(self, tensor, Bias):
+        if self.with_bias:
+            return te.compute((self.M, self.N), lambda i, j: tensor[i, j] + Bias[j], name="E")
+        return tensor
+
+    def emit(self):
+        A, B, LUT, Scale, Zeros, QZeros, Bias = self._create_placeholders()
+        A_reindex = self._propagate_input(A, self.propagate_a, "A")
+        B_reindex = self._propagage_weight(B, self.propagate_b, "B")
+
+        B_decode = self._decode_func(B_reindex, LUT, Scale, Zeros, QZeros)
+        C = self._compute_matmul(A_reindex, B_decode)
+        D = self._convert_dtype(C)
+        last_output = self._apply_bias(D, Bias)
+
+        args = [A, B]
+        if self.source_format == "nf":
+            args.append(LUT)
+        if self.with_scaling:
+            args.append(Scale)
+        if self.with_zeros:
+            args.append(QZeros if self.zeros_mode == "quantized" else Zeros)
+        if self.with_bias:
+            args.append(Bias)
+        args.append(last_output)
+
+        func = te.create_prim_func(args).with_attr(
+            "dequantize_info",
+            {
+                "B_decode": {
+                    "decode_block": "B_decode",
+                    "fast_decoding": self.fast_decoding,
+                    "source_format": {
+                        "bits": self.bit,
+                        "format": self.source_format,
+                    },
+                    "storage_dtype": self.storage_dtype,
+                    "target_format": self.in_dtype,
+                    "with_zeros": self.with_zeros,
+                    "zeros_mode": self.zeros_mode,
+                    "with_scaling": self.with_scaling,
+                    "group_size": self.group_size,
+                }
+            },
+        )
+        if self.propagate_a:
+            func = func.with_attr("input_transform_kind", self.propagate_a.value)
+        if self.propagate_b:
+            func = func.with_attr("weight_transform_kind", self.propagate_b.value)
+        return tvm.IRModule.from_expr(func)
+
+
 def matmul_nt_dequantize_b(
     M,
     N,
@@ -335,9 +655,12 @@ def decode_func(n, k):
             A[i, k].astype(accum_dtype) * B_decode[j, k].astype(accum_dtype), axis=k),
         name="C",
     )
-    D = te.compute((M, N), lambda i, j: C[i, j].astype(out_dtype), name="D")
+
+    last_output = C
+    if accum_dtype != out_dtype:
+        D = te.compute((M, N), lambda i, j: C[i, j].astype(out_dtype), name="D")
+        last_output = D
     args = [A, B]
-    last_output = D
     if source_format == "nf":
         args.append(LUT)
     if with_scaling:
@@ -517,9 +840,11 @@ def decode_func(n, k):
             A[i, k].astype(accum_dtype) * B_decode[j, k].astype(accum_dtype), axis=k),
         name="C",
     )
-    D = te.compute((M, N), lambda i, j: C[i, j].astype(out_dtype), name="D")
+    last_output = C
+    if accum_dtype != out_dtype:
+        D = te.compute((M, N), lambda i, j: C[i, j].astype(out_dtype), name="D")
+        last_output = D
     args = [A, B]
-    last_output = D
     if source_format == "nf":
         args.append(LUT)
     if with_scaling:
@@ -715,9 +1040,11 @@ def decode_func(n, k):
         ),
         name="C",
     )
-    D = te.compute((M, N), lambda i, j: C[i, j].astype(out_dtype), name="D")
+    last_output = C
+    if accum_dtype != out_dtype:
+        D = te.compute((M, N), lambda i, j: C[i, j].astype(out_dtype), name="D")
+        last_output = D
     args = [A, B]
-    last_output = D
     if source_format == "nf":
         args.append(LUT)
     if with_scaling: