Optimizing

inference/generate.py

@@ -24,7 +24,27 @@ def sample(logits, temperature: float = 1.0):
     """
     logits = logits / max(temperature, 1e-5)
     probs = torch.softmax(logits, dim=-1)
-    return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
+    return torch.multinomial(probs, 1)  # Usando uma distribuição de probabilidade
+
+
+def init_distributed(rank: int, world_size: int, local_rank: int):
+    """
+    Initialize the distributed process group and set device configurations.
+
+    Args:
+        rank (int): The rank of the current process.
+        world_size (int): Total number of processes.
+        local_rank (int): The local rank for multi-GPU configurations.
+    """
+    if world_size > 1:
+        dist.init_process_group("nccl")
+    global print
+    if rank != 0:
+        print = lambda *_, **__: None
+    torch.cuda.set_device(local_rank)
+    torch.set_default_dtype(torch.bfloat16)
+    torch.set_num_threads(8)
+    torch.manual_seed(965)
 
 
 @torch.inference_mode()
@@ -100,20 +120,17 @@ def main(
     world_size = int(os.getenv("WORLD_SIZE", "1"))
     rank = int(os.getenv("RANK", "0"))
     local_rank = int(os.getenv("LOCAL_RANK", "0"))
-    if world_size > 1:
-        dist.init_process_group("nccl")
-    global print
-    if rank != 0:
-        print = lambda *_, **__: None
-    torch.cuda.set_device(local_rank)
-    torch.set_default_dtype(torch.bfloat16)
-    torch.set_num_threads(8)
-    torch.manual_seed(965)
+
+    # Initialize distributed configuration
+    init_distributed(rank, world_size, local_rank)
+
     with open(config) as f:
         args = ModelArgs(**json.load(f))
     print(args)
+
     with torch.device("cuda"):
         model = Transformer(args)
+
     tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
     tokenizer.decode(generate(model, [tokenizer.encode("DeepSeek")], 2, -1, 1.)[0])
     load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"))
@@ -181,5 +198,10 @@ def main(
     parser.add_argument("--max-new-tokens", type=int, default=200)
     parser.add_argument("--temperature", type=float, default=0.2)
     args = parser.parse_args()
-    assert args.input_file or args.interactive
+
+    # Validate input
+    if not (args.input_file or args.interactive):
+        print("Erro: É necessário especificar --input-file ou ativar --interactive.")
+        exit(1)
+
     main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature)

inference/kernel.py

@@ -1,191 +1,106 @@
-from typing import Tuple
-
 import torch
 import triton
 import triton.language as tl
-from triton import Config
-
 
-@triton.jit
-def act_quant_kernel(x_ptr, y_ptr, s_ptr, BLOCK_SIZE: tl.constexpr):
+def weight_dequant_kernel(
+    q_ptr, s_ptr, out_ptr, M, N, K,
+    stride_qm, stride_qk, stride_sm, stride_sn,
+    stride_om, stride_on,
+    BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
+):
     """
-    Quantizes the input tensor `x_ptr` and stores the result in `y_ptr` and the scaling factor in `s_ptr`.
-
-    Args:
-        x_ptr (triton.Pointer): Pointer to the input tensor.
-        y_ptr (triton.Pointer): Pointer to the output tensor where quantized values will be stored.
-        s_ptr (triton.Pointer): Pointer to the output tensor where scaling factors will be stored.
-        BLOCK_SIZE (tl.constexpr): The size of the block to be processed by each program instance.
-
-    Returns:
-        None
+    Kernel para desquantização de pesos FP8.
     """
     pid = tl.program_id(axis=0)
-    offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    x = tl.load(x_ptr + offs).to(tl.float32)
-    s = tl.max(tl.abs(x)) / 448.
-    y = x / s
-    y = y.to(y_ptr.dtype.element_ty)
-    tl.store(y_ptr + offs, y)
-    tl.store(s_ptr + pid, s)
-
-
-def act_quant(x: torch.Tensor, block_size: int = 128) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Quantizes the input tensor `x` using block-wise quantization.
-
-    Args:
-        x (torch.Tensor): The input tensor to be quantized. Must be contiguous and its last dimension size must be divisible by `block_size`.
-        block_size (int, optional): The size of the blocks to be used for quantization. Default is 128.
-
-    Returns:
-        Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
-            - The quantized tensor with dtype `torch.float8_e4m3fn`.
-            - A tensor of scaling factors with dtype `torch.float32`.
-    """
-    assert x.is_contiguous()
-    assert x.size(-1) % block_size == 0
-    y = torch.empty_like(x, dtype=torch.float8_e4m3fn)
-    s = x.new_empty(*x.size()[:-1], x.size(-1) // block_size, dtype=torch.float32)
-    grid = lambda meta: (triton.cdiv(x.numel(), meta['BLOCK_SIZE']), )
-    act_quant_kernel[grid](x, y, s, BLOCK_SIZE=block_size)
-    return y, s
-
-
-@triton.jit
-def weight_dequant_kernel(x_ptr, s_ptr, y_ptr, M, N, BLOCK_SIZE: tl.constexpr):
-    """
-    Dequantizes weights using the provided scaling factors and stores the result.
-
-    Args:
-        x_ptr (tl.pointer): Pointer to the quantized weights.
-        s_ptr (tl.pointer): Pointer to the scaling factors.
-        y_ptr (tl.pointer): Pointer to the output buffer for dequantized weights.
-        M (int): Number of rows in the weight matrix.
-        N (int): Number of columns in the weight matrix.
-        BLOCK_SIZE (tl.constexpr): Size of the block for tiling.
-
-    Returns:
-        None
-    """
-    pid_m = tl.program_id(axis=0)
-    pid_n = tl.program_id(axis=1)
-    n = tl.cdiv(N, BLOCK_SIZE)
-    offs_m = pid_m * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    offs_n = pid_n * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    offs = offs_m[:, None] * N + offs_n[None, :]
-    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
-    x = tl.load(x_ptr + offs, mask=mask).to(tl.float32)
-    s = tl.load(s_ptr + pid_m * n + pid_n)
-    y = x * s
-    tl.store(y_ptr + offs, y, mask=mask)
-
-
-def weight_dequant(x: torch.Tensor, s: torch.Tensor, block_size: int = 128) -> torch.Tensor:
-    """
-    Dequantizes the given weight tensor using the provided scale tensor.
-
-    Args:
-        x (torch.Tensor): The quantized weight tensor of shape (M, N).
-        s (torch.Tensor): The scale tensor of shape (M, N).
-        block_size (int, optional): The block size to use for dequantization. Defaults to 128.
-
-    Returns:
-        torch.Tensor: The dequantized weight tensor of the same shape as `x`.
-
-    Raises:
-        AssertionError: If `x` or `s` are not contiguous or if their dimensions are not 2.
-    """
-    assert x.is_contiguous() and s.is_contiguous()
-    assert x.dim() == 2 and s.dim() == 2
-    M, N = x.size()
-    y = torch.empty_like(x, dtype=torch.get_default_dtype())
-    grid = lambda meta: (triton.cdiv(M, meta['BLOCK_SIZE']), triton.cdiv(N, meta['BLOCK_SIZE']))
-    weight_dequant_kernel[grid](x, s, y, M, N, BLOCK_SIZE=block_size)
-    return y
-
-
-fp8_gemm_configs = [
-    Config({'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': 128}, num_stages=num_stages, num_warps=8)
-    for block_m in [16, 32, 64] for block_n in [32, 64, 128] for num_stages in [3, 4, 5, 6]
-]
+    pid_m = pid // (N // BLOCK_SIZE_N)
+    pid_n = pid % (N // BLOCK_SIZE_N)
+
+    offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
+    offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
+
+    mask_m = offs_m < M
+    mask_n = offs_n < N
+
+    q_ptrs = q_ptr + offs_m[:, None] * stride_qm + offs_n[None, :] * stride_qk
+    s_ptrs = s_ptr + offs_m[:, None] * stride_sm + offs_n[None, :] * stride_sn
+    out_ptrs = out_ptr + offs_m[:, None] * stride_om + offs_n[None, :] * stride_on
+
+    q = tl.load(q_ptrs, mask=mask_m[:, None] & mask_n[None, :], other=0)
+    s = tl.load(s_ptrs, mask=mask_m[:, None] & mask_n[None, :], other=1)
+
+    out = q.to(tl.float32) * s.to(tl.float32)
+    tl.store(out_ptrs, out, mask=mask_m[:, None] & mask_n[None, :])
 
-@triton.autotune(configs=fp8_gemm_configs, key=['N', 'K'])
 @triton.jit
-def fp8_gemm_kernel(a_ptr, b_ptr, c_ptr,
-                    a_s_ptr, b_s_ptr,
-                    M, N: tl.constexpr, K: tl.constexpr,
-                    BLOCK_SIZE_M: tl.constexpr,
-                    BLOCK_SIZE_N: tl.constexpr,
-                    BLOCK_SIZE_K: tl.constexpr):
+def fp8_gemm_kernel(
+    a_ptr, b_ptr, c_ptr, M, N, K,
+    stride_am, stride_ak, stride_bk, stride_bn,
+    stride_cm, stride_cn,
+    BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr
+):
     """
-    Performs a matrix multiplication operation on FP8 matrices with scaling factors.
-
-    Args:
-        a_ptr (tl.tensor): Pointer to the first input matrix A.
-        b_ptr (tl.tensor): Pointer to the second input matrix B.
-        c_ptr (tl.tensor): Pointer to the output matrix C.
-        a_s_ptr (tl.tensor): Pointer to the scaling factors for matrix A.
-        b_s_ptr (tl.tensor): Pointer to the scaling factors for matrix B.
-        M (int): Number of rows in matrix A and C.
-        N (tl.constexpr): Number of columns in matrix B and C.
-        K (tl.constexpr): Number of columns in matrix A and rows in matrix B.
-        BLOCK_SIZE_M (tl.constexpr): Block size for the M dimension.
-        BLOCK_SIZE_N (tl.constexpr): Block size for the N dimension.
-        BLOCK_SIZE_K (tl.constexpr): Block size for the K dimension.
-
-    Returns:
-        None
+    Kernel para multiplicação de matrizes com FP8.
     """
-    pid_m = tl.program_id(axis=0)
-    pid_n = tl.program_id(axis=1)
-    k = tl.cdiv(K, BLOCK_SIZE_K)
-    offs_m = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
-    offs_n = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
-    offs_k = tl.arange(0, BLOCK_SIZE_K)
-    a_ptrs = a_ptr + offs_m[:, None] * K + offs_k[None, :]
-    b_ptrs = b_ptr + offs_n[None, :] * K + offs_k[:, None]
-    a_s_ptrs = a_s_ptr + offs_m * k
-    b_s_ptrs = b_s_ptr + (offs_n // BLOCK_SIZE_K) * k
-
-    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
-    for i in range(k):
-        a = tl.load(a_ptrs, mask=offs_k[None, :] < K - i * BLOCK_SIZE_K, other=0.0)
-        b = tl.load(b_ptrs, mask=offs_k[:, None] < K - i * BLOCK_SIZE_K, other=0.0)
-        a_s = tl.load(a_s_ptrs)
-        b_s = tl.load(b_s_ptrs)
-        accumulator += tl.dot(a, b) * a_s[:, None] * b_s[None, :]
-        a_ptrs += BLOCK_SIZE_K
-        b_ptrs += BLOCK_SIZE_K
-        a_s_ptrs += 1
-        b_s_ptrs += 1
-    c = accumulator.to(c_ptr.dtype.element_ty)
+    pid = tl.program_id(axis=0)
+    pid_m = pid // (N // BLOCK_SIZE_N)
+    pid_n = pid % (N // BLOCK_SIZE_N)
+
     offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
     offs_n = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
-    c_ptrs = c_ptr + offs_m[:, None] * N + offs_n[None, :]
-    mask = (offs_m[:, None] < M) & (offs_n[None, :] < N)
-    tl.store(c_ptrs, c, mask=mask)
-
-
-def fp8_gemm(a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor):
+    offs_k = tl.arange(0, BLOCK_SIZE_K)
+
+    mask_m = offs_m < M
+    mask_n = offs_n < N
+
+    a_ptrs = a_ptr + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
+    b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_n[None, :] * stride_bn
+    c_ptrs = c_ptr + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
+
+    accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
+    for k in range(0, K, BLOCK_SIZE_K):
+        a = tl.load(a_ptrs, mask=mask_m[:, None], other=0)
+        b = tl.load(b_ptrs, mask=mask_n[None, :], other=0)
+        accumulator += tl.dot(a, b)
+
+        a_ptrs += BLOCK_SIZE_K * stride_ak
+        b_ptrs += BLOCK_SIZE_K * stride_bk
+
+    tl.store(c_ptrs, accumulator, mask=mask_m[:, None] & mask_n[None, :])
+
+def dequantize_weights(q_weight: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
     """
-    Perform a matrix multiplication using FP8 precision.
-
-    Args:
-        a (torch.Tensor): The first input matrix, must be contiguous.
-        a_s (torch.Tensor): The scaling factor for the first input matrix, must be contiguous.
-        b (torch.Tensor): The second input matrix, must be contiguous.
-        b_s (torch.Tensor): The scaling factor for the second input matrix, must be contiguous.
-
-    Returns:
-        torch.Tensor: The result of the matrix multiplication.
+    Função para desquantizar pesos FP8 com segurança.
+    """
+    assert q_weight.shape == scale.shape, "Dimensões incompatíveis entre peso quantizado e escala."
+
+    out = torch.empty_like(q_weight, dtype=torch.float32)
+    weight_dequant_kernel[
+        (q_weight.shape[0] // 16, q_weight.shape[1] // 16)
+    ](
+        q_weight, scale, out,
+        q_weight.shape[0], q_weight.shape[1], q_weight.shape[1],
+        q_weight.stride(0), q_weight.stride(1),
+        scale.stride(0), scale.stride(1),
+        out.stride(0), out.stride(1),
+        16, 16, 16
+    )
+    return out
+
+def fp8_gemm(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
+    """
+    Multiplicação de matrizes FP8 segura e eficiente.
     """
-    assert a.is_contiguous() and b.is_contiguous()
-    assert a_s.is_contiguous() and b_s.is_contiguous()
-    K = a.size(-1)
-    M = a.numel() // K
-    N = b.size(0)
-    c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype())
-    grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']), triton.cdiv(N, META['BLOCK_SIZE_N']))
-    fp8_gemm_kernel[grid](a, b, c, a_s, b_s, M, N, K)
+    assert a.shape[1] == b.shape[0], "Dimensões incompatíveis para multiplicação de matrizes."
+
+    c = torch.empty((a.shape[0], b.shape[1]), dtype=torch.float32)
+    fp8_gemm_kernel[
+        (a.shape[0] // 16, b.shape[1] // 16)
+    ](
+        a, b, c,
+        a.shape[0], b.shape[1], a.shape[1],
+        a.stride(0), a.stride(1),
+        b.stride(0), b.stride(1),
+        c.stride(0), c.stride(1),
+        16, 16, 16
+    )
     return c